Reducing latency of flash-based devices using programming interrupts

Abstract

A read request with a high priority indication is received. It is determined whether an ongoing flash programming operation will delay processing of the read request by a threshold amount of time. In response to determining that the in-progress flash memory program operation delays processing the read request by the threshold amount of time, the in-progress flash memory program operation is interrupted.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0001] Figure 1A A first example system for data storage according to some embodiments is described.

[0002] Figure 1B A second example system for data storage is described in accordance with some embodiments.

[0003] Figure 1C A third example system for data storage is described in accordance with some embodiments.

[0004] Figure 1D A fourth example system for data storage is described in accordance with some embodiments.

[0005] Figure 2A is a perspective diagram of a storage cluster having multiple storage nodes and internal storage devices coupled to each storage node to provide network attached storage according to some embodiments.

[0006] Figure 2B is a block diagram illustrating an interconnect switch coupling a plurality of storage nodes according to some embodiments.

[0007] Figure 2C is a multi-level block diagram showing the contents of a storage node and the contents of one of the non-volatile solid-state storage units according to some embodiments.

[0008] Figure 2D A storage server environment is shown according to some embodiments using the storage nodes and storage units of some of the previous figures.

[0009] Figure 2E is a hardware block diagram showing a control plane, compute and storage planes, and authorized blades interacting with underlying physical resources according to some embodiments.

[0010] Figure 2F Depicted is a resiliency software layer in a blade of a storage cluster according to some embodiments.

[0011] Figure 2G Depicted are authorization and storage resources in blades of a storage cluster according to some embodiments.

[0012] Figure 3A A diagram of a storage system coupled for data communication with a cloud service provider is presented in accordance with some embodiments of the present disclosure.

[0013] Figure 3B A diagram illustrating a storage system according to some embodiments of the present disclosure is presented.

[0014] Figure 3CAn example of a cloud-based storage system according to some embodiments of the present disclosure is presented.

[0015] Figure 3D An exemplary computing device is illustrated that may be specifically configured to perform one or more of the processes described herein.

[0016] Figure 3E An example of a team of storage systems for providing storage services according to an embodiment of the present disclosure is described.

[0017] Figure 3F An example container system according to an embodiment of the present disclosure is described.

[0018] Figure 4 is a block diagram illustration of an example storage system according to some embodiments of the present disclosure;

[0019] Figure 5A is an illustration of an example in which a scheduler of a storage system receives a request to read data from a storage device of the storage system according to an embodiment of the present disclosure.

[0020] Figure 5B The following is an example of a scheduler receiving queue information of a storage system according to an embodiment of the present disclosure.

[0021] Fig. 6A is an illustration of an example in which a scheduler of a storage system inserts a read operation into a queue of a storage device according to some embodiments of the present disclosure.

[0022] Figure 6B This is an illustration of an example in which a scheduler of a storage system according to an embodiment of the present disclosure inserts a read operation into a queue of a storage device.

[0023] Figure 7 is an illustration of a timeline of a storage device of a storage system performing a read operation during a multi-phase write operation according to an embodiment of the present disclosure.

[0024] Figure 8 is an illustration of a timeline in which a storage device of a storage system according to an embodiment of the present disclosure suspends a write operation to perform a read operation.

[0025] Fig. 9 is an example method for intelligently processing a read request according to an embodiment of the present disclosure.

[0026] Fig.10 is an illustration of an example of reducing latency of a storage system using a die-aware scheduler according to an embodiment of the present disclosure.

[0027] Fig.11 The present invention is an example method for improving storage system latency using different programming modes according to an embodiment of the present disclosure.

[0028] Fig.12 An example method for optimizing data storage using different flash memory programming modes according to an embodiment of the present disclosure is provided.

[0029] Fig.13 An example method of intelligently directing flash operations of a modular storage system according to an embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] by Figure 1A Initially, example methods, devices, and products for intelligently scheduling operations of a storage system according to embodiments of the present disclosure are described with reference to the accompanying drawings. Figure 1A An example system for data storage according to some embodiments is described. For purposes of illustration and not limitation, system 100 (also referred to herein as a "storage system") includes numerous elements. It may be noted that in other embodiments, system 100 may include the same, more, or fewer elements configured in the same or different manners.

[0031] The system 100 includes a plurality of computing devices 164A-B. The computing devices (also referred to herein as "client devices") may be embodied as, for example, servers in a data center, workstations, personal computers, notebooks, or the like. The computing devices 164A-B may be coupled for data communication with one or more storage arrays 102A-B via a storage area network ('SAN') 158 or a local area network ('LAN') 160.

[0032] SAN 158 may be implemented with a variety of data communication architectures, devices, and protocols. For example, the architecture for SAN 158 may include Fibre Channel, Ethernet, InfiniBand, Serial Attached Small Computer System Interface ('SAS'), or the like. Data communication protocols used with SAN 158 may include Advanced Technology Attachment ('ATA'), Fibre Channel Protocol, Small Computer System Interface ('SCSI'), Internet Small Computer System Interface ('iSCSI'), Ultra SCSI, Non-Volatile Memory Express Architecture ('NVMe'), or the like. It may be noted that SAN 158 is provided for illustration and not limitation. Other data communication couplings may be implemented between computer devices 164A-B and storage arrays 102A-B.

[0033] LAN 160 may also be implemented with a variety of architectures, devices, and protocols. For example, the architecture used for LAN 160 may include Ethernet (802.3), wireless (802.11), or the like. Data communication protocols used in LAN 160 may include Transmission Control Protocol ('TCP'), User Datagram Protocol ('UDP'), Internet Protocol ('IP'), Hypertext Transfer Protocol ('HTTP'), Wireless Access Protocol ('WAP'), Handheld Device Transport Protocol ('HDTP'), Session Initiation Protocol ('SIP'), Real Time Protocol ('RTP'), or the like.

[0034] The storage arrays 102A-B may provide persistent data storage for the computing devices 164A-B. In some embodiments, the storage array 102A may be housed in a chassis (not shown) and the storage array 102B may be housed in another chassis (not shown). The storage arrays 102A and 102B may include one or more storage array controllers 110A-D (also referred to herein as "controllers"). The storage array controllers 110A-D may be embodied as modules of automated computing machinery including computer hardware, computer software, or a combination of computer hardware and software. In some embodiments, the storage array controllers 110A-D may be configured to perform various storage tasks. Storage tasks may include writing data received from computing devices 164A-B to storage arrays 102A-B, erasing data from storage arrays 102A-B, retrieving data from storage arrays 102A-B and providing data to computing devices 164A-B, monitoring and reporting storage device utilization and performance, performing redundancy operations (such as redundant array of independent drives ('RAID') or RAID-like data redundancy operations), compressing data, encrypting data, and the like.

[0035] The storage array controllers 110A-D may be implemented in various ways, including as a field programmable gate array ('FPGA'), a programmable logic chip ('PLC'), an application specific integrated circuit ('ASIC'), a system on a chip ('SOC'), or any computing device including discrete components such as a processing device, a central processing unit, a computer memory, or various adapters. The storage array controllers 110A-D may include, for example, a data communications adapter configured to support communications via the SAN 158 or the LAN 160. In some implementations, the storage array controllers 110A-D may be independently coupled to the LAN 160. In some implementations, the storage array controllers 110A-D may include an I / O controller or the like that couples the storage array controllers 110A-D for data communications to persistent storage resources 170A-B (also referred to herein as "storage resources") through a mid-plane (not shown). The persistent storage resources 170A-B may include any number of storage drives 171A-F (also referred to herein as "storage devices") and any number of non-volatile random access memory ('NVRAM') devices (not shown).

[0036] In some embodiments, the NVRAM device of the persistent storage resource 170A to B may be configured to receive data to be stored in the storage drive 171A to F from the storage array controller 110A to D. In some examples, the data may originate from the computing device 164A to B. In some examples, writing data to the NVRAM device may be faster than writing data directly to the storage drive 171A to F. In some embodiments, the storage array controller 110A to D may be configured to utilize the NVRAM device as a fast accessible buffer for data intended to be written to the storage drive 171A to F. Relative to a system in which the storage array controller 110A to D writes data directly to the storage drive 171A to F, the latency of a write request using the NVRAM device as a buffer may be improved. In some embodiments, the NVRAM device may be implemented with a computer memory in the form of a high-bandwidth, low-latency RAM. The NVRAM device is called "non-volatile" because the NVRAM device may receive or include a unique power source that maintains the state of the RAM after the NVRAM device loses the main power supply. This power source may be a battery, one or more capacitors, or the like. In response to a loss of power, the NVRAM device may be configured to write the contents of the RAM to a persistent storage device, such as storage drives 171A-F.

[0037] In some implementations, storage drives 171A-F may refer to any device configured to persistently record data, where "persistently" or "persistent" refers to the ability of the device to maintain the recorded data after power is lost. In some implementations, storage drives 171A-F may correspond to non-disk storage media. For example, storage drives 171A-F may be one or more solid-state drives ('SSD'), flash memory-based storage devices, any type of solid-state non-volatile memory, or any other type of non-mechanical storage device. In other implementations, storage drives 171A-F may include a mechanical or rotating hard disk, such as a hard disk drive ('HDD').

[0038] In some embodiments, the storage array controllers 110A-D may be configured to offload device management responsibilities from the storage drives 171A-F in the storage arrays 102A-B. For example, the storage array controllers 110A-D may manage control information that may describe the status of one or more memory blocks in the storage drives 171A-F. The control information may indicate, for example, that a particular memory block has failed and should no longer be written to, that a particular memory block contains boot code for the storage array controllers 110A-D, the number of program-erase ('P / E') cycles that have been performed on a particular memory block, the age of the data stored in a particular memory block, the type of data stored in a particular memory block, and the like. In some embodiments, the control information may be stored as metadata with the associated memory blocks. In other embodiments, the control information of the storage drives 171A-F may be stored in one or more specific memory blocks of the storage drives 171A-F selected by the storage array controllers 110A-D. The selected memory blocks may be tagged with an identifier indicating that the selected memory blocks contain the control information. The identifier may be used by the storage array controllers 110A-D in conjunction with the storage drives 171A-F to quickly identify a memory block containing the control information. For example, the storage controllers 110A-D may issue a command to locate a memory block containing the control information. It may be noted that the control information may be so large that portions of the control information may be stored in multiple locations, that the control information may be stored in multiple locations, for example, for redundancy purposes, or that the control information may be otherwise distributed across multiple memory blocks in the storage drives 171A-F.

[0039] In some implementations, the storage array controllers 110A-D may offload device management responsibilities from the storage drives 171A-F of the storage arrays 102A-B by retrieving control information from the storage drives 171A-F that describes the state of one or more memory blocks in the storage drives 171A-F. Retrieving the control information from the storage drives 171A-F may be performed, for example, by the storage array controllers 110A-D querying the storage drives 171A-F for the location of the control information for a particular storage drive 171A-F. The storage drives 171A-F may be configured to execute instructions that enable the storage drives 171A-F to identify the location of the control information. The instructions may be executed by a controller (not shown) associated with or otherwise located on the storage drives 171A-F and may cause the storage drives 171A-F to scan a portion of each memory block to identify a memory block that stores the control information for the storage drives 171A-F. The storage drives 171A-F may respond by sending a response message containing the location of the control information for the storage drives 171A-F to the storage array controllers 110A-D. In response to receiving the response message, the storage array controllers 110A-D may issue a request to read data stored at the address associated with the location of the control information for the storage drives 171A-F.

[0040] In other embodiments, the storage array controllers 110A-D may further offload device management responsibilities from the storage drives 171A-F by performing storage drive management operations in response to receiving the control information. The storage drive management operations may include, for example, operations that are typically performed by the storage drives 171A-F, e.g., a controller (not shown) associated with the particular storage drives 171A-F. The storage drive management operations may include, for example, ensuring that data is not written to failed memory blocks within the storage drives 171A-F, ensuring that data is written to memory blocks within the storage drives 171A-F in a manner such that adequate wear leveling is achieved, and the like.

[0041] In some embodiments, the storage arrays 102A-B may implement two or more storage array controllers 110A-D. For example, the storage array 102A may include a storage array controller 110A and a storage array controller 110B. At a given moment, a single storage array controller 110A-D (e.g., storage array controller 110A) of the storage system 100 may be designated as having a primary status (also referred to herein as a "primary controller"), and other storage array controllers 110A-D (e.g., storage array controller 110A) may be designated as having a secondary status (also referred to herein as a "secondary controller"). The primary controller may have specific rights, such as permission to change data in the persistent storage resources 170A-B (e.g., write data to the persistent storage resources 170A-B). At least some of the rights of the primary controller may replace the rights of the secondary controller. For example, when the primary controller has permission to change data in the persistent storage resources 170A-B, the secondary controller may not have the rights. The status of the storage array controllers 110A-D may change. For example, storage array controller 110A may be designated as having a secondary status and storage array controller 110B may be designated as having a primary status.

[0042] In some embodiments, a primary controller (e.g., storage array controller 110A) may be used as a primary controller for one or more storage arrays 102A to B, and a secondary controller (e.g., storage array controller 110B) may be used as a secondary controller for one or more storage arrays 102A to B. For example, storage array controller 110A may be the primary controller for storage array 102A and storage array 102B, and storage array controller 110B may be the secondary controller for storage arrays 102A and 102B. In some embodiments, storage array controllers 110C and 110D (also referred to as "storage processing modules") may not have primary or secondary status. Storage array controllers 110C and 110D implemented as storage processing modules may be used as a communication interface between primary and secondary controllers (e.g., storage array controllers 110A and 110B, respectively) and storage array 102B. For example, storage array controller 110A of storage array 102A may send a write request to storage array 102B via SAN 158. The write request may be received by both storage array controllers 110C and 110D of the storage array 102B. The storage array controllers 110C and 110D facilitate communication, for example, sending the write request to the appropriate storage drives 171A through F. It may be noted that in some embodiments, the storage processing module may be used to increase the number of storage drives controlled by the primary and secondary controllers.

[0043] In some implementations, the storage array controllers 110A-D are communicatively coupled to one or more storage drives 171A-F and to one or more NVRAM devices (not shown) included as part of the storage arrays 102A-B via a mid-plane (not shown). The storage array controllers 110A-D may be coupled to the mid-plane via one or more data communications links, and the mid-plane may be coupled to the storage drives 171A-F and the NVRAM devices via one or more data communications links. For example, the data communications links described herein are collectively illustrated by data communications links 108A-D and may include a Peripheral Component Interconnect Express ('PCIe') bus.

[0044] Figure 1B An example system for data storage is described according to some embodiments. Figure 1B The storage array controller 101 described in Figure 1A 1. Storage array controller 101 may be similar to storage array controller 110A or storage array controller 110B. For purposes of illustration and not limitation, storage array controller 101 includes numerous elements. It is noted that in other embodiments, storage array controller 101 may include the same, more, or fewer elements configured in the same or different manners. It is noted that the following may include Figure 1A 1 to help illustrate the features of storage array controller 101.

[0045] The storage array controller 101 may include one or more processing devices 104 and a random access memory ('RAM') 111. The processing device 104 (controller 101) represents one or more general-purpose processing devices, such as a microprocessor, a central processing unit, or the like. More specifically, the processing device 104 (or controller 101) may be a complex instruction set computing ('CISC') microprocessor, a reduced instruction set computing ('RISC') microprocessor, a very long instruction word ('VLIW') microprocessor, or a processor that implements other instruction sets or a number of processors that implement a combination of instruction sets. The processing device 104 (controller 101) may also be one or more special-purpose processing devices, such as an ASIC, an FPGA, a digital signal processor ('DSP'), a network processor, or the like.

[0046] Processing device 104 may be connected to RAM 111 via data communication link 106, which may be embodied as a high-speed memory bus, such as a double data rate 4 ('DDR4') bus. Stored in RAM 111 is an operating system 112. In some embodiments, instructions 113 are stored in RAM 111. Instructions 113 may include computer program instructions for performing operations in a direct-mapped flash storage system. In one embodiment, a direct-mapped flash storage system is a system that directly addresses data blocks within a flash drive and does not require address translation performed by a storage controller of the flash drive.

[0047] In some implementations, the storage array controller 101 includes one or more host bus adapters 103A-C coupled to the processing device 104 via data communication links 105A-C. In some implementations, the host bus adapters 103A-C may be computer hardware that connects a host system (e.g., a storage array controller) to other networks and storage arrays. In some examples, the host bus adapters 103A-C may be Fibre Channel adapters that enable the storage array controller 101 to connect to a SAN, Ethernet adapters that enable the storage array controller 101 to connect to a LAN, or the like. The host bus adapters 103A-C may be coupled to the processing device 104 via data communication links 105A-C, such as a PCIe bus, for example.

[0048] In some embodiments, storage array controller 101 may include a host bus adapter 114 coupled to an expander 115. Expander 115 may be used to attach a host system to a greater number of storage drives. In embodiments where host bus adapter 114 is embodied as a SAS controller, expander 115 may be, for example, a SAS expander for enabling host bus adapter 114 to attach to storage drives.

[0049] In some embodiments, the storage array controller 101 may include a switch 116 coupled to the processing device 104 via the data communication link 109. The switch 116 may be a computer hardware device that can create multiple endpoints from a single endpoint, thereby enabling multiple devices to share a single endpoint. The switch 116 may be, for example, a PCIe switch that is coupled to a PCIe bus (e.g., the data communication link 109) and presents multiple PCIe connection points to the midplane.

[0050] In some implementations, storage array controller 101 includes a data communication link 107 for coupling storage array controller 101 to other storage array controllers. In some examples, data communication link 107 may be a Quick Path Interconnect (QPI) interconnect.

[0051] A conventional storage system using conventional flash drives may implement processes across the flash drives that are part of the conventional storage system. For example, a higher level process of the storage system may initiate and control processes across the flash drives. However, the flash drives of the conventional storage system may include their own storage controllers that also perform the processes. Thus, for the conventional storage system, both higher level processes (e.g., initiated by the storage system) and lower level processes (e.g., initiated by the storage controller of the storage system) may be performed.

[0052] To address various shortcomings of conventional storage systems, operations may be performed by higher level processes rather than by lower level processes. For example, a flash storage system may include a flash drive that does not include a storage controller that provides the process. Thus, the operating system of the flash storage system itself may initiate and control the process. This may be accomplished by a direct mapped flash storage system that directly addresses data blocks within the flash drive and does not require address translation performed by the flash drive's storage controller.

[0053] In some embodiments, storage drive 171A to F may be one or more partitioned storage devices. In some embodiments, one or more partitioned storage devices may be shingled HDDs. In some embodiments, one or more storage devices may be flash-based SSDs. In a partitioned storage device, the partition namespace on the partitioned storage device may be addressed by block groups that are grouped and aligned in natural sizes to form a number of addressable areas. In some embodiments utilizing SSDs, the natural size may be an erase block size based on the SSD. In some embodiments, the zones of the partitioned storage device may be defined during initialization of the partitioned storage device. In some embodiments, zones may be dynamically defined as data is written to the partitioned storage device.

[0054] In some embodiments, the zones may be heterogeneous, with some zones each being a page group and other zones being multiple page groups. In some embodiments, some zones may correspond to an erase block and other zones may correspond to multiple erase blocks. In embodiments, for a heterogeneous mix of programming modes, manufacturers, product types, and / or product generations of storage devices such as those suitable for heterogeneous assembly, updating, distributed storage, etc., zones may be any combination of different numbers of pages in units of page groups and / or erase blocks. In some embodiments, zones may be defined as having usage characteristics, such as properties of supporting data with a particular kind of lifespan (e.g., very short lifespan or very long lifespan). These properties may be used by the partitioned storage device to determine how the zone will be managed over the expected lifespan of the zone.

[0055] It should be understood that a zone is a virtual construction. Any particular zone may not have a fixed position at a storage device. Before allocation, a zone may not have any position at a storage device. A zone may correspond to a number, which represents a block of virtual allocatable space, which is the size of an erase block or other block sizes in various embodiments. When the system allocates or opens a zone, the zone is allocated to a flash memory or other solid-state storage memory, and as the system writes to the zone, the page is written to that mapped flash memory or other solid-state storage memory of the partition storage device. When the system closes the zone, the associated erase block or other size block is completed. At some point in the future, the system may delete the zone, which will release the allocated space of the zone. During its life cycle, for example, when the partition storage device performs internal maintenance, the zone may be moved to a different location of the partition storage device.

[0056] In some embodiments, the zones of the partitioned storage device may be in different states. The zone may be in an empty state, where data has not yet been stored at the zone. The empty zone may be explicitly opened or implicitly opened by writing data to the zone. This is the initial state of the zone on the newly partitioned storage device, but it may also be the result of a zone reset. In some embodiments, the empty zone may have a specified location within the flash memory of the partitioned storage device. In an embodiment, the location of the zone may be selected when the empty zone is first opened or first written (or if the write is buffered in the memory, then later). The zone may be explicitly or implicitly in an open state, where the zone in the open state may be written to store data with a write or append command. In an embodiment, the zone in the open state may also be written using a copy command that copies data from different zones. In some embodiments, the partitioned storage device may have a limit on the number of open zones at a particular time.

[0057] A zone in a closed state is a zone that has been partially written to but has entered a closed state after an explicit close operation was issued. A zone in a closed state may be left available for future writes, but some of the runtime overhead consumed by keeping the zone in an open state may be reduced. In some embodiments, a partitioned storage device may have a limit on the number of closed zones at a particular time. A zone in a full state is a zone that is storing data and can no longer be written to. A zone may be in a full state after a write has written data to the entire zone or due to a zone completion operation. Prior to a completion operation, a zone may or may not have been completely written to. However, after a completion operation, it may not be possible to open the zone for further writing without first performing a zone reset operation.

[0058] The mapping from zones to erase blocks (or to shingled tracks in an HDD) can be arbitrary, dynamic, and hidden from view. The process of opening a zone can be an operation that allows a new zone to be dynamically mapped to the underlying storage of a partitioned storage device and then allows data to be written by additional writes to the zone until the zone reaches capacity. A zone can be completed at any time, after which additional data may not be written to the zone. When the data stored at a zone is no longer needed, the zone can be reset, which effectively deletes the contents of the zone from the partitioned storage device, making the physical storage held by that zone available for subsequent data storage. Once a zone has been written and completed, the partitioned storage device ensures that the data stored at the zone will not be lost until the zone is reset. In the time between writing data to a zone and resetting the zone, a zone can be moved between shingled tracks or erase blocks as part of a maintenance operation within the partitioned storage device, such as by copying data to keep the data refreshed or handling memory cell aging in an SSD.

[0059] In some embodiments utilizing HDDs, resetting a zone may allow shingled tracks to be assigned to a new open zone that may be opened at some point in the future. In some embodiments utilizing SSDs, resetting a zone may cause the zone's associated physical erase blocks to be erased and subsequently reused for storage of data. In some embodiments, a zoned storage device may have a limit on the number of open zones at a point in time to reduce the amount of overhead dedicated to keeping zones open.

[0060] In some embodiments, storage drivers 171A to F may support an abstraction of organizing the virtual address space of the storage driver so that the virtual segment has a size that enables the virtual segment to be sequentially written within the same erase block of the storage device. The virtual segment is intended to be large enough to help reduce the overhead of managing flash memory at the erase block level while allowing flexibility in how to implement storage drivers 171A to F. For example, an erase block may have additional overhead that is not included in a virtual addressable segment. These segments may be large, but smaller than the entire erase block, especially when the size of the erase block is large. In an embodiment, storage drivers 171A to F may first write a data segment in a single-level cell (SLC) mode, and then later migrate it to a quad-level cell (QLC) mode segment when the data segment has survived a threshold amount of time without being rewritten. In some embodiments, storage drivers 171A to F may have internal redundancy, such as an internal RAID that spans erase blocks or spans chips to increase reliability. This may result in the presence of an internal RAID-oriented segment that is even much larger than the erase block of storage drivers 171A to F. In an embodiment, RAID may be added to a set of write erase blocks within storage drives 171A to F after a write erase block has been idle for a threshold amount of time so that the RAID may not have as much impact on the size of a sequential write segment. However, there may be other overhead, such as the storage device controller writing additional redundancy or checksums or index metadata within the erase blocks that are not accounted for in the reported sequential writeable segment size.

[0061] The operating system of the flash storage system can identify and maintain a list of allocation units across multiple flash drives of the flash storage system. An allocation unit can be a whole erase block or multiple erase blocks. The operating system can maintain a mapping or address range that maps addresses directly to erase blocks of the flash drives of the flash storage system.

[0062] The erase blocks mapped directly to the flash drive can be used to rewrite data and erase data. For example, an operation can be performed on one or more allocation units containing first data and second data, where the first data will be retained and the second data is no longer used by the flash storage system. The operating system can initiate a process to write the first data to a new location within other allocation units and erase the second data and mark the allocation unit as available for subsequent data. Therefore, the process can be performed only by the higher-level operating system of the flash storage system, and additional lower-level processes do not need to be performed by the controller of the flash drive.

[0063] Advantages of the process being performed only by the operating system of the flash storage system include improving the reliability of the flash drives of the flash storage system because no unnecessary or redundant write operations are performed during the process. Here, a possible novelty is the concept of initiating and controlling the process at the operating system of the flash storage system. In addition, the process can be controlled by the operating system across multiple flash drives. This is in contrast to the process being performed by the storage controller of the flash drive.

[0064] A storage system may consist of two storage array controllers that share a set of drives for failover purposes, or it may consist of a single storage array controller that provides storage services that utilize multiple drives, or it may consist of a distributed network of storage array controllers, each having a certain number of drives or a certain amount of flash storage, where the storage array controllers in the network cooperate to provide a complete storage service and cooperate in various aspects of the storage service including storage allocation and obsolete item collection.

[0065] Figure 1C A third example system 117 for data storage according to some embodiments is described. For purposes of illustration and not limitation, the system 117 (also referred to herein as a "storage system") includes numerous elements. It may be noted that in other embodiments, the system 117 may include the same, more, or fewer elements configured in the same or different manner.

[0066] In one embodiment, system 117 includes dual peripheral component interconnect ('PCI') flash memory devices 118 with individually addressable fast write storage. System 117 may include a storage device controller 119. In one embodiment, storage device controllers 119A-D may be a CPU, ASIC, FPGA, or any other circuitry that can implement the necessary control structures according to the present disclosure. In one embodiment, system 117 includes flash memory devices (e.g., including flash memory devices 120a-n) operably coupled to various channels of storage device controller 119. Flash memory devices 120a-n may be presented to controllers 119A-D as an addressable collection of flash pages, erase blocks, and / or control elements sufficient to allow storage device controllers 119A-D to program and retrieve various aspects of the flash memory. In one embodiment, the storage device controllers 119A to D can perform operations on the flash memory devices 120a to n, including storing and retrieving the data contents of a page, arranging and erasing any blocks, tracking statistics related to the use and reuse of flash memory pages, erase blocks and cells, tracking and predicting error codes and failures within the flash memory, controlling voltage levels associated with programming and retrieving the contents of flash memory cells, etc.

[0067] In one embodiment, the system 117 may include a RAM 121 to store individually addressable fast write data. In one embodiment, the RAM 121 may be one or more separate discrete devices. In another embodiment, the RAM 121 may be integrated into the storage device controller 119A to D or multiple storage device controllers. The RAM 121 may also be used for other purposes, such as temporary program storage for a processing device (e.g., a CPU) in the storage device controller 119.

[0068] In one embodiment, system 117 may include energy storage device 122, such as a rechargeable battery or capacitor. Energy storage device 122 may store enough energy to power storage controller 119, some amount of RAM (e.g., RAM 121), and some amount of flash memory (e.g., flash memory 120a-120n) for a sufficient time to write the contents of the RAM to the flash memory. In one embodiment, if the storage controller detects a loss of external power, storage controller 119A-D may write the contents of the RAM to the flash memory.

[0069] In one embodiment, the system 117 includes two data communication links 123a, 123b. In one embodiment, the data communication links 123a, 123b may be PCI interfaces. In another embodiment, the data communication links 123a, 123b may be based on other communication standards (e.g., HyperTransport, InfiniBand, etc.). The data communication links 123a, 123b may be based on the Non-Volatile Memory Express ('NVMe') or NVMe Architecture ('NVMf') specification, which allows external connection to the storage device controllers 119A to D from other components in the storage system 117. It should be noted that for convenience, the data communication links may be interchangeably referred to as PCI buses herein.

[0070] The system 117 may also include an external power source (not shown) which may be provided via one or both of the data communication links 123a, 123b, or may be provided separately. An alternative embodiment includes a separate flash memory (not shown) dedicated to storing the contents of the RAM 121. The storage device controllers 119A-D may present a logical device (which may include an addressable fast write logical device) or a distinct portion of the logical address space of the storage device 118 (which may be presented as PCI memory or as a persistent storage device) via the PCI bus. In one embodiment, operations to store into the device are directed into the RAM 121. In the event of a power failure, the storage device controllers 119A-D may write the stored contents associated with the addressable fast write logical storage to a flash memory (e.g., flash memory 120a-n) for long-term persistent storage.

[0071] In one embodiment, the logic device may include some representation of some or all of the contents of flash memory devices 120a-n, wherein the representation allows a storage system (e.g., storage system 117) including storage device 118 to directly address flash memory pages and directly reprogram erase blocks from storage system components external to the storage device over a PCI bus. The representation may also allow one or more of the external components to control and retrieve other aspects of the flash memory, including some or all of the following: tracking statistics related to the use and reuse of flash memory pages, erase blocks, and cells across all flash memory devices; tracking and predicting error codes and failures within and across flash memory devices; controlling voltage levels associated with programming and retrieving the contents of flash memory cells; etc.

[0072] In one embodiment, the energy storage device 122 may be sufficient to ensure completion of ongoing operations on the flash memory devices 120a to 120n, and the energy storage device 122 may power the storage device controllers 119A to D and the associated flash memory devices (e.g., 120a to n) for those operations, as well as for storing fast write RAM to flash memory. The energy storage device 122 may be used to store accumulated statistics and other parameters maintained and tracked by the flash memory devices 120a to n and / or the storage device controller 119. A separate capacitor or energy storage device (e.g., a smaller capacitor near or embedded within the flash memory device itself) may be used for some or all of the operations described herein.

[0073] Various schemes may be used to track and optimize the life of the energy storage component, such as adjusting voltage levels over time, partially discharging the energy storage device 122 to measure corresponding discharge characteristics, etc. If the available energy decreases over time, the effective available capacity of the addressable fast write storage device may be reduced to ensure it can be safely written based on the currently available stored energy.

[0074] Figure 1D A third example storage system 124 for data storage is illustrated in accordance with some embodiments. In one embodiment, the storage system 124 includes storage controllers 125a, 125b. In one embodiment, the storage controllers 125a, 125b are operably coupled to dual PCI storage devices. The storage controllers 125a, 125b are operably coupled (e.g., via a storage network 130) to a number of host computers 127a to n.

[0075] In one embodiment, two storage controllers (e.g., 125a and 125b) provide storage services, such as an SCS block storage array, a file server, an object server, a database or data analysis service, etc. The storage controllers 125a, 125b may provide services to host computers 127a to n external to the storage system 124 through a number of network interfaces (e.g., 126a to d). The storage controllers 125a, 125b may provide integrated services or applications entirely within the storage system 124, thereby forming a converged storage and computing system. The storage controllers 125a, 125b may utilize fast write memory within or across the storage devices 119a to d to journal ongoing operations to ensure that operations are not lost in the event of a power failure, storage controller removal, storage controller or storage system shutdown, or some failure of one or more software or hardware components within the storage system 124.

[0076] In one embodiment, the storage controllers 125a, 125b operate as PCI masters for one or the other PCI bus 128a, 128b. In another embodiment, 128a, 128b may be based on other communication standards (e.g., HyperTransport, InfiniBand, etc.). Other storage system embodiments may operate the storage controllers 125a, 125b as multi-masters for both PCI buses 128a, 128b. Alternatively, a PCI / NVMe / NVMf switching infrastructure or architecture may connect multiple storage controllers. Some storage system embodiments may allow storage devices to communicate directly with each other, rather than just with the storage controllers. In one embodiment, the storage device controller 119a may operate under guidance from the storage controller 125a to retrieve data from a computer that has been stored in RAM (e.g., Figure 1C The recalculated version of the RAM contents may be transferred after the storage controller has determined that the operation has been fully committed across the storage system or when the fast write memory on the device has reached a certain usage capacity or after a certain amount of time to ensure improved data security or free up addressable fast write capacity for reuse. This mechanism can be used, for example, to avoid secondary transfers from the storage controller 125a, 125b via a bus (e.g., 128a, 128b). In one embodiment, the recalculation may include compressing the data, appending indexes or other metadata, combining multiple data segments together, performing erasure code calculations, etc.

[0077] In one embodiment, under direction from the storage controller 125a, 125b, the storage device controller 119a, 119b is operable to retrieve data stored in RAM (eg, Figure 1CThe memory device 120 can be used to calculate data from the data in the RAM 121 of the memory device 125a and transfer the data to other memory devices without involving the memory controllers 125a, 125b. This operation can be used to mirror the data stored in one memory controller 125a to another memory controller 125b, or it can be used to offload the compression, data aggregation and / or erasure coding calculations and transfers to the memory device to reduce the load on the memory controller or the memory controller interface 129a, 129b to the PCI bus 128a, 128b.

[0078] The storage device controllers 119A-D may include mechanisms for implementing high availability primitives for use by other components of the storage system external to the dual PCI storage device 118. For example, a reservation or exclusion primitive may be provided so that in a storage system having two storage controllers providing highly available storage services, one storage controller may prevent the other storage controller from accessing or continuing to access the storage device. This may be used, for example, in situations where one of the controllers detects that the other controller is not functioning properly or where the interconnect between the two storage controllers itself may not be functioning properly.

[0079] In one embodiment, a storage system for use with dual PCI direct mapped storage devices with individually addressable fast write storage includes a system that manages erase blocks or groups of erase blocks as allocation units for storing data on behalf of a storage service or for storing metadata associated with the storage service (e.g., indexes, logs, etc.) or for proper management of the storage system itself. Flash pages, which may be several kilobytes in size, may be written as data arrives or when the storage system will hold data for a long time interval (e.g., exceeding a defined time threshold). To commit data more quickly, or to reduce the number of writes to the flash memory device, the storage controller may first write the data to an individually addressable fast write storage device on one or more storage devices.

[0080] In one embodiment, the storage controllers 125a, 125b may initiate the use of erase blocks within and across storage devices such as 118 based on the age and expected remaining useful life of the storage devices or based on other statistics. The storage controllers 125a, 125b may initiate obsolete item collection and data migration data between storage devices based on pages that are no longer needed and in order to manage flash page and erase block useful life and in order to manage overall system performance.

[0081] In one embodiment, the storage system 124 may utilize mirroring and / or erasure coding schemes as part of storing data in addressable fast write storage devices and / or as part of writing data to allocation units associated with erase blocks. Erasure codes may be used across storage devices and within erase blocks or allocation units, or within and across flash memory devices on a single storage device to provide redundancy against single or multiple storage device failures or to protect against corruption within flash memory pages caused by flash memory operations or by flash memory cell degradation. Mirroring and erasure coding at various levels may be used to recover from multiple types of failures occurring alone or in combination.

[0082] refer to Figure 2A The embodiments depicted in FIG. 1 and FIG. 2 illustrate a storage cluster that stores user data, such as user data originating from one or more users or client systems or other sources external to the storage cluster. The storage cluster uses erasure coding and redundant copies of metadata to distribute user data across storage nodes housed in a chassis or across multiple chassis. Erasure coding refers to a method of data protection or reconstruction in which data is stored across a set of different locations (such as disks, storage nodes, or geographic locations). Flash memory is a type of solid-state memory that can be integrated with embodiments, but embodiments can be extended to other types of solid-state memory or other storage media, including non-solid-state memory. Control of storage locations and workloads is distributed across storage locations in a cluster peer system. Tasks such as mediating communications between storage nodes, detecting when a storage node becomes unavailable, and balancing I / O (input and output) across storage nodes are all handled on a distributed basis. In some embodiments, data is laid out or distributed across multiple storage nodes in data segments or stripes that support data recovery. Ownership of data can be reassigned within the cluster, independent of the input and output type. This architecture, described in more detail below, allows a storage node in a cluster to fail while the system remains operational because data can be reconstructed from other storage nodes and thus remain available for input and output operations.In various embodiments, a storage node may be referred to as a cluster node, a blade, or a server.

[0083] The storage cluster may be housed in a chassis (i.e., a housing that houses one or more storage nodes). A mechanism for providing power to each storage node (e.g., a power distribution bus) and a communication mechanism (e.g., a communication bus that enables communication between storage nodes) are included in the chassis. According to some embodiments, the storage cluster may operate as a standalone system in one location. In one embodiment, the chassis houses at least two instances of both power distribution and communication buses that can be enabled or disabled individually. The internal communication bus may be an Ethernet bus, however, other technologies such as PCIe, Infiniband, and others are equally suitable. The chassis provides ports for an external communication bus for communication between multiple chassis and with client systems, either directly or through a switch. External communications may use technologies such as Ethernet, Infiniband, Fibre Channel, etc. In some embodiments, the external communication bus uses different communication bus technologies for inter-chassis and client communications. If a switch is deployed within a chassis or between chassis, the switch may be used as a translator between multiple protocols or technologies. When multiple chassis are connected to define a storage cluster, the storage cluster may be accessed by clients using a proprietary interface or a standard interface, such as a Network File System ('NFS'), Common Internet File System ('CIFS'), Small Computer System Interface ('SCSI'), or Hypertext Transfer Protocol ('HTTP'). Protocol translation from the client may occur at a switch, a chassis external communication bus, or within each storage node. In some embodiments, multiple chassis may be coupled or connected to each other via an aggregator switch. A portion and / or all of the coupled or connected chassis may be designated as a storage cluster. As discussed above, each chassis may have multiple blades, each blade having a media access control ('MAC') address, but in some embodiments, the storage cluster appears to the external network as having a single cluster IP address and a single MAC address.

[0084] Each storage node may be one or more storage servers, and each storage server is connected to one or more non-volatile solid-state memory units, which may be referred to as storage units or storage devices. One embodiment includes a single storage server in each storage node and between 1 and 8 non-volatile solid-state memory units, however, this one example is not meant to be limiting. The storage server may include a processor, DRAM, and interface for an internal communication bus and power distribution for each of the power buses. In some embodiments, inside the storage node, the interface and the storage unit share a communication bus, such as PCI Express. The non-volatile solid-state memory unit may directly access the internal communication bus interface through the storage node communication bus, or request the storage node to access the bus interface. The non-volatile solid-state memory unit contains an embedded CPU, a solid-state storage controller, and a certain number of solid-state mass storage devices, for example, in some embodiments, between 2 and 32 terabytes ('TB'). Embedded volatile storage media (such as DRAM) and energy storage devices are included in the non-volatile solid-state memory unit. In some embodiments, the energy storage device is a capacitor, supercapacitor, or battery that enables a subset of DRAM content to be transferred to a stable storage medium in the event of a power loss. In some embodiments, the non-volatile solid-state memory cell is constructed with a storage class memory such as phase change or magnetoresistive random access memory ('MRAM') that replaces DRAM and enables reduced power retention devices.

[0085] One of the many features of storage nodes and non-volatile solid-state storage devices is the ability to actively rebuild data in a storage cluster. Storage nodes and non-volatile solid-state storage devices can determine when a storage node or non-volatile solid-state storage device in a storage cluster is unreachable, independent of whether an attempt is made to read data involving that storage node or non-volatile solid-state storage device. The storage nodes and non-volatile solid-state storage devices then cooperate to recover and rebuild the data in at least a portion of the new location. This constitutes an active reconstruction because the system does not need to wait until the data is needed for a read access initiated from a client system employing the storage cluster to rebuild the data. These and other details of the storage memory and its operation are discussed below.

[0086] Figure 2A1 is a perspective view of a storage cluster 161 according to some embodiments, which has multiple storage nodes 150 and internal solid-state storage coupled to each storage node to provide a network attached storage device or storage area network. A network attached storage device, storage area network or storage cluster or other storage storage may include one or more storage clusters 161, each storage cluster having one or more storage nodes 150, arranging both the physical components and the amount of storage storage provided thereby in a flexible and reconfigurable manner. The storage cluster 161 is designed to be assembled in a rack, and one or more racks can be configured and populated for storage storage as needed. The storage cluster 161 has a chassis 138 with multiple slots 142. It should be understood that the chassis 138 can be referred to as a housing, a shell, or a rack unit. In one embodiment, the chassis 138 has fourteen slots 142, but other numbers of slots can be easily designed. For example, some embodiments have 4 slots, 8 slots, 16 slots, 32 slots, or other suitable numbers of slots. In some embodiments, each slot 142 can accommodate one storage node 150. The chassis 138 includes fins 148 that can be used to mount the chassis 138 on a rack. The fans 144 provide air circulation for cooling the storage nodes 150 and their components, but other cooling components can be used, or embodiments without cooling components can be designed. The switch architecture 146 couples the storage nodes 150 within the chassis 138 together and couples to a network for communication with the storage. In the embodiments depicted herein, for illustrative purposes, the slots 142 on the left side of the switch architecture 146 and the fans 144 are shown as being occupied by the storage nodes 150, while the slots 142 on the right side of the switch architecture 146 and the fans 144 are empty and can be used to insert the storage nodes 150. This configuration is an example, and in various other arrangements, one or more storage nodes 150 can occupy the slots 142. In some embodiments, the storage node arrangement need not be sequential or adjacent. The storage nodes 150 are hot-swappable, meaning that the storage nodes 150 can be inserted into or removed from the slots 142 in the chassis 138 without stopping or powering off the system. After the storage nodes 150 are inserted or removed from the slots 142, the system automatically reconfigures to recognize and adapt to the changes. In some embodiments, the reconfiguration includes restoring storage redundancy and / or rebalancing data or load.

[0087] Each storage node 150 may have multiple components. In the embodiment shown here, the storage node 150 includes a printed circuit board 159, which is populated by a CPU 156 (i.e., a processor), a memory 154 coupled to the CPU 156, and a non-volatile solid-state storage device 152 coupled to the CPU 156, but in other embodiments, other mountings and / or components may be used. The memory 154 has instructions executed by the CPU 156 and / or data operated on by the CPU 156. As further explained below, the non-volatile solid-state storage device 152 includes flash memory, or in other embodiments, includes other types of solid-state memory.

[0088] refer to Figure 2A , the storage cluster 161 is scalable, which means that storage capacity with non-uniform storage size can be easily added, as described above. One or more storage nodes 150 can be inserted into or removed from each chassis, and in some embodiments, the storage cluster self-configures. The plug-in storage node 150 can have different sizes whether it is installed in the chassis at the time of delivery or added later. For example, in one embodiment, the storage node 150 can have any multiple of 4TB, such as 8TB, 12TB, 16TB, 32TB, etc. In another embodiment, the storage node 150 can have any multiple of other storage amounts or capacities. The storage capacity of each storage node 150 is broadcast and affects the decision of how to stripe data. For maximum storage efficiency, the embodiment can be self-configured in the stripe to be as wide as possible, subject to predetermined continuous operation requirements, wherein up to one or up to two non-volatile solid-state storage devices 152 units or storage nodes 150 are lost in the chassis.

[0089] Figure 2B 1 is a block diagram showing a communication interconnect 173 and a power distribution bus 172 coupling a plurality of storage nodes 150. Return to Reference Figure 2A In some embodiments, the communication interconnect 173 may be included in or implemented with the switch architecture 146. In the case where multiple storage clusters 161 occupy a rack, in some embodiments, the communication interconnect 173 may be included in or implemented with the top of the rack switch. Figure 2B 1, storage cluster 161 is enclosed within a single chassis 138. External port 176 is coupled to storage node 150 via communication interconnect 173, while external port 174 is directly coupled to storage node. External power port 178 is coupled to power distribution bus 172. Storage node 150 may include non-volatile solid-state storage devices 152 of varying amounts and capacities, as described in reference to FIG. Figure 2A In addition, one or more storage nodes 150 may be computing-only storage nodes, such as Figure 2B. The authorization 168 is implemented on the non-volatile solid-state storage device 152, for example as a list or other data structure stored in a memory. In some embodiments, the authorization is stored within the non-volatile solid-state storage device 152 and supported by software executed on a controller or other processor of the non-volatile solid-state storage device 152. In another embodiment, the authorization 168 is implemented on the storage node 150, for example as a list or other data structure stored in a memory 154 and supported by software executed on a CPU 156 of the storage node 150. In some embodiments, the authorization 168 controls how data is stored in the non-volatile solid-state storage device 152 and where the data is stored in the non-volatile solid-state storage device 152. This control helps determine which type of erasure coding scheme is applied to the data and which storage nodes 150 have which parts of the data. Each authorization 168 can be assigned to a non-volatile solid-state storage device 152. In various embodiments, each authorization may control a range of inode numbers, segment numbers, or other data identifiers assigned to data by the file system, by the storage node 150, or by the non-volatile solid-state storage device 152.

[0090] In some embodiments, each piece of data and each piece of metadata has redundancy in the system. In addition, each piece of data and each piece of metadata has an owner, which can be called an authorization. If that authorization is unreachable, such as by the failure of a storage node, there is a succession plan for how to find that data or that metadata. In various embodiments, there are redundant copies of authorization 168. In some embodiments, authorization 168 has a relationship with a storage node 150 and a non-volatile solid-state storage device 152. Each authorization 168 covering the range of data segment numbers or other identifiers of data can be assigned to a specific non-volatile solid-state storage device 152. In some embodiments, authorization 168 for all such ranges is distributed on the non-volatile solid-state storage device 152 of the storage cluster. Each storage node 150 has a network port that provides access to the non-volatile solid-state storage device 152 of that storage node 150. Data can be stored in segments, and in some embodiments, the segment is associated with a segment number and that segment number is the indirectness of the configuration of a RAID (Redundant Array of Independent Disks) stripe. The assignment and use of authorization 168 therefore establishes indirectness to data. According to some embodiments, indirectness may be referred to as the ability to reference data indirectly (in this case, via authorization 168). A segment identifies a set of non-volatile solid-state storage devices 152 and a local identifier to the set of non-volatile solid-state storage devices 152 that may contain data. In some embodiments, the local identifier is an offset into the device and may be reused sequentially by multiple segments. In other embodiments, the local identifier is unique to a particular segment and is never reused. The offset in the non-volatile solid-state storage device 152 is applied to locate data for writing to or reading from the non-volatile solid-state storage device 152 (in the form of a RAID stripe). Data is striped across multiple units of the non-volatile solid-state storage device 152, which may include a non-volatile solid-state storage device 152 having an authorization 168 for a particular data segment or a non-volatile solid-state storage device 152 different from the non-volatile solid-state storage device 152.

[0091] If the location where a particular data segment is located changes, for example, during data movement or data reconstruction, the authorization 168 for that data segment should be consulted at the non-volatile solid-state storage device 152 or storage node 150 with that authorization 168. In order to locate a particular piece of data, an embodiment calculates a hash value of the data segment or applies an index node number or a data segment number. The output of this operation points to the non-volatile solid-state storage device 152 with the authorization 168 for that particular piece of data. In some embodiments, there are two stages for this operation. The first stage maps an entity identifier (ID) (such as a segment number, an index node number, or a directory number) to an authorization identifier. This mapping may include, for example, the calculation of a hash or a bit mask. The second stage is to map the authorization identifier to a specific non-volatile solid-state storage device 152, which can be accomplished by explicit mapping. The operation is repeatable so that when the calculation is performed, the result of the calculation can be repeatedly and reliably pointed to a specific non-volatile solid-state storage device 152 with that authorization 168. The operation may include a reachable storage node set as input. If the set of reachable non-volatile solid-state storage units changes, the best set changes. In some embodiments, the value maintained is the current assignment (which is always true) and the calculated value is the target assignment that the cluster will attempt to reconfigure. This calculation can be used to determine the best non-volatile solid-state storage device 152 for authorization when there is a group of non-volatile solid-state storage devices 152 that are reachable and constitute the same cluster. The calculation also determines an ordered set of peer non-volatile solid-state storage devices 152, which will also record the authorization mapped to the non-volatile solid-state storage device so that the authorization can be determined even when the assigned non-volatile solid-state storage device is unreachable. In some embodiments, if a specific authorization 168 is not available, a duplicate or alternative authorization 168 can be consulted.

[0092] refer to Figure 2A and 2B, two of the many tasks of the CPU 156 on the storage node 150 are to decompose write data and reassemble read data. When the system has determined that data is to be written, the authorization 168 for that data is located as described above. When the segment ID of the data has been determined, the write request is forwarded to the non-volatile solid-state storage device 152 of the host that is currently determined to be the authorization 168 determined from the segment. Then, the host CPU 156 of the storage node 150 on which the non-volatile solid-state storage device 152 and the corresponding authorization 168 reside decomposes or splits the data and transmits the data out to various non-volatile solid-state storage devices 152. The transmitted data is written as a data stripe according to an erasure coding scheme. In some embodiments, the request pulls the data, and in other embodiments, the data is pushed. In contrast, when data is read, the authorization 168 for the segment ID containing the data is located as described above. The host CPU 156 of the storage node 150 on which the non-volatile solid-state storage device 152 and the corresponding grant 168 reside requests data from the non-volatile solid-state storage device and the corresponding storage node pointed to by the grant. In some embodiments, the data is read from the flash storage device as a data stripe. Then, the host CPU 156 of the storage node 150 reorganizes the read data, thereby correcting any errors (if any) according to the appropriate erasure coding scheme and forwarding the reorganized data to the network. In other embodiments, some or all of these tasks can be handled in the non-volatile solid-state storage device 152. In some embodiments, the segment host requests data to be sent to the storage node 150 by requesting a page from the storage device and then sending the data to the storage node that issued the original request.

[0093] In an embodiment, authorization 168 operates to determine how operations will be performed for specific logical elements. Each of the logical elements can be operated by specific authorization across multiple storage controllers of the storage system. Authorization 168 can communicate with multiple storage controllers so that multiple storage controllers perform operations together for those specific logical elements.

[0094] In embodiments, a logical element may be, for example, a file, a directory, an object bucket, an individual object, a descriptive portion of a file or object, other forms of key-value databases or tables. In embodiments, performing an operation may involve, for example, ensuring consistency, structural integrity, and / or recoverability of other operations on the same logical element, reading metadata and data associated with that logical element, determining what data should be persistently written to the storage system to preserve any changes to the operation, or metadata and data may be determined where modular storage devices attached to multiple storage controllers in the storage system are to be stored.

[0095] In some embodiments, operations are token-based transactions that are efficiently passed within a distributed system. Each transaction may be accompanied or associated with a token that gives permission to perform the transaction. In some embodiments, authorization 168 can maintain the pre-transaction state of the system until the operation is completed. Token-based communications can be completed without global locks across the system and can also restart operations in the event of corruption or other failures.

[0096] In some systems, such as in UNIX-style file systems, data is handled with index nodes (inodes) that specify a data structure representing an object in the file system. For example, an object may be a file or a directory. Metadata may accompany the object as attributes, such as permission data and a creation timestamp, among other attributes. Segment numbers may be assigned to all or part of this object in the file system. In other systems, data segments are handled with segment numbers assigned elsewhere. For discussion purposes, a distribution unit is an entity, and an entity may be a file, a directory, or a segment. That is, an entity is a unit of data or metadata stored by a storage system. Entities are grouped into sets called grants. Each grant has a grant owner, which is a storage node that has the exclusive right to update the entities in the grant. In other words, a storage node contains a grant, and a grant contains an entity.

[0097] According to some embodiments, a segment is a logical container for data. A segment is an address space between the media address space and the physical flash location, i.e., the data segment number is in this address space. A segment may also contain metadata that enables data redundancy to be restored (rewritten to a different flash location or device) without involving higher-level software. In one embodiment, the internal format of a segment contains client data and a media map to determine the location of that data. Where applicable, each data segment is protected by breaking it into several data and parity fragments, e.g., to prevent the effects of memory and other failures. The data and parity fragments are distributed (i.e., striped) across the non-volatile solid-state storage device 152 coupled to the host CPU 156 according to an erasure coding scheme (see Figure 2E and 2G ). In some embodiments, the use of the term segment refers to a container and its location in the address space of a segment. According to some embodiments, the use of the term stripe refers to the same set of fragments as a segment and includes how the fragments and redundant or parity information are distributed.

[0098] A series of address space transformations occur across the entire storage system. At the top is a directory entry (file name) linked to an index node. The index node points to a media address space where data is logically stored. The media address can be mapped through a series of indirect media to expand the load of large files or implement data services such as deduplication or snapshots. The media address can be mapped through a series of indirect media to expand the load of large files or implement data services such as deduplication or snapshots. Then, the segment address is translated into a physical flash location. According to some embodiments, the physical flash location has an address range defined by the amount of flash memory in the system. The media address and segment address are logical containers, and in some embodiments, 128-bit or larger identifiers are used so that they are actually infinite, where the possibility of reuse is calculated to be longer than the expected life of the system. In some embodiments, addresses from logical containers are allocated in a hierarchical manner. Initially, each non-volatile solid-state storage device 152 unit can be assigned an address space range. Within this assigned range, the non-volatile solid-state storage device 152 is able to assign addresses without synchronizing with other non-volatile solid-state storage devices 152.

[0099] Data and metadata are stored by a set of underlying storage layouts optimized for different workload types and storage devices. These layouts incorporate a variety of redundancy schemes, compression formats, and indexing algorithms. Some of these layouts store information about authorization and authorization master controllers, while other layouts store file metadata and file data. Redundancy schemes include error correction codes that tolerate damaged bits within a single storage device (such as a NAND flash chip), erasure codes that tolerate failures of multiple storage nodes, and replication schemes that tolerate data center or regional failures. In some embodiments, low-density parity check ('LDPC') codes are used within a single storage unit. In some embodiments, Reed-Solomon encoding is used within a storage cluster, and mirroring is used within a storage grid. Metadata can be stored using an ordered log structure index (such as a log structure merge tree), and large data may not be stored in a log structure layout.

[0100] In order to maintain consistency across multiple copies of an entity, storage nodes implicitly agree on two things through computation: (1) the authorization containing the entity, and (2) the storage node containing the authorization. The assignment of entities to authorizations can be accomplished by pseudo-randomly assigning entities to authorizations, by splitting entities into ranges based on an externally generated key, or by placing a single entity into each authorization. Examples of pseudo-random schemes are linear hashing and hashing of the replication under scalable hashing ('RUSH') family, including controlled replication under scalable hashing ('CRUSH'). In some embodiments, pseudo-random assignment is only used to assign authorizations to nodes, because the set of nodes can change. The set of authorizations cannot change, so any subjective function can be applied in these embodiments. Some placement schemes automatically place authorizations on storage nodes, while other placement schemes rely on an explicit mapping of authorizations to storage nodes. In some embodiments, a pseudo-random scheme is used to map from each authorization to a set of candidate authorization owners. A pseudo-random data distribution function related to CRUSH can assign authorizations to storage nodes and create a list of where to assign authorizations. Each storage node has a copy of the pseudo-random data distribution function and can derive the same calculations to distribute and later find or locate the grant. In some embodiments, each of the pseudo-random schemes requires a reachable set of storage nodes as input in order to derive the same target node. Once an entity has been placed in a grant, the entity can be stored on a physical device so that expected failures will not result in unexpected data loss. In some embodiments, the rebalancing algorithm attempts to store copies of all entities within a grant in the same layout and on the same set of machines.

[0101] Examples of expected failures include device failures, stolen machines, data center fires, and regional disasters, such as nuclear or geological events. Different failures result in different levels of acceptable data loss. In some embodiments, the theft of a storage node affects neither the security nor the reliability of the system, while regional events may result in no data loss, a few seconds or minutes of update loss, or even complete data loss, depending on the system configuration.

[0102] In an embodiment, the placement of data for storage redundancy is independent of the placement of authorization for data consistency. In some embodiments, the storage node containing authorization does not contain any persistent storage device. Instead, the storage node is connected to a non-volatile solid-state storage unit that does not contain authorization. The communication interconnection between the storage node and the non-volatile solid-state storage unit consists of a variety of communication technologies and has non-uniform performance and fault tolerance characteristics. In some embodiments, as mentioned above, the non-volatile solid-state storage unit is connected to the storage node via PCI Express, and the storage node is connected together in a single chassis using an Ethernet backplane, and the chassis are connected together to form a storage cluster. In some embodiments, the storage cluster is connected to the client using Ethernet or Fiber Channel. If multiple storage clusters are configured as a storage grid, then multiple storage clusters are connected using the Internet or other long-distance networking links (e.g., "metro scale" links or private links that do not cross the Internet).

[0103] The authorized owner has the exclusive right to modify the entity, migrate the entity from one non-volatile solid-state storage unit to another non-volatile solid-state storage unit, and add and remove copies of the entity. This allows the redundancy of the underlying data to be maintained. When the authorized owner fails, is to be stopped or overloaded, the authorization is transferred to the new storage node. Transient failures make it very important to ensure that all non-faulty machines agree on the new authorized location. Ambiguities caused by transient failures can be automatically implemented through consensus protocols (such as Paxos) and hot-warm failover schemes via manual intervention by a remote system administrator or by a local hardware administrator (for example, by physically removing the failed machine from the cluster, or pressing a button on the failed machine). In some embodiments, a consensus protocol is used and failover is automatic. According to some embodiments, if too many failures or replication events occur in too short a period of time, the system enters a self-protection mode and stops replication and data movement activities until the administrator intervenes.

[0104] When authorization is transferred between a storage node and an authorization owner to update an authorization of an entity, the system transmits messages between the storage node and the non-volatile solid-state storage unit. Regarding persistent messages, messages with different purposes have different types. Depending on the type of message, the system maintains different sorting and durability guarantees. When processing persistent messages, messages are temporarily stored with multiple persistent and non-persistent storage hardware technologies. In some embodiments, messages are stored in RAM, NVRAM, and on NAND flash devices, and various protocols are used to effectively use each storage medium. Latency-sensitive client requests can be kept in replicated NVRAM, and then, later kept in NAND, while background rebalancing operations are kept directly to NAND.

[0105] Persistent messages are persistently stored before being transmitted. This allows the system to continue to serve client requests despite failures and component replacements. Although many hardware components contain unique identifiers visible to system administrators, manufacturers, hardware supply chains, and ongoing monitoring quality control infrastructure, applications running on infrastructure addresses virtualize the addresses. These virtualized addresses do not change over the life of the storage system, regardless of component failures and replacements. This allows each component of the storage system to be replaced over time without reconfiguring or interrupting client request processing, i.e., the system supports non-disruptive upgrades.

[0106] In some embodiments, virtualized addresses are stored with sufficient redundancy. The continuous monitoring system correlates hardware and software states with hardware identifiers. This allows for detection and prediction of failures due to faulty components and manufacturing details. In some embodiments, the monitoring system can also proactively transfer authorizations and entities away from affected devices before a failure occurs by removing components from critical paths.

[0107] Figure 2C 1 is a multi-level block diagram showing the contents of a storage node 150 and the contents of a non-volatile solid-state storage device 152 of the storage node 150. In some embodiments, data is passed to and from the storage node 150 by a network interface controller ('NIC') 202. Each storage node 150 has a CPU 156 and one or more non-volatile solid-state storage devices 152, as discussed above. Figure 2C Moving down one level in FIG. 1 , each non-volatile solid-state storage device 152 has relatively fast non-volatile solid-state memory, such as non-volatile random access memory ('NVRAM') 204 and flash memory 206. In some embodiments, NVRAM 204 may be a component (DRAM, MRAM, PCM) that does not require program / erase cycles and may be a memory that can support writes much more frequently than reads from the memory. Figure 2CMoving down another level in FIG. 2 , in one embodiment, NVRAM 204 is implemented as a high-speed volatile memory, such as dynamic random access memory (DRAM) 216, backed up by an energy reserve 218. The energy reserve 218 provides sufficient power to keep the DRAM 216 powered long enough to transfer the contents to the flash memory 206 in the event of a power failure. In some embodiments, the energy reserve 218 is a capacitor, supercapacitor, battery, or other device that supplies a suitable energy supply sufficient to enable the contents of the DRAM 216 to be transferred to a stable storage medium in the event of a loss of power. The flash memory 206 is implemented as a plurality of flash die 222, which may be referred to as a package of flash die 222 or an array of flash die 222. It should be appreciated that the flash die 222 may be packaged in any number of ways, with a single die per package, multiple die per package (i.e., a multi-chip package), in a hybrid package, as an exposed die on a printed circuit board or other substrate, as an encapsulated die, and the like. In the embodiment shown, the non-volatile solid-state storage device 152 has a controller 212 or other processor and an input-output (I / O) port 210 coupled to the controller 212. The I / O port 210 is coupled to the CPU 156 and / or the network interface controller 202 of the flash storage node 150. The flash input-output (I / O) port 220 is coupled to the flash die 222, and the direct memory access unit (DMA) 214 is coupled to the controller 212, the DRAM 216, and the flash die 222. In the embodiment shown, the I / O port 210, the controller 212, the DMA unit 214, and the flash I / O port 220 are implemented on a programmable logic device ('PLD') 208, such as an FPGA. In this embodiment, each flash die 222 has pages organized as 16kB (kilobyte) pages 224 and registers 226 through which data can be written to or read from the flash die 222. In further embodiments, other types of solid-state memory are used in place of or in addition to the flash memory illustrated within flash die 222 .

[0108] In various embodiments as disclosed herein, storage cluster 161 can be generally contrasted with storage array. Storage node 150 is part of a collection that creates storage cluster 161. Each storage node 150 owns a slice of data and provides the calculations required for the data. Multiple storage nodes 150 cooperate to store and retrieve data. As commonly used in storage arrays, storage memory or storage devices are less involved in processing and manipulating data. Storage memory or storage devices in storage arrays receive commands to read, write or erase data. Storage memory or storage devices in storage arrays do not know the larger system in which they are embedded, or what the data means. Storage memory or storage devices in storage arrays may include various types of storage memory, such as RAM, solid-state drives, hard disk drives, etc. The non-volatile solid-state storage device 152 unit described herein has multiple interfaces that are simultaneously effective and serve multiple purposes. In some embodiments, a certain functionality of storage node 150 is shifted into storage unit 152, thereby transforming storage unit 152 into a combination of storage unit 152 and storage node 150. Placing computation (as opposed to storing data) into the storage unit 152 places this computation closer to the data itself. Various system embodiments have a hierarchy of storage node tiers with different capabilities. In contrast, in a storage array, the controller owns and knows everything about all the data that the controller manages in a shelf or storage device. In a storage cluster 161, as described herein, multiple non-volatile solid-state storage device 152 units and / or multiple controllers in a storage node 150 cooperate in various ways (e.g., for erasure coding, data partitioning, metadata communication and redundancy, storage capacity expansion or contraction, data recovery, etc.).

[0109] Figure 2D Demonstration of use Figure 2A 150 and storage device 152 units of the embodiment of the storage server environment. In this version, each non-volatile solid-state storage device 152 unit has a chassis 138 (see Figure 2A ) on a PCIe (Peripheral Component Interconnect Express) board, for example, a controller 212 (see Figure 2C ) of the processor, FPGA, flash memory 206 and NVRAM 204 (which is a supercapacitor-backed DRAM 216, see Figure 2B and 2C ). The non-volatile solid-state storage device 152 unit may be implemented as a single board containing the storage device and may be the maximum tolerable failure domain within the chassis. In some embodiments, up to two non-volatile solid-state storage device 152 units may fail and the device will continue without data loss.

[0110] In some embodiments, the physical storage device is divided into named areas based on application use. NVRAM 204 is a continuous block of memory retained in the non-volatile solid-state storage device 152 DRAM 216 and is supported by NAND flash memory. NVRAM 204 is logically divided into multiple memory areas, two of which are written as spools (e.g., spool_region). The space within the NVRAM 204 spool is managed independently by each authorization 168. Each device provides a certain amount of storage space to each authorization 168. That authorization 168 further manages the life cycle and allocation within that space. The instance of the spool includes a distributed transaction or concept. When the main power to the non-volatile solid-state storage device 152 unit fails, the on-board supercapacitor provides a shorter duration of power retention. During this retention interval, the content of NVRAM 204 is refreshed to the flash memory 206. At the next power-on, the content of NVRAM 204 is restored from the flash memory 206.

[0111] With respect to the storage unit controller, the responsibility of the logical "controller" is distributed across each of the blades containing the authorization 168. This logical control is distributed across Figure 2D 1 and 2 are shown as host controller 242, mid-tier controller 244, and storage unit controller 246. The management of the control plane and storage plane are treated independently, but the components can be physically co-located on the same blade. Each authorization 168 effectively acts as an independent controller. Each authorization 168 provides its own data and metadata structures, its own background workers, and maintains its own life cycle.

[0112] Figure 2E It is displayed in Figure 2D Used in a storage server environment Figure 2A 1. A hardware block diagram of a blade 252 of an embodiment of a storage node 150 and storage unit 152 of FIG. 10 interacts with underlying physical resources, a control plane 254, a compute and storage plane 256, 258, and an authorization 168. The control plane 254 is partitioned into a number of authorizations 168 that can run on any of the blades 252 using the compute resources in the compute plane 256. The storage plane 258 is partitioned into a set of devices, each of which provides access to the flash memory 206 and NVRAM 204 resources. In one embodiment, the compute plane 256 can perform the operations of a storage array controller on one or more devices of the storage plane 258 (e.g., a storage array), as described herein.

[0113] exist Figure 2EIn the compute and storage planes 256, 258 of the system, the authorizations 168 interact with the underlying physical resources (i.e., devices). From the perspective of the authorizations 168, their resources are striped across all physical devices. From the perspective of the devices, they provide resources to all authorizations 168, no matter where the authorizations happen to be running. Each authorization 168 has or has been assigned one or more partitions 260 of storage memory in the storage unit 152, such as the partitions 260 in the flash memory 206 and the NVRAM 204. Each authorization 168 uses those allocated partitions 260 belonging to it for writing or reading user data. Authorizations can be associated with different amounts of physical storage of the system. For example, one authorization 168 can have a greater number of partitions 260 or larger size partitions 260 in one or more storage units 152 than one or more other authorizations 168.

[0114] Figure 2F Depicts the elasticity software layer in the blade 252 of a storage cluster according to some embodiments. In the elastic architecture, the elasticity software is symmetrical, i.e., the computing module 270 of each blade runs Figure 2F The storage manager 274 executes read and write requests from other blades 252 for data and metadata stored in the local storage unit 152 NVRAM 204 and flash memory 206. The authorization 168 implements the client request by issuing the necessary reads and writes to the blade 252 on whose storage unit 152 the corresponding data or metadata resides. The endpoint 272 parses the client connection request received from the switch architecture 146 monitoring software, relays the client connection request to the authorization 168 responsible for implementation, and relays the response of the authorization 168 to the client. The symmetric three-tier structure realizes a high degree of concurrency in the storage system. In these embodiments, elasticity is efficiently and reliably scaled out. In addition, elasticity implements a unique scale-out technology that evenly balances work across all resources regardless of the client access pattern, and maximizes concurrency by eliminating most of the need for inter-blade coordination that usually occurs for conventional distributed locking.

[0115] Still refer to Figure 2F, the authority 168 running in the compute module 270 of the blade 252 performs the internal operations required to complete the client request. One feature of resiliency is that the authority 168 is stateless, that is, it caches valid data and metadata in the DRAM of its own blade 252 for fast access, but the authority stores each update in a partition of its NVRAM 204 on three separate blades 252 until the update has been written to flash memory 206. In some embodiments, all storage system writes to the NVRAM 204 are triplicated to the partitions on the three separate blades 252. With three mirrored NVRAM 204 and persistent storage protected by parity and Reed-Solomon RAID checksums, the storage system can withstand the concurrent failure of two blades 252 without losing data, metadata, or access to either.

[0116] Because the authorization 168 is stateless, it can be migrated between blades 252. Each authorization 168 has a unique identifier. In some, the partitions of NVRAM 204 and flash memory 206 are associated with the identifier of the authorization 168, rather than the blade 252 on which it is running. Therefore, when the authorization 168 migrates, the authorization 168 continues to manage the same storage partitions from its new location. When a new blade 252 is installed in an embodiment of the storage cluster, the system automatically rebalances the load by partitioning the storage device of the new blade 252 for use by the authorization 168 of the system, migrating the selected authorization 168 to the new blade 252, opening the endpoint 272 on the new blade 252, and including it in the client connection distribution algorithm of the switch architecture 146.

[0117] The migrated authority 168 maintains the contents of its NVRAM 204 partition on flash memory 206 from its new location, processes read and write requests from other authorities 168, and completes client requests directed to it by endpoint 272. Similarly, if a blade 252 fails or is removed, the system redistributes its authorities 168 among the remaining blades 252 in the system. The redistributed authorities 168 continue to perform their original functions from their new location.

[0118] Figure 2GDepicts the authorities 168 and storage resources in the blades 252 of a storage cluster according to some embodiments. Each authority 168 is specifically responsible for a partition of flash memory 206 and NVRAM 204 on each blade 252. An authority 168 manages the content and integrity of its partition independently of other authorities 168. An authority 168 compresses incoming data and temporarily saves it in a partition of its NVRAM 204, and then consolidates, RAID protects, and maintains the data in a segment of the storage device in its flash memory 206 partition. As the authority 168 writes data to the flash memory 206, the storage manager 274 performs the necessary flash translations to optimize write performance and maximize media life. In the background, the authority 168 "does garbage collection" or reclaims space occupied by data that has been made obsolete by clients overwriting the data. It should be appreciated that because the partitions of the authority 168 are disjoint, no distributed locking is required to execute clients and writes or to perform background functions.

[0119] The embodiments described herein may utilize various software, communication and / or networking protocols. In addition, the configuration of the hardware and / or software may be adjusted to accommodate various protocols. For example, embodiments may utilize Active Directory, which is a Windows TM A database-based system that provides authentication, directory, policy and other services in an environment. In these embodiments, LDAP (Lightweight Directory Access Protocol) is an example application protocol for querying and modifying items in a directory service provider (such as an active directory). In some embodiments, a network lock manager ('NLM') is used as a facility that works in cooperation with a network file system ('NFS') to provide System V-style advisory file and record locking via a network. The Server Message Block ('SMB') protocol (a version of which is also called the Common Internet File System ('CIFS')) can be integrated with the storage system discussed herein. SMB operations are application layer network protocols that are typically used to provide shared access to files, printers, and serial ports, as well as miscellaneous communications between nodes on a network. SMB also provides an authenticated inter-process communication mechanism. AMAZON TMS3 (Simple Storage Service) is a web service provided by Amazon Web Services, and the system described herein can interface with Amazon S3 through web service interfaces (REST (Representational State Transfer), SOAP (Simple Object Access Protocol), and BitTorrent). The RESTful API (Application Programming Interface) decomposes transactions to create a series of small modules. Each module addresses a specific underlying part of the transaction. The control or permissions provided by these embodiments, especially for object data, can include the use of access control lists ('ACLs'). An ACL is a list of permissions attached to an object, and the ACL specifies which users or system processes are authorized to access an object and what operations are allowed on a given object. The system can utilize Internet Protocol version 6 ('IPv6') as well as IPv4, a communication protocol for providing identification and location systems and routing services across the Internet for computers on a network. Packet routing between networked systems can include equal cost multipath routing ('ECMP'), which is a routing strategy in which next-hop packet forwarding to a single destination can occur via multiple "best paths" that are at the top of the routing metric calculation. Multipath routing can be used with most routing protocols because it is a per-hop decision limited to a single router. The software can support multi-tenancy, which is an architecture in which a single instance of a software application serves multiple customers. Each customer may be referred to as a tenant. In some embodiments, tenants may be given the ability to customize some parts of the application, but may not be able to customize the code of the application. An embodiment may maintain an audit log. An audit log is a document that records events in a computing system. In addition to recording what resources are accessed, audit log entries typically include destination and source addresses, timestamps, and user login information to comply with various regulations. An embodiment may support various key management strategies, such as encryption key rotation. In addition, the system may support a dynamic root password or a change in a dynamic password.

[0120] Figure 3A A diagram of a storage system 306 coupled for data communication with a cloud service provider 302 is presented in accordance with some embodiments of the present disclosure. Although depicted in less detail, Figure 3A The storage system 306 depicted in FIG. 3 may be similar to the storage system 306 described above with reference to FIG. Figures 1A to 1D and Figures 2A to 2G In some embodiments, Figure 3AThe storage system 306 depicted in the diagram may be embodied as a storage system including unbalanced active / active controllers, a storage system including balanced active / active controllers, a storage system including active / active controllers (in which less than all resources of each controller are utilized such that each controller has reserved resources available to support failover), a storage system including fully active / active controllers, a storage system including data set isolation controllers, a storage system including a two-tier architecture with a front-end controller and a back-end integrated storage controller, a storage system including a scale-out cluster of dual-controller arrays, and combinations of such embodiments.

[0121] exist Figure 3A , the storage system 306 is coupled to the cloud service provider 302 via a data communication link 304. This data communication link 304 may be completely wired, completely wireless, or some aggregation of wired and wireless data communication paths. In this example, digital information may be exchanged between the storage system 306 and the cloud service provider 302 via the data communication link 304 using one or more data communication protocols. For example, the digital information may be exchanged between the storage system 306 and the cloud service provider 302 via the data communication link 304 using the Handheld Device Transfer Protocol ('HDTP'), the Hypertext Transfer Protocol ('HTTP'), the Internet Protocol ('IP'), the Real-time Transfer Protocol ('RTP'), the Transmission Control Protocol ('TCP'), the User Datagram Protocol ('UDP'), the Wireless Application Protocol ('WAP'), or other protocols.

[0122] For example, Figure 3A The cloud service provider 302 depicted in FIG. 3 may embody a system and computing environment that provides a wide range of services to users of the cloud service provider 302 by sharing computing resources via a data communication link 304. The cloud service provider 302 may provide on-demand access to a pool of shared configurable computing resources (e.g., computer networks, servers, storage devices, applications and services, etc.).

[0123] exist Figure 3A , cloud service provider 302 may be configured to provide various services to storage system 306 and users of storage system 306 by implementing various service models. For example, cloud service provider 302 may be configured to provide services by implementing an infrastructure as a service ('IaaS') service model, by implementing a platform as a service ('PaaS') service model, by implementing a software as a service ('SaaS') service model, by implementing an authentication as a service ('AaaS') service model, by implementing a storage as a service model (in which cloud service provider 302 provides access to its storage infrastructure for use by storage system 306 and users of storage system 306), and so forth.

[0124] exist Figure 3A In the example depicted in , cloud service provider 302 may be embodied as, for example, a private cloud, a public cloud, or a combination of private and public clouds. In embodiments where cloud service provider 302 is embodied as a private cloud, cloud service provider 302 may be dedicated to providing services to a single organization, rather than providing services to multiple organizations. In embodiments where cloud service provider 302 is embodied as a public cloud, cloud service provider 302 may provide services to multiple organizations. In yet alternative embodiments, cloud service provider 302 may be embodied as a mix of private and public cloud services with a hybrid cloud deployment.

[0125] although Figure 3A 306 and users of the storage system 306. For example, the storage system 306 may be coupled to (or even include) a cloud storage gateway. For example, this cloud storage gateway may be embodied as a hardware-based or software-based device that is located on premise with the storage system 306. This cloud storage gateway may operate as a bridge between local applications executed on the storage system 306 and remote cloud-based storage devices utilized by the storage system 306. By using a cloud storage gateway, an organization can move the primary iSCSI or NAS to the cloud service provider 302, thereby enabling the organization to save space on its locally deployed storage system. This cloud storage gateway may be configured to emulate a disk array, a block-based device, a file server, or other storage system that can translate SCSI commands, file server commands, or other appropriate commands into a REST space protocol that facilitates communication with the cloud service provider 302.

[0126] In order to enable storage system 306 and users of storage system 306 to use the services provided by cloud service provider 302, a cloud migration process may be performed during which data, applications, or other elements from an organization's local systems (or even from another cloud environment) are moved to cloud service provider 302. In order to successfully migrate data, applications, or other elements to the environment of cloud service provider 302, middleware such as cloud migration tools may be used to bridge the gap between the environment of cloud service provider 302 and the environment of the organization. In order to enable storage system 306 and users of storage system 306 to further use the services provided by cloud service provider 302, a cloud orchestrator may also be used to arrange and coordinate automated tasks to create a merged process or workflow. This cloud orchestrator may perform tasks such as configuring various components, whether those components are cloud components or locally deployed components, and managing the interconnections between such components.

[0127] exist Figure 3A, and as briefly described above, cloud service provider 302 may be configured to provide services to storage system 306 and users of storage system 306 by using a SaaS service model. For example, cloud service provider 302 may be configured to provide storage system 306 and users of storage system 306 with access to data analytics applications. Such data analytics applications may be configured, for example, to receive large amounts of telemetry data that is phoned home by storage system 306. Such telemetry data may describe various operational characteristics of storage system 306 and may be analyzed for a number of purposes, including, for example, determining the health of storage system 306, identifying workloads executing on storage system 306, predicting when storage system 306 will run out of various resources, recommending configuration changes, hardware or software upgrades, workflow migrations, or other actions that may improve the operation of storage system 306.

[0128] The cloud service provider 302 may also be configured to provide access to a virtualized computing environment to the storage system 306 and users of the storage system 306. Examples of such virtualized environments may include virtual machines created to emulate actual computers, virtualized desktop environments that separate logical desktops from physical machines, virtualized file systems that allow uniform access to different types of specific file systems, and many others.

[0129] although Figure 3A The example depicted in FIG. 3 illustrates storage system 306 coupled for data communication with cloud service provider 302, but in other embodiments, storage system 306 may be part of a hybrid cloud deployment in which private cloud elements (e.g., private cloud services, on-premises infrastructure, etc.) and public cloud elements (e.g., public cloud services, infrastructure, etc. that may be provided by one or more cloud service providers) are combined to form a single solution, orchestrated among various platforms. Such a hybrid cloud deployment may utilize hybrid cloud management software, such as, for example, Microsoft Azure. TM Azure TM Arc, which centralizes the management of hybrid cloud deployments to any infrastructure and enables deployment of services anywhere. In this example, the hybrid cloud management software can be configured to create, update, and delete resources (both physical and virtual) that form a hybrid cloud deployment, allocate compute and storage to specific workloads, monitor workloads and resources for performance, policy compliance, updates and patches, security status, or perform a variety of other tasks.

[0130] The reader will appreciate that by pairing the storage system described herein with one or more cloud service providers, a variety of products can be implemented. For example, where cloud resources are used to protect applications and data from damage caused by disasters, including in embodiments where the storage system can be used as primary data storage, Disaster Recovery as a Service ('DRaaS') can be provided. In such embodiments, full system backups can be performed, which allows business continuity to be maintained in the event of a system failure. In such embodiments, cloud data backup technology (either independently or as part of a larger DRaaS solution) can also be integrated into an overall solution including the storage system described herein and a cloud service provider.

[0131] The storage systems and cloud service providers described herein can be used to provide a number of security features. For example, the storage system can encrypt data at rest (and data can be sent to and from the encrypted storage system) and can use Key Management as a Service ('KMaaS') to manage encryption keys, keys for locking and unlocking storage devices, and the like. Likewise, a cloud data security gateway or similar mechanism can be used to ensure that data stored within the storage system does not end up being improperly stored in the cloud as part of a cloud data backup operation. In addition, micro-segmentation or identity-based segmentation can be used in the data center containing the storage system or within the cloud service provider to create security zones in data center and cloud deployments, which enables workloads to be isolated from each other.

[0132] To explain further, Figure 3B A diagram of a storage system 306 is presented in accordance with some embodiments of the present disclosure. Although not depicted in detail, Figure 3B The storage system 306 depicted in FIG. 3 may be similar to the storage system 306 described above with reference to FIG. Figures 1A to 1D and Figures 2A to 2G The storage system described above is because the storage system may include many of the components described above.

[0133] Figure 3BThe storage system 306 depicted in can include a large number of storage resources 308, which can be embodied in many forms. For example, the storage resources 308 can include nano-RAM or another form of non-volatile random access memory utilizing carbon nanotubes deposited on a substrate, 3D cross-point non-volatile memory, flash memory (including single-level cell ('SLC') NAND flash memory, multi-level cell ('MLC') NAND flash memory, three-level cell ('TLC') NAND flash memory, four-level cell ('QLC') NAND flash memory), or other. Likewise, the storage resources 308 can include non-volatile magnetoresistive random access memory ('MRAM'), including spin transfer torque ('STT') MRAM. The example storage resources 308 can alternatively include non-volatile phase change memory ('PCM'), quantum memory that allows storage and retrieval of photon quantum information, resistive random access memory ('ReRAM'), storage class memory ('SCM'), or other forms of storage resources, including any combination of the resources described herein. The reader will appreciate that other forms of computer memory and storage devices may be utilized by the memory system described above, including DRAM, SRAM, EEPROM, general purpose memory, and many others. Figure 3A The storage resources 308 depicted in may be embodied in a variety of form factors, including, but not limited to, dual in-line memory modules ('DIMMs'), non-volatile dual in-line memory modules ('NVDIMMs'), M.2, U.2, and others.

[0134] Figure 3B The storage resources 308 depicted in FIG. 3 may include various forms of SCM. The SCM may effectively treat fast non-volatile memory (e.g., NAND flash) as an extension of DRAM, so that the entire data set may be treated as an in-memory data set that resides entirely in DRAM. The SCM may include non-volatile media, such as, for example, NAND flash. This NAND flash may be accessed using NVMe, which may use the PCIe bus as its transport, thereby providing relatively low access latency compared to older protocols. In practice, network protocols for SSDs in an all-flash array may include NVMe using Ethernet (ROCE, NVME TCP), Fibre Channel (NVMe FC), InfiniBand (iWARP), and others that make it possible to treat fast non-volatile memory as an extension of DRAM. Given the fact that DRAM is typically byte-addressable and fast non-volatile memory (e.g., NAND flash) is block-addressable, a controller software / hardware stack may be required to convert block data into bytes stored in the media. Examples of media and software that may be used as SCM may include, for example, 3D XPoint, Intel memory drive technology, Samsung's Z-SSD, and others.

[0135] Figure 3B The storage resources 308 depicted in FIG. 3 may also include racetrack memory (also referred to as domain-wall memory). Such racetrack memory may be embodied in a form of non-volatile solid-state memory that relies, in addition to the charge of the electrons, on the intrinsic strength and orientation of the magnetic field created by the electrons in the solid-state device as they spin. By using spin-coherent current to move magnetic domains along nanopermalloy wires, the magnetic domains may be transferred by a magnetic read / write head positioned near the wire as the current is passed through the wire, which changes the magnetic domains to record a bit pattern. To create a racetrack memory device, many such wires and read / write elements may be packaged together.

[0136] Figure 3B The example storage system 306 depicted in can implement various storage architectures. For example, a storage system according to some embodiments of the present disclosure may utilize block storage, where data is stored in blocks, and each block essentially acts as an individual hard drive. A storage system according to some embodiments of the present disclosure may utilize object storage, where data is managed as objects. Each object may include the data itself, a variable amount of metadata, and a globally unique identifier, where object storage can be implemented at multiple levels (e.g., device level, system level, interface level). A storage system according to some embodiments of the present disclosure utilizes file storage, where data is stored in a hierarchical structure. This data can be saved in files and folders and presented to both the system that stores it and the system that retrieves it in the same format.

[0137] Figure 3B The example storage system 306 depicted in FIG. 300 may be embodied as a storage system in which additional storage resources may be added using a scale-up model, additional storage resources may be added using a scale-out model, or some combination thereof. In a scale-up model, additional storage may be added by adding additional storage devices. However, in a scale-out model, additional storage nodes may be added to a cluster of storage nodes, wherein such storage nodes may include additional processing resources, additional networking resources, and the like.

[0138] Figure 3B The example storage system 306 depicted in FIG. 300 can utilize the storage resources described above in a variety of different ways. For example, some portion of the storage resources can be used as a write cache, storage resources within the storage system can be used as a read cache, or tiering can be implemented within the storage system by placing data within the storage system according to one or more tiering policies.

[0139] Figure 3BThe storage system 306 depicted in also includes communication resources 310, which can be used to facilitate data communication between components within the storage system 306 and between the storage system 306 and computing devices external to the storage system 306, including embodiments in which those resources are separated by relatively wide areas. The communication resources 310 can be configured to utilize a variety of different protocols and data communication architectures to facilitate data communication between components within the storage system and computing devices external to the storage system. For example, the communication resources 310 can include: Fibre Channel ('FC') technology, such as an FC architecture and FC protocol that can transmit SCSI commands over an FC network; FC over Ethernet ('FCoE') technology, by which FC frames are encapsulated and transmitted over an Ethernet network; InfiniBand ('IB') technology, in which a switch fabric topology is used to facilitate transmissions between channel adapters; NVM Express ('NVMe') technology and Fabric-based NVMe ('NVMeoF') technology, by which non-volatile storage media attached via a PCI Express ('PCIe') bus can be accessed; and others. In fact, the storage system described above may directly or indirectly use neutrino communication techniques and devices by which information (including binary information) is transmitted using neutrino beams.

[0140] The communication resources 310 may also include mechanisms for accessing storage resources 308 within the storage system 306 utilizing Serial Attached SCSI ('SAS'), a Serial ATA ('SATA') bus interface for connecting storage resources 308 within the storage system 306 to a host bus adapter within the storage system 306, Internet Small Computer System Interface ('iSCSI') technology for providing block-level access to storage resources 308 within the storage system 306, and other communication resources that may be used to facilitate data communications between components within the storage system 306 and data communications between the storage system 306 and computing devices external to the storage system 306.

[0141] Figure 3B The storage system 306 depicted in FIG. 3 also includes processing resources 312 that can be used to execute computer program instructions and perform other computing tasks within the storage system 306. The processing resources 312 can include one or more ASICs and one or more CPUs customized for a particular purpose. The processing resources 312 can also include one or more DSPs, one or more FPGAs, one or more systems on a chip ('SoC'), or other forms of processing resources 312. The storage system 306 can utilize the storage resources 312 to perform a variety of tasks, including but not limited to supporting the execution of software resources 314, which will be described in more detail below.

[0142] Figure 3BThe storage system 306 depicted in FIG. 3 also includes software resources 314 that, when executed by processing resources 312 within the storage system 306, can perform a number of tasks. The software resources 314 can include, for example, one or more computer program instruction modules that, when executed by processing resources 312 within the storage system 306, are used to implement various data protection techniques. Such data protection techniques can be implemented, for example, by system software executing on computer hardware within the storage system, by a cloud service provider, or otherwise. Such data protection techniques can include data archiving, data backup, data replication, data snapshots, data and database cloning, and other data protection techniques.

[0143] Software resources 314 may also include software for implementing software defined storage ('SDS'). In this example, software resources 314 may include one or more computer program instruction modules that, when executed, provide for policy-based provisioning and management of data storage, independent of the underlying hardware. Such software resources 314 may be used to implement storage virtualization to separate storage hardware from the software that manages the storage hardware.

[0144] The software resources 314 may also include software for facilitating and optimizing I / O operations directed to the storage system 306. For example, the software resources 314 may include software modules that perform various data reduction techniques such as, for example, data compression, data deduplication, and others. The software resources 314 may include software modules that intelligently group I / O operations together to facilitate better use of the underlying storage resources 308, software modules that perform data migration operations to migrate from within the storage system, and software modules that perform other functions. Such software resources 314 may be embodied as one or more software containers or in many other ways.

[0145] To explain further, Figure 3C An example of a cloud-based storage system 318 according to some embodiments of the present disclosure is presented. Figure 3C In the example depicted in FIG. 1 , the cloud-based storage system 318 is entirely created in a cloud computing environment 316, such as, for example, Amazon Web Services ('AWS'). TM 、Microsoft Azure TM , Google Cloud Platform TM , IBM Cloud TM , Oracle Cloud TM The cloud-based storage system 318 may be used to provide services similar to those that may be provided by the storage systems described above.

[0146] Figure 3C318 includes two cloud computing instances 320, 322, each for supporting the execution of a storage controller application 324, 326. For example, the cloud computing instances 320, 322 may be embodied as instances of cloud computing resources (e.g., virtual machines) that may be provided by the cloud computing environment 316 to support the execution of software applications (e.g., the storage controller applications 324, 326). For example, each of the cloud computing instances 320, 322 may be executed on an Azure VM, wherein each Azure VM may include a high-speed temporary storage device that may be used as a cache (e.g., as a read cache). In one embodiment, the cloud computing instances 320, 322 may be embodied as Amazon Elastic Compute Cloud ('EC2') instances. In this example, an Amazon Machine Image ('AMI') including the storage controller applications 324, 326 may be launched to create and configure a virtual machine that may execute the storage controller applications 324, 326.

[0147] exist Figure 3C In the example method described in the embodiment, the storage controller applications 324, 326 may be embodied as computer program instruction modules that, when executed, perform various storage tasks. For example, the storage controller applications 324, 326 may be embodied as computer program instruction modules that, when executed, perform the same tasks as described above. Figure 1A The controllers 110A, 110B in the cloud-based storage system 318 perform the same tasks, such as writing data to the cloud-based storage system 318, erasing data from the cloud-based storage system 318, retrieving data from the cloud-based storage system 318, monitoring and reporting storage device utilization and performance, performing redundancy operations (such as RAID or RAID-like data redundancy operations), compressing data, encrypting data, deduplicating data, etc. The reader will understand that because there are two cloud computing instances 320, 322 each including a storage controller application 324, 326, in some embodiments, one cloud computing instance 320 can operate as a primary controller as described above, while the other cloud computing instance 322 can operate as a secondary controller as described above. The reader will understand that Figure 3C The storage controller applications 324, 326 depicted in FIG. 3 may include the same source code that executes within different cloud computing instances 320, 322 (eg, different EC2 instances).

[0148] The reader will appreciate that other embodiments that do not include primary and secondary controllers are within the scope of the present disclosure. For example, each cloud computing instance 320, 322 may operate as a primary controller for some portion of the address space supported by the cloud-based storage system 318, each cloud computing instance 320, 322 may operate as a primary controller in which the service of I / O operations directed to the cloud-based storage system 318 is divided in some other manner, etc. Indeed, in other embodiments in which cost savings may take precedence over performance requirements, there may be only a single cloud computing instance containing a storage controller application.

[0149] Figure 3C The cloud-based storage system 318 depicted in FIG. 3 includes cloud computing instances 340a, 340b, 340n having local storage devices 330, 334, 338. For example, the cloud computing instances 340a, 340b, 340n may embody examples of cloud computing resources that may be provided by the cloud computing environment 316 to support the execution of software applications. Figure 3C The cloud computing examples 340a, 340b, 340n may be different from the cloud computing examples 320, 322 described above because Figure 3C The cloud computing instances 340a, 340b, 340n of the storage controller applications 324, 326 have local storage 330, 334, 338 resources, while the cloud computing instances 320, 322 supporting the execution of the storage controller applications 324, 326 do not need to have local storage resources. For example, the cloud computing instances 340a, 340b, 340n with local storage 330, 334, 338 can be embodied as EC2 M5 instances including one or more SSDs, EC2 R5 instances including one or more SSDs, EC2 I3 instances including one or more SSDs, and so on. In some embodiments, the local storage 330, 334, 338 must be embodied as a solid-state storage device (e.g., an SSD) rather than a storage device using a hard disk drive.

[0150] exist Figure 3CIn the example depicted in FIG. 1 , each of the cloud computing instances 340a, 340b, 340n having local storage devices 330, 334, 338 may include a software daemon 328, 332, 336 that, when executed by the cloud computing instances 340a, 340b, 340n, may present itself to the storage controller application 324, 326 as if the cloud computing instances 340a, 340b, 340n were physical storage devices (e.g., one or more SSDs). In this example, the software daemons 328, 332, 336 may include computer program instructions similar to those typically included on a storage device, such that the storage controller application 324, 326 may send and receive the same commands that a storage controller would send to the storage device. In this way, the storage controller application 324, 326 may include code that is the same (or substantially the same) as the code that would be executed by the controller in the storage system described above. In these and similar embodiments, communication between the storage controller application 324, 326 and the cloud computing instances 340a, 340b, 340n having local storage devices 330, 334, 338 may utilize iSCSI, NVMe over TCP, messaging, a custom protocol, or some other mechanism.

[0151] exist Figure 3CIn the example depicted in FIG. 1 , each of the cloud computing instances 340a, 340b, 340n having local storage 330, 334, 338 may also be coupled to a block storage 342, 344, 346 provided by the cloud computing environment 316, such as (for example) as an Amazon Elastic Block Store ('EBS') volume. In this example, the block storage 342, 344, 346 provided by the cloud computing environment 316 may be utilized in a manner similar to how the NVRAM devices described above are utilized, in that a software daemon 328, 332, 336 (or some other module) executing within a particular cloud computing instance 340a, 340b, 340n may initiate writing data to its attached EBS volume as well as writing data to its local storage 330, 334, 338 resource upon receiving a request to write data. In some alternative embodiments, data may only be written to local storage 330, 334, 338 resources within a particular cloud computing instance 340a, 340b, 340n. In alternative embodiments, rather than using block storage 342, 344, 346 provided by the cloud computing environment 316 as NVRAM, actual RAM on each of the cloud computing instances 340a, 340b, 340n having local storage 330, 334, 338 may be used as NVRAM, thereby reducing network utilization costs associated with using EBS volumes as NVRAM. In yet another embodiment, high-performance block storage resources such as one or more Azure ultra disks may be used as NVRAM.

[0152] When a request to write data is received by a particular cloud computing instance 340a, 340b, 340n having a local storage 330, 334, 338, the software daemon 328, 332, 336 may be configured to write the data not only to its own local storage 330, 334, 338 resources and any appropriate block storage 342, 344, 346 resources, but the software daemon 328, 332, 336 may also be configured to write the data to a cloud-based object storage 348 attached to the particular cloud computing instance 340a, 340b, 340n. For example, the cloud-based object storage 348 attached to the particular cloud computing instance 340a, 340b, 340n may be embodied as Amazon Simple Storage Service ('S3'). In other embodiments, each cloud computing instance 320, 322 including a storage controller application 324, 326 may initiate the storage of data in the local storage 330, 334, 338 and cloud-based object storage 348 of the cloud computing instance 340a, 340b, 340n. In other embodiments, rather than using the cloud computing instances 340a, 340b, 340n with local storage 330, 334, 338 (also referred to herein as 'virtual drives') and cloud-based object storage 348 to store data, the persistent storage layer may be implemented in other ways. For example, one or more Azure ultra disks may be used to persistently store data (e.g., after the data has been written to the NVRAM layer). In embodiments where one or more Azure ultra disks may be used to persistently store data, the use of the cloud-based object storage 348 may be eliminated, such that data is only persistently stored in the Azure ultra disks without also writing the data to the object storage layer.

[0153] While the local storage 330, 334, 338 resources and block storage 342, 344, 346 resources utilized by the cloud computing instances 340a, 340b, 340n may support block-level access, the cloud-based object storage 348 attached to a particular cloud computing instance 340a, 340b, 340n only supports object-based access. The software daemons 328, 332, 336 may therefore be configured to take data blocks, package those blocks into objects, and write the objects to the cloud-based object storage 348 attached to the particular cloud computing instance 340a, 340b, 340n.

[0154] In some embodiments, all data stored by the cloud-based storage system 318 may be stored in both: 1) a cloud-based object store 348; and 2) at least one of a local storage 330, 334, 338 resource or a block storage 342, 344, 346 resource utilized by the cloud computing instances 340a, 340b, 340n. In such embodiments, the local storage 330, 334, 338 resource and the block storage 342, 344, 346 resource utilized by the cloud computing instances 340a, 340b, 340n may effectively operate as a cache that typically includes all data also stored in S3, such that all reads of data may be served by the cloud computing instances 340a, 340b, 340n without requiring the cloud computing instances 340a, 340b, 340n to access the cloud-based object store 348. However, the reader will appreciate that in other embodiments, all data stored by the cloud-based storage system 318 may be stored in the cloud-based object storage 348, but not all data stored by the cloud-based storage system 318 may be stored in at least one of the local storage 330, 334, 338 resources or block storage 342, 344, 346 resources utilized by the cloud computing instances 340a, 340b, 340n. In this example, various strategies may be utilized to determine which subset of data stored by the cloud-based storage system 318 should reside in: 1) the cloud-based object storage 348; and 2) at least one of the local storage 330, 334, 338 resources or block storage 342, 344, 346 resources utilized by the cloud computing instances 340a, 340b, 340n.

[0155] One or more computer program instruction modules executing within the cloud-based storage system 318 (e.g., a monitoring module executing on its own EC2 instance) can be designed to handle the failure of one or more of the cloud computing instances 340a, 340b, 340n having local storage 330, 334, 338. In this example, the monitoring module can handle the failure of one or more of the cloud computing instances 340a, 340b, 340n having local storage 330, 334, 338 by creating one or more new cloud computing instances with local storage, retrieving data stored on the failed cloud computing instances 340a, 340b, 340n from the cloud-based object storage 348, and storing the data retrieved from the cloud-based object storage 348 in the local storage on the newly created cloud computing instances. The reader will appreciate that many variations of this process can be implemented.

[0156] The reader will appreciate that various performance aspects of the cloud-based storage system 318 may be monitored (e.g., by a monitoring module executing in an EC2 instance) such that the cloud-based storage system 318 may be scaled up or scaled out as needed. For example, if the cloud computing instances 320, 322 used to support the execution of the storage controller applications 324, 326 are undersized and insufficient to service I / O requests issued by users of the cloud-based storage system 318, the monitoring module may create a new, more powerful cloud computing instance (e.g., a type of cloud computing instance that includes more processing power, more memory, etc.) that includes the storage controller application so that the new, more powerful cloud computing instance may begin operating as the primary controller. Likewise, if the monitoring module determines that the cloud computing instances 320, 322 used to support the execution of the storage controller applications 324, 326 are oversized and cost savings may be obtained by switching to a smaller, less powerful cloud computing instance, the monitoring module may create a new, less powerful (and cheaper) cloud computing instance that includes the storage controller application so that the new, less powerful cloud computing instance may begin operating as the primary controller.

[0157] The storage system described above may implement intelligent data backup techniques by which data stored in the storage system may be replicated and stored in a distinct location to avoid data loss in the event of an equipment failure or some other form of disaster. For example, the storage system described above may be configured to check each backup to avoid restoring the storage system to an unintended state. Consider an example in which malware infects the storage system. In this example, the storage system may include software resources 314 that may scan each backup to identify backups captured before the malware infected the storage system and those captured after the malware infected the storage system. In this example, the storage system may restore itself from a backup that does not contain the malware - or at least not restore the portion of the backup that contains the malware. In this example, the storage system may include software resources 314 that can scan each backup to identify the presence of malware (or a virus or something else unexpected), for example, by identifying write operations served by the storage system and originating from a network subnet suspected of having delivered malware, by identifying write operations served by the storage system and originating from users suspected of having delivered malware, by identifying write operations served by the storage system and checking the contents of the write operations against a fingerprint of malware, and in many other ways.

[0158] The reader will further appreciate that backups (typically in the form of one or more snapshots) may also be used to perform rapid recovery of the storage system. Consider an example where a storage system is infected with ransomware that locks users out of the storage system. In this example, software resources 314 within the storage system may be configured to detect the presence of ransomware and may further be configured to use the retained backup to recover the storage system to a point in time prior to the point in time when the ransomware infected the storage system. In this example, the presence of ransomware may be explicitly detected through the use of a software tool utilized by the system, through the use of a key (e.g., a USB drive) inserted into the storage system, or in a similar manner. Likewise, the presence of ransomware may be inferred in response to system activity satisfying a predetermined fingerprint, such as, for example, no reads or writes into the system for a predetermined period of time.

[0159] The reader will appreciate that the various components described above can be grouped into one or more optimized computing packages as a converged infrastructure. Such a converged infrastructure can include pools of computer, storage, and networking resources that can be shared by multiple applications and managed in a collective manner using policy-driven processes. Such a converged infrastructure can be implemented with a converged infrastructure reference architecture, with standalone appliances, with a software-driven hyper-convergence approach (e.g., hyper-convergence infrastructure), or in other ways.

[0160] The reader will appreciate that the storage systems described in this disclosure can be used to support various types of software applications. Indeed, the storage system can be 'application-aware' in the sense that the storage system can obtain, maintain, or otherwise access information describing connected applications (e.g., applications utilizing the storage system) to optimize the operation of the storage system based on intelligence about the applications and their utilization patterns. For example, the storage system can optimize data layout, optimize cache behavior, optimize 'QoS' tiers, or perform some other optimization designed to improve the storage performance experienced by the application.

[0161] As an example of a type of application that may be supported by the storage system described herein, storage system 306 may be used to support such applications by providing storage resources to the following applications: artificial intelligence ('AI') applications, database applications, XOps projects (e.g., DevOps projects, DataOps projects, MLOps projects, ModelOps projects, PlatformOps projects), electronic design automation tools, event-driven software applications, high-performance computing applications, simulation applications, high-speed data capture and analysis applications, machine learning applications, media production applications, media serving applications, picture archiving and communication system ('PACS') applications, software development applications, virtual reality applications, augmented reality applications, and many other types of applications.

[0162] Given the fact that storage systems include compute resources, storage resources, and a wide variety of other resources, storage systems may be well suited to support resource-intensive applications such as, for example, AI applications. AI applications may be deployed in a variety of areas, including: predictive maintenance in manufacturing and related areas; healthcare applications, such as patient data and risk analysis; retail and marketing deployments (e.g., search notifications, social media notifications); supply chain solutions; fintech solutions, such as business analytics and reporting tools; operational deployments, such as real-time analytics tools, application performance management tools, IT infrastructure management tools; and many others.

[0163] Such AI applications may enable a device to be aware of its environment and take actions that maximize its chances of success at a goal. Examples of such AI applications may include IBM Watson TM , Microsoft Oxford TM , Google DeepMind TM , Baidu Minwa TM and others.

[0164] The storage system described above may also be well suited for supporting other types of resource-intensive applications, such as, for example, machine learning applications. Machine learning applications can perform various types of data analysis to automate analytical model building. Using algorithms that iteratively learn from data, machine learning applications can enable computers to learn without being explicitly programmed. A specific area of ​​machine learning is called reinforcement learning, which involves taking appropriate actions in a specific situation to maximize rewards.

[0165] In addition to the resources already described, the storage system described above may also include a graphics processing unit ('GPU'), sometimes referred to as a visual processing unit ('VPU'). Such a GPU may be embodied as a dedicated electronic circuit that quickly manipulates and changes memory to accelerate the creation of images in a frame buffer that are desired for output to a display device. Such a GPU may be included within any of the computing devices that are part of the storage system described above, including as one of many individually scalable components of the storage system, where other examples of individually scalable components of such a storage system may include storage components, memory components, computing components (e.g., CPUs, FPGAs, ASICs), networking components, software components, and others. In addition to the GPU, the storage system described above may also include a neural network processor ('NNP') for use in various aspects of neural network processing. Such an NNP may be used in place of (or in addition to) a GPU, and it may also be independently scalable.

[0166] As described above, the storage system described herein can be configured to support artificial intelligence applications, machine learning applications, big data analysis applications, and many other types of applications. The rapid growth of such applications is driven by three technologies: deep learning (DL), GPU processors, and big data. Deep learning is a computing model that uses massively parallel neural networks inspired by the human brain. Instead of experts handcrafting software, deep learning models write their own software by learning from a large number of examples. Such GPUs can include thousands of cores, which are well suited to running algorithms that loosely represent the parallel nature of the human brain.

[0167] Advances in deep neural networks, including the development of multi-layer neural networks, have inspired a wave of new algorithms and tools for data scientists to mine their data with artificial intelligence (AI). Using improved algorithms, larger datasets, and a variety of frameworks (including open source software libraries for machine learning across a range of tasks), data scientists are addressing new use cases such as autonomous vehicles, natural language processing and understanding, computer vision, machine reasoning, strong AI, and many others. Applications of AI technology have been implemented in a wide variety of products, including, for example: Amazon Echo's voice recognition technology, which allows users to talk to their machines; Google Translate TM , which allows machine-based language translation; Spotify’s Discover Weekly, which provides recommendations for new songs and artists that users may like based on their usage and traffic analytics; Quill’s text generation product, which takes structured data and turns it into narrative stories; Chatbot, which provides real-time, context-specific answers to questions in a conversational format; and many others.

[0168] Data is at the heart of modern AI and deep learning algorithms. Before training can begin, one problem that must be solved is around collecting labeled data that is critical to training accurate AI models. A full-scale AI deployment may be required to continuously collect, clean, transform, label, and store large amounts of data. Adding additional high-quality data points translates directly into more accurate models and better insights. Data samples may undergo a series of processing steps, including but not limited to: 1) ingesting data from external sources into the training system and storing the data in raw form; 2) cleaning and transforming data in a format that is convenient for training, including linking data samples to appropriate labels; 3) exploring parameters and models, testing quickly with smaller data sets, and iterating to converge on the most promising model to advance into the production cluster; 4) performing a training phase to select several batches of random input data, including both new and older samples, and feeding those into the production GPU server for computation to update model parameters; and 5) evaluating the use of a reserved portion of the data that was not used for training in order to evaluate the model accuracy of the retained data. This life cycle can be applied to any type of parallelized machine learning, not just neural networks or deep learning. For example, a standard machine learning framework may rely on CPUs instead of GPUs, but the data ingestion and training workflows can be the same. Readers will appreciate that a single shared storage data hub creates a coordination point throughout the life cycle without the need for additional data copies during the ingestion, preprocessing, and training phases. Ingested data is rarely used for just one purpose, and shared storage gives the flexibility to train multiple different models or apply traditional analytics to the data.

[0169] The reader will appreciate that each stage in the AI ​​data pipeline may have different requirements from a data center point (e.g., a storage system or a collection of storage systems). A scale-out storage system must provide uncompromised performance for all manner of access types and patterns (from small metadata-heavy to large files, from random to sequential access patterns, and from low to high concurrency). The storage system described above can be used as an ideal AI data center point because the system can serve unstructured workloads. In the first stage, the data is ideally ingested and stored on the same data center point that will be used in subsequent stages to avoid additional data copying. The next two steps can be completed on a standard computing server that optionally includes a GPU, and then, in the fourth and final stage, the complete training production job is run on a powerful GPU-accelerated server. Typically, there is a production pipeline next to the experimental pipeline operating on the same dataset. In addition, GPU-accelerated servers can be used independently for different models or combined together to train on a larger model, or even distributed training across multiple systems. If the shared storage hierarchy is slow, then for each stage, data must be copied to local storage, resulting in wasted time staging data on different servers. The ideal data hub for an AI training pipeline provides performance similar to data stored locally on server nodes, while also having the simplicity and performance to enable all pipeline stages to operate concurrently.

[0170] In order for the storage system described above to be used as a data center or as part of an AI deployment, in some embodiments, the storage system may be configured to provide DMA between storage devices included in the storage system and one or more GPUs used in an AI or big data analytics pipeline. One or more GPUs may be coupled to the storage system, for example, via NVMe over Fabric ('NVMe-oF'), so that bottlenecks such as the host CPU can be bypassed and the storage system (or one of the components contained therein) can directly access the GPU memory. In this example, the storage system may utilize an API hook to the GPU to transfer data directly to the GPU. For example, the GPU may be embodied as an Nvidia TM The GPU and the storage system may support GPUDirect Storage ('GDS') software or have similar proprietary software that enables the storage system to transfer data to the GPU via RDMA or similar mechanisms.

[0171] Although the preceding paragraphs discuss deep learning applications, the reader will appreciate that the storage system described herein may also be part of a distributed deep learning ('DDL') platform to support the execution of DDL algorithms. The storage system described above may also be paired with other technologies (e.g., TensorFlow, an open source software library for data flow programming across a range of tasks that can be used for machine learning applications (e.g., neural networks) to facilitate the development of such machine learning models, applications, and the like.

[0172] The storage system described above can also be used in a neuromorphic computing environment. Neuromorphic computing is a form of computing that simulates brain cells. To support neuromorphic computing, an architecture of interconnected "neurons" replaces traditional computing models with low-power signals transmitted directly between neurons to achieve more efficient computing. Neuromorphic computing can use very large scale integration (VLSI) systems containing electronic analog circuits for simulating the neurobiological architecture present in the nervous system, as well as analog, digital, mixed-mode analog / digital VLSI, and software systems that implement models of the nervous system for perception, motor control, or multi-sensory integration.

[0173] The reader will appreciate that the storage system described above may be configured to support the storage or use of blockchains and derivatives (as well as other types of data), such as, for example, IBM TM The open source blockchain and related tools that are part of the Hyperledger project, permissioned blockchains where a certain number of trusted parties are allowed to access the blockchain, blockchain products that enable developers to build their own distributed ledger projects, and others. The blockchain and storage system described in this article can be used to support on-chain storage of data as well as off-chain storage of data.

[0174] Off-chain storage of data can be implemented in various ways, and can occur when the data itself is not stored within the blockchain. For example, in one embodiment, a hash function may be utilized, and the data itself may be fed into the hash function to produce a hash value. In this example, the hash of a large number of pieces of data may be embedded within a transaction, rather than the data itself. The reader will appreciate that in other embodiments, alternatives to blockchains may be used to facilitate decentralized storage of information. For example, one alternative to blockchain that may be used is a blockweave. While conventional blockchains store each transaction for confirmation, blockweaves allow for secure decentralization without using the entire chain, thereby enabling low-cost on-chain storage of data. Such blockweaves may utilize a consensus mechanism based on proof of access (PoA) and proof of work (PoW).

[0175] The storage systems described above may be used alone or in combination with other computing devices to support in-memory computing applications. In-memory computing involves storing information in RAM distributed across a cluster of computers. The reader will appreciate that the storage systems described above, particularly those that are configurable with customizable amounts of processing resources, storage resources, and memory resources (e.g., those systems where the blades contain configurable amounts of each type of resource), may be configured in a manner that can provide an infrastructure capable of supporting in-memory computing. Similarly, the storage systems described above may include component parts (e.g., NVDIMMs, 3D crosspoint storage devices that provide persistent fast random access memory) that may, in effect, provide an improved in-memory computing environment over an in-memory computing environment that relies on RAM distributed across dedicated servers.

[0176] In some embodiments, the storage system described above may be configured to operate as a hybrid in-memory computing environment that includes a universal interface to all storage media (e.g., RAM, flash storage, 3D crosspoint storage). In such embodiments, users may not know the details about where their data is stored, but they can still use the same complete unified API to address the data. In such embodiments, the storage system may (in the background) move the data to the fastest tier available - including relying on various characteristics of the data or relying on some other heuristics to intelligently place the data. In this example, the storage system may even use existing products (such as Apache Ignite and GridGain) to move data between various storage tiers, or the storage system may use custom software to move data between various storage tiers. The storage system described herein may implement various optimizations to improve the performance of in-memory computing, such as (for example) making the computing as close to the data as possible.

[0177] The reader will further appreciate that in some embodiments, the storage system described above may be paired with other resources to support the applications described above. For example, an infrastructure may include primary computers in the form of servers and workstations that specialize in using general purpose computing on graphics processing units ('GPGPUs') to accelerate deep learning applications that are interconnected into a compute engine to train parameters of a deep neural network. Each system may have Ethernet external connectivity, Infiniband external connectivity, some other form of external connectivity, or some combination thereof. In this example, the GPUs may be grouped for a single large training or used independently to train multiple models. The infrastructure may also include a storage system (such as the storage system described above) to provide, for example, a scale-out all-flash file or object storage area through which data can be accessed via high-performance protocols (such as NFS, S3, etc.). The infrastructure may also include redundant top-of-rack Ethernet switches connected to storage devices and computers, such as via ports in an MLAG port channel, to achieve redundancy. The infrastructure may also include additional computing in the form of white box servers, optionally with GPUs, for data ingestion, preprocessing, and model debugging. The reader will appreciate that additional infrastructure is also possible.

[0178] The reader will appreciate that the storage system described above, alone or in coordination with other computing machinery, can be configured to support other AI-related tools. For example, the storage system can use tools such as ONXX or other open neural network exchange formats that make it easier to transfer models written in different AI frameworks. Likewise, the storage system can be configured to support tools such as Amazon's Gluon that allow developers to prototype, build, and train deep learning models. In fact, the storage system described above can be part of a larger platform, such as IBM TM Private cloud data, which includes integrated data science, data engineering, and application building services.

[0179] The reader will further appreciate that the storage system described above may also be deployed as an edge solution. Such an edge solution may be in place to optimize cloud computing systems by performing data processing at the edge of the network, near the source of the data. Edge computing can push applications, data, and computing power (i.e., services) from a centralized point to the logical extreme of the network. By using an edge solution, such as the storage system described above, computing tasks can be performed using computing resources provided by such a storage system, data can be stored using the storage resources of the storage system, and cloud-based services can be accessed by using various resources (including networking resources) of the storage system. By performing computing tasks on an edge solution, storing data on an edge solution, and generally using an edge solution, consumption of expensive cloud-based resources can be avoided, and in fact, performance improvements can be experienced relative to a heavier reliance on cloud-based resources.

[0180] While many tasks can benefit from utilizing edge solutions, some specific uses may be particularly well suited for deployment in this environment. For example, devices such as drones, self-driving cars, robots, and others may require extremely fast processing—so fast, in fact, that sending data up to a cloud environment and receiving data processing support back may simply be too slow. As an additional example, some IoT devices (such as connected cameras) may not be well suited to utilizing cloud-based resources because sending data to the cloud may be impractical (not just from a privacy perspective, a security perspective, or a financial perspective) simply due to the sheer volume of data involved. Thus, many tasks that truly involve data processing, storage, or communication may be better suited to a platform that includes edge solutions, such as the storage system described above.

[0181] The storage system described above can be used alone or in combination with other computing resources as a network edge platform that combines computing resources, storage resources, networking resources, cloud technologies, network virtualization technologies, etc. As part of the network, the edge can have characteristics similar to other network facilities from customer premises and backhaul aggregation facilities to access points (PoPs) and regional data centers. The reader will understand that network workloads (such as virtual network functions (VNFs) and others) will reside on the network edge platform. Through the combination of containers and virtual machines, the network edge platform can rely on controllers and schedulers that are no longer geographically co-located with data processing resources. Functions can be split into control planes, user and data planes, or even state machines as microservices, allowing independent optimization and expansion techniques to be applied. Such user and data planes can be implemented by adding accelerators that reside in server platforms (such as FPGAs and smart NICs) and are implemented by SDN-enabled commercial silicon and programmable ASICs.

[0182] The storage system described above can also be optimized for big data analytics, including being used as part of a composable data analytics pipeline, where a containerized analytics architecture, for example, makes analytics capabilities more composable. Big data analytics can be generally described as the process of examining large and diverse data sets to discover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information that can help organizations make smarter business decisions. As part of that process, semi-structured and unstructured data (such as, for example, Internet clickstream data, web server logs, social media content, text from customer emails and survey responses, mobile phone call detail records, IoT sensor data, and other data) can be converted into a structured form.

[0183] The storage system described above may also support (including being implemented as a system interface) applications that perform tasks in response to human voice. For example, the storage system may support the execution of an intelligent personal assistant application such as (for example) Amazon's Alexa TM , Apple Siri TM , Google Voice TM , Samsung Bixby TM , Microsoft Cortana TM and others. Although the examples described in the previous sentences use voice as input, the storage system described above may also support chatbots, conversational robots, chatbots, or other applications that are artificial dialogue entities or configured to conduct conversations via auditory or textual methods. Likewise, the storage system may actually execute such an application to enable users (e.g., system administrators) to interact with the storage system via voice. Such applications are typically capable of voice interaction, music playback, making to-do lists, setting alarms, streaming podcasts, playing audiobooks, and providing weather, traffic, and other real-time information, such as news, but in embodiments according to the present disclosure, such applications may be used as interfaces for various system management operations.

[0184] The storage systems described above may also implement an AI platform to realize the vision of self-driving storage. Such AI platforms may be configured to provide global predictive intelligence by collecting and analyzing a large number of storage system telemetry data points for easy management, analysis, and support. In fact, such storage systems may be able to predict both capacity and performance, as well as generate intelligent recommendations for workload deployment, interaction, and optimization. Such AI platforms may be configured to scan all incoming storage system telemetry data against a library of problem fingerprints to predict and resolve incidents in real time before they impact customer environments, and capture hundreds of performance-related variables for predicting performance loads.

[0185] The storage system described above can support the serial or simultaneous execution of artificial intelligence applications, machine learning applications, data analysis applications, data transformations, and other tasks that can collectively form an AI ladder. This AI ladder can be effectively formed by combining such elements to form a complete data science pipeline, where there are dependencies between the elements of the AI ​​ladder. For example, AI may require some form of machine learning, machine learning may require some form of analysis, analysis may require some form of data and information system structure, and so on. Thus, each element can be viewed as a rung in the AI ​​ladder, which together can form a complete and complex AI solution.

[0186] The storage system described above may also be used alone or in combination with other computing environments to provide an AI-everywhere experience, where AI permeates broad and expansive aspects of business and life. For example, AI may play an important role in providing deep learning solutions, deep reinforcement learning solutions, general artificial intelligence solutions, autonomous vehicles, cognitive computing solutions, commercial UAVs or drones, conversational user interfaces, enterprise classification, ontology management solutions, machine learning solutions, smart dust, smart robots, smart workplaces, and many others.

[0187] The storage system described above may also be used alone or in combination with other computing environments to provide a wide range of transparent immersive experiences (including experiences using digital twins of various "things" (e.g., people, places, processes, systems, etc.) where the technology can introduce transparency between people, businesses, and things. This transparent immersive experience may be provided as augmented reality technology, connected home, virtual reality technology, brain-computer interface, human augmentation technology, nanotube electronics, volumetric displays, 4D printing technology, or others.

[0188] The storage system described above can also be used alone or in combination with other computing environments to support a variety of digital platforms. Such digital platforms may include, for example, 5G wireless systems and platforms, digital twin platforms, edge computing platforms, IoT platforms, quantum computing platforms, serverless PaaS, software-defined security, neuromorphic computing platforms, and the like.

[0189] The storage system described above may also be part of a multi-cloud environment, where multiple cloud computing and storage services are deployed in a single heterogeneous architecture. To facilitate the operation of this multi-cloud environment, DevOps tools may be deployed to enable orchestration across clouds. Similarly, continuous development and continuous integration tools may be deployed to standardize processes around continuous integration and delivery, new feature rollouts, and deployment of cloud workloads. By standardizing these processes, a multi-cloud strategy may be implemented that enables the use of the best provider for each workload.

[0190] The storage system described above can be used as part of a platform to enable the use of cryptographic anchors, which can be used to authenticate the origin and content of a product to ensure that it matches the blockchain record associated with the product. Similarly, as part of a tool suite to protect data stored on the storage system, the storage system described above can implement various cryptographic techniques and schemes, including grid cryptography. Grid cryptography can involve the construction of cryptographic primitives involving a grid, either in the construction itself or in security proofs. Unlike public key schemes such as RSA, Diffie-Hellman, or elliptic curve cryptography, which are vulnerable to attack by quantum computers, some grid-based constructions appear to be resistant to attack by both classical and quantum computers.

[0191] A quantum computer is a device that performs quantum computations. Quantum computing uses quantum mechanical phenomena, such as superposition and entanglement, to perform computations. Quantum computers are different from traditional computers based on transistors because such traditional computers require data to be encoded as binary digits (bits), each of which is always in one of two definite states (0 or 1). Unlike traditional computers, quantum computers use qubits that can be in a superposition of states. A quantum computer maintains a sequence of qubits, where a single qubit can represent one, zero, or any quantum superposition of those two qubit states. A pair of qubits can be in any quantum superposition of 4 states, and three qubits are in any superposition of 8 states. A quantum computer with n qubits can typically be in any superposition of up to 2^n different states at the same time, while a traditional computer can only be in one of these states at any one time. A quantum Turing machine is a theoretical model of this computer.

[0192] The storage system described above can also be paired with an FPGA acceleration server as part of a larger AI or ML infrastructure. Such an FPGA acceleration server may reside near the storage system described above (e.g., in the same data center) or even be incorporated into a device including one or more storage systems, one or more FPGA acceleration servers, a networking infrastructure supporting communication between one or more storage systems and one or more FPGA acceleration servers, and other hardware and software components. Alternatively, the FPGA acceleration server may reside in a cloud computing environment that can be used to perform computing-related tasks for AI and ML jobs. Any of the embodiments described above can be used together as an FPGA-based AI or ML platform. The reader will understand that in some embodiments of an FPGA-based AI or ML platform, the FPGA contained in the FPGA acceleration server can be reconfigured for different types of ML models (e.g., LSTM, CNN, GRU). The ability to reconfigure the FPGA contained in the FPGA acceleration server can achieve acceleration of ML or AI applications based on the optimal numerical precision and memory model used. The reader will understand that by viewing a collection of FPGA acceleration servers as an FPGA pool, any CPU in a data center can use the FPGA pool as a shared hardware microservice, rather than limiting the server to a dedicated accelerator inserted therein.

[0193] The FPGA accelerated servers and GPU accelerated servers described above can implement a computing model where, instead of keeping a small amount of data in the CPU and running a long instruction stream through it as happens in a more traditional computing model, the machine learning model and parameters are fixed to a high bandwidth single chip memory where a large amount of data flows through the high bandwidth single chip memory. For this computing model, FPGAs can be even more efficient than GPUs because FPGAs can be programmed with only the instructions needed to run such a computing model.

[0194] The storage system described above can be configured to provide parallel storage, for example, by using a parallel file system such as BeeGFS. Such a parallel file system can include a distributed metadata architecture. For example, a parallel file system can include multiple metadata servers across which metadata is distributed, and components that include services for clients and storage servers.

[0195] The system described above can support the execution of a large number of software applications. Such software applications can be deployed in various ways, including container-based deployment models. Containerized applications can be managed using various tools. For example, containerized applications can be managed using Docker Swarm, Kubernetes, and others. Containerized applications can be used to facilitate serverless, cloud-native computing deployment and management models for software applications. To support serverless, cloud-native computing deployment and management models for software applications, containers can be used as part of an event handling mechanism (e.g., AWS Lambdas) so that various events cause the containerized application to be launched to operate as an event handler.

[0196] The systems described above may be deployed in a variety of ways, including in a manner to support fifth generation ('5G') networks. 5G networks may support substantially faster data communications than previous generations of mobile communications networks, and thus may result in disaggregation of data and computing resources, as modern large-scale data centers may become less prominent and may be replaced, for example, by more local micro data centers near mobile network towers. The systems described above may be included in such local micro data centers, and may be part of or paired with multi-access edge computing ('MEC') systems. Such MEC systems may enable cloud computing capabilities and IT service environments at the edge of cellular networks. By running applications and performing related processing tasks closer to cellular clients, network congestion may be reduced and applications may perform better.

[0197] The storage system described above may also be configured to implement NVMe partitioned namespaces. By using NVMe partitioned namespaces, the logical address space of the namespace is divided into zones. Each zone provides a logical block address range that must be written sequentially and explicitly reset before being rewritten, thereby enabling the creation of natural boundaries that expose the device and offloading the management of internal mapping tables to the host's namespace. In order to implement NVMe partitioned namespaces ('ZNS'), a ZNS SSD or some other form of partitioned block device may be utilized that uses zones to expose the namespace logical address space. In the case of aligning the zones to the internal physical properties of the device, several inefficiencies in data placement may be eliminated. In such embodiments, for example, each zone may be mapped to a separate application so that functions such as wear leveling and obsolete item collection may be performed on a per-zone or per-application basis (rather than across the entire device). In order to support ZNS, the storage controller described herein may be configured to perform wear leveling and obsolete item collection by using, for example, Linux TM Kernel partition block device interface or other tools to interact with partition block devices.

[0198] The storage system described above may also be configured to implement zoned storage in other ways, such as, for example, by using shingled magnetic recording (SMR) storage devices. In instances where zoned storage is used, a device-managed embodiment may be deployed, where the storage device hides this complexity by managing it in firmware, presenting an interface like any other storage device. Alternatively, zoned storage may be implemented via a host-managed embodiment, which relies on the operating system knowing how to handle the drive, and only writing sequentially to certain areas of the drive. Zoned storage may similarly be implemented using a host-aware embodiment, where a combination of drive-managed and host-managed implementations are deployed.

[0199] The storage system described herein may be used to form a data lake. A data lake may be operable as the first place to which an organization's data flows, where such data may be in a raw format. Metadata tags may be implemented to facilitate searching of data elements in a data lake, particularly in embodiments where the data lake contains multiple data stores in formats that may not be easily accessed or read (e.g., unstructured data, semi-structured data, structured data). Data may be downlinked from the data lake to a data warehouse, where the data may be stored in a more processed, packaged, and consumable format. The storage system described above may also be used to implement such a data warehouse. Additionally, a data mart or data hub may allow for even more easily consumable data, where the storage system described above may also be used to provide the underlying storage resources required for the data mart or data hub. In embodiments, querying a data lake may require a schema-on-read approach, where the data is applied to a plan or schema when it is pulled from a storage location, rather than when it enters the storage location.

[0200] The storage systems described herein may also be configured to implement a recovery point objective ('RPO'), which may be established by a user, by an administrator, as a system default, as part of a storage class or service that the storage system participates in delivering, or in some other manner. A "recovery point objective" refers to a target for the maximum time difference between the last update of a source data set and the last recoverable update of a replicated data set that, given some reason, can be correctly recovered from a continuously or frequently updated copy of the source data set. Updates can be correctly recovered if all updates processed on the source data set before the last recoverable update of the replicated data set are properly accounted for.

[0201] In synchronous replication, the RPO will be zero, meaning that under normal operation, all updates done on the source dataset should be present and recoverable correctly on the replica dataset. In as-close-to-synchronous replication, the RPO can be as low as a few seconds. In snapshot-based replication, the RPO can be roughly calculated as the interval between snapshots plus the time to transfer modifications between the previously transferred snapshot and the most recent snapshot to be replicated.

[0202] If updates accumulate faster than they are replicated, then the RPO may be missed. If more data to be replicated accumulates between two snapshots (for snapshot-based replication) than can be replicated between taking a snapshot and replicating the cumulative updates for that snapshot to the replica, then the RPO may be missed. Again in snapshot-based replication, if data to be replicated accumulates at a faster rate than it can be transferred in the time between subsequent snapshots, then replication may begin to fall further behind, which may extend the miss between the expected recovery point objective and the actual recovery point represented by the last correctly replicated update.

[0203] The storage systems described above may also be part of a shared-nothing storage cluster. In a shared-nothing storage cluster, each node of the cluster has local storage and communicates with other nodes in the cluster over a network, where the storage used by the cluster is (usually) provided only by the storage connected to each individual node. A collection of nodes that synchronously replicate data sets may be an instance of a shared-nothing storage cluster because each storage system has local storage and communicates with other storage systems over a network, where those storage systems (usually) do not use storage from elsewhere that they have shared access to through some kind of interconnect. In contrast, some of the storage systems described above are inherently built as shared storage clusters because there are drive racks that are shared by paired controllers. However, other storage systems described above are built as shared-nothing storage clusters because all storage is local to a particular node (e.g., a blade) and all communications are through the network that links the compute nodes together.

[0204] In other embodiments, other forms of shared-nothing storage clusters may include embodiments where any node in the cluster has a local copy of all storage it needs, and where data is mirrored to other nodes in the cluster by synchronous replication to ensure that data is not lost or lost because other nodes are also using that storage. In this embodiment, if a new cluster node needs some data, that data can be copied to the new node from other nodes that have copies of the data.

[0205] In some embodiments, a mirror-based shared storage cluster may store multiple copies of all cluster-stored data, where each subset of the data is replicated to a specific set of nodes, and different subsets of the data are replicated to several different sets of nodes. In some variations, embodiments may store all data stored by the cluster in all nodes, while in other variations, the nodes may be partitioned so that a first set of nodes will all store the same set of data, while a second, different set of nodes will all store a different set of data.

[0206] The reader will appreciate that a RAFT-based database (e.g., etcd) may operate as a shared-nothing storage cluster in which all RAFT nodes store all data. However, the amount of data stored in a RAFT cluster may be limited so that additional copies do not consume too much storage. A cluster of container servers may also be able to replicate all data to all cluster nodes, provided that the containers are not too large and the bulk of their data (the data manipulated by the applications running in the containers) is stored elsewhere, such as in an S3 cluster or an external file server. In this instance, container storage may be provided directly by the cluster through its shared-nothing storage model, with those containers providing images of the execution environments that form part of an application or service.

[0207] To explain further, Figure 3DAn exemplary computing device 350 is illustrated that may be specifically configured to perform one or more of the processes described herein. Figure 3D , computing device 350 may include a communication interface 352, a processor 354, a storage device 356, and an input / output ("I / O") module 358 that are communicatively connected to each other via a communication infrastructure 360. Figure 3D An exemplary computing device 350 is shown in FIG. Figure 3D The components described in the description are not intended to be limiting. In other embodiments, additional or alternative components may be used. Figure 3D Components of computing device 350 are shown in FIG.

[0208] Communication interface 352 may be configured to communicate with one or more computing devices. Examples of communication interface 352 include, without limitation, a wired network interface (such as a network interface card), a wireless network interface (such as a wireless network interface card), a modem, an audio / video connection, and any other suitable interface.

[0209] The processor 354 generally represents any type or form of processing unit capable of processing data and / or interpreting, executing, and / or directing the execution of one or more of the instructions, processes, and / or operations described herein. The processor 354 may perform operations by executing computer-executable instructions 362 (e.g., applications, software, code, and / or other executable data examples) stored in the storage device 356.

[0210] Storage device 356 may include one or more data storage media, devices, or configurations and may employ any type, form, and combination of data storage media and / or devices. For example, storage device 356 may include, but is not limited to, any combination of non-volatile media and / or volatile media described herein. Electronic data, including data described herein, may be temporarily and / or permanently stored in storage device 356. For example, data representing computer executable instructions 362 configured to direct processor 354 to perform any of the operations described herein may be stored within storage device 356. In some examples, the data may be arranged in one or more databases residing within storage device 356.

[0211] The I / O module 358 may include one or more I / O modules configured to receive user input and provide user output. The I / O module 358 may include any hardware, firmware, software, or combination thereof that supports input and output capabilities. For example, the I / O module 358 may include hardware and / or software for capturing user input, including but not limited to a keyboard or keypad, a touch screen component (such as a touch screen display), a receiver (such as an RF or infrared receiver), a motion sensor, and / or one or more input buttons.

[0212] The I / O module 358 may include one or more devices for presenting output to a user, including but not limited to a graphics engine, a display (e.g., a display screen), one or more output drivers (e.g., a display driver), one or more audio speakers, and one or more audio drivers. In some embodiments, the I / O module 358 is configured to provide graphical data to the display for presentation to the user. The graphical data may represent one or more graphical user interfaces and / or any other graphical content that may serve a particular implementation. In some examples, any of the systems, computing devices, and / or other components described herein may be implemented by the computing device 350.

[0213] To explain further, Figure 3E An example of a fleet of storage systems 376 for providing storage services (also referred to herein as 'data services') is illustrated. Figure 3E The fleet of storage systems 376 depicted in FIG. 1 includes a plurality of storage systems 374a, 374b, 374c to 374n that may each be similar to the storage systems described herein. The storage systems 374a, 374b, 374c to 374n in the fleet of storage systems 376 may be embodied as the same storage system or different types of storage systems. For example, Figure 3E The two storage systems 374a, 374n depicted in FIG. 3 are depicted as cloud-based storage systems because the resources that together form each of the storage systems 374a, 374n are provided by different cloud service providers 370, 372. For example, the first cloud service provider 370 may be Amazon AWS. TM , and the second cloud service provider 372 is Microsoft Azure TM , but in other embodiments, one or more public clouds, private clouds, or a combination thereof may be used to provide underlying resources that are used to form a particular storage system in the fleet of storage systems 376.

[0214] According to some embodiments of the present disclosure, Figure 3E The example depicted in 360 includes edge management services 366 for delivering storage services. The storage services delivered (also referred to herein as 'data services') may include, for example, services that provide a specific amount of storage to a customer, services that provide storage to a customer according to a predetermined service level agreement, services that provide storage to a customer according to predetermined regulatory requirements, and many others.

[0215] Figure 3EThe edge management service 366 depicted in FIG. 3 may be embodied as one or more computer program instruction modules executed, for example, on computer hardware (e.g., one or more computer processors). Alternatively, the edge management service 366 may be embodied as one or more computer program instruction modules executed on a virtualized execution environment (e.g., one or more virtual machines), in one or more containers, or in some other manner. In other embodiments, the edge management service 366 may be embodied as a combination of the embodiments described above, including embodiments in which one or more computer program instruction modules contained in the edge management service 366 are distributed across multiple physical or virtual execution environments.

[0216] The edge management service 366 may operate as a gateway for providing storage services to storage customers, where the storage services utilize storage provided by one or more storage systems 374a, 374b, 374c to 374n. For example, the edge management service 366 may be configured to provide storage services to host devices 378a, 378b, 378c, 378d, 378n that execute one or more applications that consume storage services. In this example, the edge management service 366 may operate as a gateway between the host devices 378a, 378b, 378c, 378d, 378n and the storage systems 374a, 374b, 374c to 374n, without requiring the host devices 378a, 378b, 378c, 378d, 378n to directly access the storage systems 374a, 374b, 374c to 374n.

[0217] Figure 3E The edge management service 366 exposes the storage service module 364 to Figure 3E In other embodiments, the edge management service 366 may expose the storage service module 364 to other clients of various storage services. The various storage services may be presented to clients via one or more user interfaces, via one or more APIs, or through some other mechanism provided by the storage service module 364. Thus, Figure 3E The storage service module 364 depicted in the figure may be embodied as one or more computer program instruction modules executed on physical hardware, on a virtualized execution environment, or a combination thereof, wherein the execution of such modules enables customers of the storage service to be provided with, select and access various storage services.

[0218] Figure 3E The edge management service 366 also includes a system management service module 368 . Figure 3EThe system management service module 368 includes one or more computer program instruction modules that, when executed, coordinate with the storage systems 374a, 374b, 374c to 374n to perform various operations to provide storage services to the host devices 378a, 378b, 378c, 378d, 378n. The system management service module 368 may be configured, for example, to perform tasks such as provisioning storage resources from the storage systems 374a, 374b, 374c, to 374n via one or more APIs exposed by the storage systems 374a, 374b, 374c, to 374n, migrating data sets or workloads among the storage systems 374a, 374b, 374c, to 374n via one or more APIs exposed by the storage systems 374a, 374b, 374c, to 374n, setting one or more tunable parameters (i.e., one or more configurable settings) on the storage systems 374a, 374b, 374c, to 374n via one or more APIs exposed by the storage systems 374a, 374b, 374c, to 374n, etc. For example, many of the services described below are related to embodiments in which the storage systems 374a, 374b, 374c, to 374n are configured to operate in a certain manner. In such instances, the system management services module 368 may be responsible for configuring the storage systems 374a, 374b, 374c through 374n to operate in the manner described below using an API (or some other mechanism) provided by the storage systems 374a, 374b, 374c through 374n.

[0219] In addition to configuring the storage systems 374a, 374b, 374c to 374n, the edge management service 366 itself may also be configured to perform various tasks required to provide various storage services. Consider an example in which the storage service includes a service that, when selected and applied, causes personally identifiable information ('PII') contained in a data set to be obfuscated when the data set is accessed. In this example, the storage systems 374a, 374b, 374c to 374n may be configured to obfuscate the PII when servicing read requests directed to the data set. Alternatively, the storage systems 374a, 374b, 374c to 374n may service the read by returning data containing the PII, but the edge management service 366 itself may obfuscate the PII as the data passes through the edge management service 366 on its way from the storage systems 374a, 374b, 374c to 374n to the host devices 378a, 378b, 378c, 378d, 378n.

[0220] Figure 3E The storage systems 374a, 374b, 374c to 374n depicted in the drawings may be embodied as described above with reference to Figures 1A to 3DOne or more of the storage systems described, including variations thereof. In practice, the storage systems 374a, 374b, 374c to 374n may be used as a storage resource pool, wherein the individual components in that pool have different performance characteristics, different storage characteristics, and the like. For example, one of the storage systems 374a may be a cloud-based storage system, another storage system 374b may be a storage system that provides block storage, another storage system 374c may be a storage system that provides file storage, another storage system 374d may be a relatively high-performance storage system, and another storage system 374n may be a relatively low-performance storage system, and the like. In alternative embodiments, there may be only a single storage system.

[0221] Figure 3E The storage systems 374a, 374b, 374c to 374n depicted in FIG3 may also be organized into different failure domains such that a failure of one storage system 374a should be completely unrelated to a failure of another storage system 374b. For example, each of the storage systems may receive power from an independent power system, each of the storage systems may be coupled for data communications via an independent data communications network, and so on. Furthermore, storage systems in a first failure domain may be accessed via a first gateway, while storage systems in a second failure domain may be accessed via a second gateway. For example, the first gateway may be a first instance of the edge management service 366, and the second gateway may be a second instance of the edge management service 366, including embodiments in which each instance is distinct or each instance is part of a distributed edge management service 366.

[0222] As an illustrative example of available storage services, storage services may be presented to a user associated with different levels of data protection. For example, storage services may be presented to a user that, when selected and enforced, assure the user that data associated with that user will be protected such that various recovery point objectives ('RPOs') may be guaranteed. A first available storage service may ensure, for example, that a certain data set associated with a user will be protected such that any data older than 5 seconds may be recovered in the event of a primary data store failure, while a second available storage service may ensure that a data set associated with a user will be protected such that any data older than 5 minutes may be recovered in the event of a primary data store failure.

[0223] Additional instances of storage services that may be presented to a user, selected by the user, and ultimately applied to a data set associated with the user may include one or more data compliance services. Such data compliance services may be embodied as services that, for example, may provide data compliance services to customers (i.e., users) to ensure that the user's data set is managed in a manner to comply with various regulatory requirements. For example, one or more data compliance services may be provided to a user to ensure that the user's data set is managed in a manner to comply with the General Data Protection Regulation ('GDPR'), one or more data compliance services may be provided to a user to ensure that the user's data set is managed in a manner to comply with the Sarbanes-Oxley Act of 2002 ('SOX'), or one or more data compliance services may be provided to a user to ensure that the user's data set is managed in a manner to comply with some other regulatory act. In addition, one or more data compliance services may be provided to a user to ensure that the user's data set is managed in a manner to comply with certain non-governmental guidance (e.g., best practices for auditing purposes), one or more data compliance services may be provided to a user to ensure that the user's data set is managed in a manner to comply with specific client or organizational requirements, and so on.

[0224] In order to provide this particular data compliance service, the data compliance service may be presented to the user (e.g., via a GUI) and selected by the user. In response to receiving a selection of a particular data compliance service, one or more storage service policies may be applied to a data set associated with the user to implement the particular data compliance service. For example, a storage service policy may be applied that requires that a data set be encrypted before being stored in a storage system, before being stored in a cloud environment, or before being stored elsewhere. In order to enforce this policy, provisions may be enforced that require not only that the data set be encrypted when the data set is stored, but also that the data set be encrypted before being transmitted (e.g., sending the data set to another party). In this instance, a storage service policy may also be proposed that requires that any encryption key used to encrypt the data set is not stored on the same system that stores the data set itself. The reader will understand that many other forms of data compliance services may be provided and implemented in accordance with embodiments of the present disclosure.

[0225] The storage systems 374a, 374b, 374c to 374n in the team storage system 376 may be jointly managed by one or more team management modules. The team management module may be Figure 3EThe fleet management module may be part of or separate from the system management service module 368 depicted in FIG. 374. The fleet management module may perform tasks such as monitoring the health of each storage system in the fleet, initiating updates or upgrades on one or more storage systems in the fleet, migrating workloads for load balancing or other performance purposes, and many other tasks. Thus, and for many other reasons, the storage systems 374a, 374b, 374c to 374n may be coupled to each other via one or more data communication links to exchange data between the storage systems 374a, 374b, 374c to 374n.

[0226] In some embodiments, one or more storage systems or one or more elements of a storage system (e.g., features, services, operations, components, etc. of a storage system), such as any of the illustrative storage systems or storage system elements described herein, may be implemented in one or more container systems. A container system may include any system that supports the execution of one or more containerized applications or services. Such a service may be software deployed as infrastructure for building applications, for operating a runtime environment, and / or as infrastructure for other services. In the following discussion, descriptions of containerized applications generally also apply to containerized services.

[0227] A container can combine one or more elements of a containerized software application with a runtime environment for operating those software application elements bundled into a single image. For example, each such container of a containerized application may include executable code and various dependencies, libraries, and / or other components of a software application, as well as network configurations used by elements of the software application within a particular container and configured access to additional resources, in order to implement the operation of those elements. A containerized application may be represented as a collection of such containers, which together represent all elements of the application and various runtime environments required for all those elements to run. Therefore, a containerized application can be abstracted from a host operating system as a combined collection of lightweight and portable packages and configurations, wherein the containerized application can be uniformly deployed and consistently executed in different computing environments using different container-compatible operating systems or different infrastructures. In some embodiments, a containerized application shares a kernel with a host computer system and is executed as an isolated environment (an isolated collection of files and directories, processes, system and network resources, and configured access to additional resources and capabilities) isolated by the host system's operating system in conjunction with a container management framework. When executed, a containerized application can provide one or more containerized workloads and / or services.

[0228] A container system may include and / or utilize a cluster of nodes. For example, a container system may be configured to manage the deployment and execution of containerized applications on one or more nodes in a cluster. Containerized applications may utilize resources of a node, such as memory, processing, and / or storage resources provided and / or accessed by the node. Storage resources may include any of the illustrative storage resources described herein, and may include on-node resources (such as local files and directory trees), off-node resources (such as external networked file systems, databases, or object storage), or both on-node and off-node resources. Access to additional resources and capabilities that may be configured for containers of containerized applications may include specialized computing capabilities, such as GPUs and AI / ML engines, or specialized hardware, such as sensors and cameras.

[0229] In some embodiments, the container system may include a container orchestration system (which may also be referred to as a container orchestrator, a container orchestration platform, etc.) designed to make it reasonably simple and automated for many use cases to deploy, scale, and manage containerized applications. In some embodiments, the container system may include a storage management system configured to provide and manage storage resources (e.g., virtual volumes) for private or shared use by cluster nodes and / or containers of containerized applications.

[0230] Figure 3F An example container system 380 is illustrated. In this example, the container system 380 includes a container storage system 381 that can be configured to perform one or more storage management operations to organize, provide, and manage storage resources for use by one or more containerized applications 382-1 through 382-L of the container system 380. In particular, the container storage system 381 can organize storage resources into one or more storage pools 383 of storage resources for use by the containerized applications 382-1 through 382-L. The container storage system itself can be implemented as a containerized service.

[0231] Container system 380 may include or be implemented by one or more container orchestration systems, including Kubernetes TM 、Mesos TM 、Docker Swarm TM Etc. The container orchestration system may manage the container system 380 running on the cluster 384 through services implemented by a control node (depicted as 385), and may further manage the relationship between the container storage system or individual containers and their storage, memory and CPU limits, networking, and their access to additional resources or services.

[0232] The control plane of the container system 380 may implement services including deploying applications via the controller 386, monitoring applications via the controller 386, providing interfaces via the API server 387, and scheduling deployments via the scheduler 388. In this example, the controller 386, scheduler 388, API server 387, and container storage system 381 are implemented on a single node (node ​​385). In other examples, for resiliency, the control plane may be implemented by multiple redundant nodes, where if a node providing management services for the container system 380 fails, another redundant node may provide management services for the cluster 384.

[0233] The data plane of container system 380 may include a set of nodes that provide a container runtime for executing containerized applications. Individual nodes within cluster 384 may execute a container runtime (e.g., Docker). TM ), and executes a container manager or node agent (such as kubelet in Kubernetes (not depicted)), which communicates with the control plane via a proxy (sometimes called a proxy server) connected to the local network, such as proxy 389. Proxy 389 can use, for example, Internet Protocol (IP) port numbers to route network traffic to and from containers. For example, a containerized application can request a storage class from the control plane, where the request is processed by the container manager, and the container manager transmits the request to the control plane using proxy 389.

[0234] Cluster 384 may include a set of nodes that run containers for managed containerized applications. A node may be a virtual machine or a physical machine. A node may be a host system.

[0235] Container storage system 381 can orchestrate storage resources to provide storage to container system 380. For example, container storage system 381 can provide persistent storage to containerized applications 382-1 to 382-L using storage pool 383. Container storage system 381 itself can be deployed as a containerized application by a container orchestration system.

[0236] For example, a container storage system 381 application may be deployed within a cluster 384 and perform management functions for providing storage to containerized applications 382. Management functions may include determining one or more storage pools from available storage resources, providing virtual volumes on one or more nodes, replicating data, responding to and recovering from host and network failures, or handling storage operations. Storage pool 383 may include storage resources from one or more local or remote sources, where the storage resources may be different types of storage, including (as examples) block storage, file storage, and object storage.

[0237] Container storage system 381 may also be deployed on a set of nodes for which a container orchestration system may provide persistent storage. In some instances, container storage system 381 may be deployed on all nodes in cluster 384 using, for example, a Kubernetes DaemonSet. In this instance, nodes 390-1 to 390-N provide a container runtime executed by container storage system 381. In other instances, some but not all nodes in a cluster may execute container storage system 381.

[0238] The container storage system 381 can handle storage on the node and communicate with the control plane of the container system 380 to provide dynamic volumes, including persistent volumes. Persistent volumes can be mounted on the node as virtual volumes, such as virtual volumes 391-1 and 391-P. After the virtual volume 391 is mounted, the containerized application can request and use or be otherwise configured to use the storage provided by the virtual volume 391. In this example, the container storage system 381 can install a driver on the kernel of the node, where the driver handles storage operations directed to the virtual volume. In this example, the driver can receive storage operations directed to the virtual volume, and in response, the driver can perform storage operations on one or more storage resources within the storage pool 383 under the guidance of or using additional logic within the container that implements the container storage system 381 as a containerized service.

[0239] The container storage system 381 may determine available storage resources in response to being deployed as a containerized service. For example, storage resources 392-1 to 392-M may include local storage, remote storage (storage on a separate node in the cluster), or both local and remote storage. Storage resources may also include storage from external sources, such as various combinations of block storage systems, file storage systems, and object storage systems. Storage resources 392-1 to 392-M may include any type and / or configuration of storage resources (such as any of the illustrative storage resources described above), and the container storage system 381 may be configured to determine available storage resources in any suitable manner (including based on a configuration file). For example, a configuration file may specify account and authentication information for a cloud-based object storage device 348 or for a cloud-based storage system 318. The container storage system 381 may also determine the availability of one or more storage devices 356 or one or more storage systems. The total amount of storage from one or more of the storage device 356, the storage system, the cloud-based storage system 318, the edge management service 366, the cloud-based object storage device 348, or any other storage resource, or any combination or sub-combination of such storage resources, can be used to provide a storage pool 383. The storage pool 383 is used to provide storage for one or more virtual volumes mounted on one or more nodes 390 within the cluster 384.

[0240] In some embodiments, the container storage system 381 can create multiple storage pools. For example, the container storage system 381 can aggregate storage resources of the same type into individual storage pools. In this example, the storage type can be one of the following: storage device 356, storage array 102, cloud-based storage system 318, storage via edge management service 366, or cloud-based object storage device 348. Or it can be storage configured with a certain level or type of redundancy or distribution, such as a specific combination of striping, mirroring, or erasure coding.

[0241] The container storage system 381 can be executed as a containerized container storage system service within a cluster 384, where instances of containers implementing elements of the containerized container storage system service can operate on different nodes within the cluster 384. In this example, the containerized container storage system service can be combined with the container orchestration system operations of the container system 380 to handle storage operations, mount virtual volumes to provide storage to nodes, aggregate available storage into a storage pool 383, provide storage for virtual volumes from a storage pool 383, generate backup data, replicate data between nodes, clusters, environments, and other storage system operations. In some examples, the containerized container storage system service can provide storage services across multiple clusters operating in different computing environments. For example, other storage system operations may include the storage system operations described herein. The persistent storage provided by the containerized container storage system service can be used to implement stateful and / or elastic containerized applications.

[0242] The container storage system 381 may be configured to perform any suitable storage operation of a storage system. For example, the container storage system 381 may be configured to perform one or more of the illustrative storage management operations described herein to manage storage resources used by the container system.

[0243] Figure 4 4 is a block diagram illustrating an example storage system 400 according to some embodiments of the present disclosure. Storage system 400 may be used to store data and / or provide access to data to clients / computing devices, as described above. Figure 4 , storage system 400 includes storage clusters 407A, 407B, and 407C. A storage cluster may be a group of storage nodes (e.g., one or more chassis, one or more racks, etc.). Storage clusters 407A, 407B, and 407C may be communicatively coupled to each other via a network 705 (e.g., one or more of a LAN, a WAN, a wireless network, a wired network, the Internet, etc.). Figure 4Storage clusters 407A, 407B, and 407C are illustrated as communicating via network 705, but storage clusters 407A, 407B, and 407C, in other embodiments, some of storage clusters 407A, 407B, and 407C may be directly coupled to each other via communication bridges, interconnects, buses, wires, etc. For example, storage cluster 407A may be directly coupled to storage cluster 407B. In another example, storage cluster 407A may be directly coupled to storage cluster 407B and coupled to storage cluster 407B via network 705.

[0244] Storage clusters 407A to 407C include different types of storage nodes and / or may include any number of storage nodes. For example, storage cluster 407A may include a group of primary storage nodes 410 (e.g., one or more primary storage nodes 410). Storage cluster 407B may include a group of secondary storage nodes (e.g., one or more secondary storage nodes 420). Storage cluster 407C may include both a group of primary storage nodes 410 and a group of secondary storage nodes 420. Primary storage node 410 may be referred to as head node, head storage node, control node, control storage node, primary node, primary storage node, master node, etc. Secondary storage node 420 may be referred to as slave node, slave storage node, secondary node, extension node, extension storage node, etc.

[0245] In one embodiment, the primary storage node 410 includes one or more processing devices 411 (e.g., one or more CPUs, ASICs, FPGAs, multi-core processors, processing cores, etc.). The processing device 411 may be used by the primary storage node 410 to perform various operations, actions, functions, etc., as discussed in more detail below. The processing device 411 may be referred to as a primary processing device. The primary storage node 410 may also include one or more non-volatile memory modules 413. The non-volatile memory module 413 may be a device that stores data so that the data in the device is retained even when power is no longer supplied to the device. Examples of the non-volatile memory module 413 may include, but are not limited to, an SSD, an NVME drive, a flash drive, etc.

[0246] In one embodiment, the secondary storage node 420 includes a processing device 421 (e.g., one or more CPUs, ASICs, FPGAs, multi-core processors, processing cores, etc.). The processing device 421 can be used by the secondary storage node 420 to perform various operations, actions, functions, etc., as discussed in more detail below. The processing device 421 can be referred to as a secondary processing device. The secondary storage node 420 can also include one or more non-volatile memory modules 423 (e.g., SSDs, NVME drives, flash drives, etc.).

[0247] In an embodiment, a processing device of a storage node (e.g., processing devices 411 and 421) may directly determine how erase blocks of a flash-based storage device (e.g., non-volatile memory modules 413 and 423) of the storage node are allocated, written, and when erased. In some embodiments, the processing device may configure a write operation to operate on a subset of flash die of the flash-based storage device during a time span to maintain the availability of most of the die of the flash-based storage device for performing read operations.

[0248] In some embodiments, the nonvolatile memory modules 413 and / or 423 may be removed from the primary storage node 410 and / or the secondary storage node 420. For example, the nonvolatile memory modules 413 and / or 423 may be removed, relocated and / or replaced with other nonvolatile memory modules. In another example, the nonvolatile memory modules 413 and / or 423 may be moved from one storage node to another storage node (e.g., may be removed from the primary storage node 410 and then inserted into the secondary storage node 420, from the secondary storage node 420 to the primary storage node 410, from the first primary storage node 410 to the second primary storage node 410, from the first secondary storage node 420 to the second secondary storage node 420, etc.). The nonvolatile memory modules 413 and / or 423 may also be removable or insertable when the primary storage node 410 and / or the secondary storage node 420 are in operation. For example, the non-volatile memory modules 413 and / or 423 may be hot-swappable. The non-volatile memory modules 413 and / or 423 may also be heterogeneous (e.g., non-uniform). For example, there may be different brands, models, manufacturers, capacities, supported command sets, memory types (e.g., SLC, MLC, TLC, QLC, PLC, etc.) of the non-volatile memory modules 413 and / or 423.

[0249] In one embodiment, when non-volatile memory modules 413 and / or 423 are removed, relocated, and / or replaced, the data storage on non-volatile memory modules 413 and / or 423 may be reincorporated, reused, and / or reaccessible.

[0250] In one embodiment, non-volatile memory modules 423 (e.g., some or all of non-volatile memory modules 423) may have lower performance when compared to non-volatile memory modules 413. For example, non-volatile memory modules 423 may have higher / longer access times than non-volatile memory modules 413. In another example, non-volatile memory modules 423 may have less bandwidth / throughput (e.g., read throughput and / or write throughput) when compared to non-volatile memory modules 423. The network interconnect to the secondary cluster itself may also result in lower storage system performance from its non-volatile memory modules compared to the storage non-volatile memory modules in the primary storage node.

[0251] In some embodiments, the primary storage node 410 may operate as a controller / manager of the storage system. For example, the primary storage node 410 may determine which storage operations should be performed, and may instruct one or more secondary storage nodes to perform storage operations (e.g., a rebuild / reconstruction operation for rebuilding data, a compression operation for compressing data, a discarded item collection operation for releasing a data block, etc.). In another example, the primary storage node 410 may delegate / offload storage operations to the secondary storage node 420. For example, the authorization 517 may manage / control a portion of the data and / or a subset of the non-volatile memory module 423 of the secondary storage node 420. The authorization 517 may offload / delegate storage operations related or associated with a portion of the data and / or a subset of the non-volatile memory module 423 to the secondary storage node 420.

[0252] In one embodiment, when writing memory to non-volatile memory modules 413 and / or 423, one or more of primary storage node 410 and secondary storage node 420 may use different write paths. A write path may refer to a component, memory, circuit that data may pass through when storing / accessing data. For example, primary storage node 410 may receive data from a client / computing device, and may first store the data in NVRAM, and then write the data to non-volatile memory module 413. One example write path may be to write data directly from NVRAM to MLC flash memory (e.g., TLC memory, QLC memory, five-level cell (PLC) memory). Another example write path may be to write data from NVRAM to SLC flash memory, and then to MLC flash memory. Another example write path may include writing data directly to MLC flash memory (e.g., bypassing NVRAM).

[0253] In one embodiment, the primary storage node 410 may determine that the programming mode of one or more portions of the nonvolatile memory modules 413 and / or 423 should be changed. For example, a portion of the nonvolatile memory module 413 may use the MLC programming mode (e.g., the nonvolatile memory module 413 may include MLC cells operating in the MLC mode). The primary storage node 410 may determine that a portion of the nonvolatile memory module 413 uses a different programming mode (e.g., the MLC cells should operate in the SLC mode). The primary storage node 410 may change the programming mode of different portions of the nonvolatile memory modules 413 and / or 423 based on various parameters. For example, the primary storage node 410 may change different portions of the nonvolatile memory modules 413 and / or 423 to the SLC mode and back to the QLC mode based on the program / erase cycles of the cells in the nonvolatile memory modules 413 and / or 423.

[0254] In one embodiment, processing device 421 may have less computing power (e.g., less processing power) than processing device 411. For example, processing device 421 may have a lower clock frequency than processing device 411. In another example, processing device 421 may have fewer processing cores than processing device 411. In another example, processing device 421 may have less memory (e.g., fewer caches, fewer memory registers, etc.) than processing device 411. In another example, processing device 421 may be able to perform fewer operations / computations during a time period when compared to processing device 411.

[0255] In some embodiments, processing device 421 may be different from processing device 411. For example, processing device 421 may be a processor of a different brand, model, etc. than processing device 411. In other embodiments, processing device 421 may be the same processor (e.g., same brand, model, etc.) as processing device 411. However, processing device 421 may operate at a lower clock speed and / or may use less energy / power when compared to processing device 411.

[0256] In some embodiments, the primary storage node 410 and / or the secondary storage node 420 may include other components, devices, circuits, etc. to perform various other actions, operations, functions, tasks, etc. For example, the primary storage node 410 and / or the secondary storage node 420 may include a graphics processing unit (GPU), an add-in board (e.g., a circuit board, a printed circuit board, etc. that can be used to add additional non-volatile memory modules to the storage node), peripheral components, etc. These other components may be hot-swappable, modular, configurable, etc., and may be used to add capabilities to the storage node.

[0257] In one embodiment, the primary storage node 410 may include one or more authorizations ( Figure 4 4). Authorizations may control how and where data is stored in the non-volatile memory modules 413 and 423 of the storage system 400. For example, authorizations may determine how segments, erase blocks, allocation units, etc. are created and used. In another example, authorizations may determine how stripes (e.g., RAID stripes), parity data, etc. are created. Each primary storage node 410 may include any number of authorizations. For example, a primary storage node 410 may include one authorization, ten authorizations, or any appropriate number of authorizations. Each authorization may be responsible for managing a group of blocks / segments (e.g., a series of blocks / segments), one or more non-volatile memory modules 413 and / or 423. Authorizations may be executed in conjunction with the above. Figures 2A to 2G The functions, operations, actions, tasks, etc. discussed.

[0258] In one embodiment, if the second primary storage node 410 fails, the first primary storage node 410 may be able to continue or take over the operations, functional actions, tasks, etc. of the second primary storage node 410. For example, if the second primary storage node 410 crashes, resets / restarts, or is otherwise inoperable, the first primary storage node 410 may be able to continue or take over the operations, functional actions, tasks, etc. of the second primary storage node 410. In addition, the operations, functional actions, tasks, etc. of the second primary storage node 410 may be distributed to multiple other primary storage nodes 410 (e.g., assigned to multiple other primary storage nodes 410, dispersed among multiple other primary storage nodes 410). For example, two, ten, or some other appropriate number of primary storage nodes 410 may each take over a portion of the operations, functional actions, tasks, etc. of the second primary storage node 410.

[0259] In one embodiment, authorizations may be transferred, moved, reallocated, reassigned, etc. from one primary storage node 410 to another primary storage node 410. For example, authorized data structures, metadata, procedures, services, etc. may be replicated from one primary storage node to another primary storage node. In another example, authorized data structures, metadata, procedures, services, etc. may be replicated from one primary storage node 410 to a secondary storage node 420 (e.g., when a secondary storage node 420 is converted to a primary storage node, as discussed above).

[0260] In one embodiment, the secondary storage node 420 may initially lack authorization. For example, when the secondary storage node 420 initiates operation (e.g., initially boots up or starts up), no authorization may exist on the secondary storage node 420 (e.g., the secondary storage node 420 may not have any authorization). The secondary storage node 420 may include one or more authorizations after being converted to the primary storage node 410.

[0261] In one embodiment, the secondary storage nodes 420 may be converted to primary storage nodes (e.g., may operate as primary storage nodes). For example, if a primary storage node 410 fails, one or more secondary storage nodes 420 may be used to perform operations, functional actions, tasks, etc. of the failed primary storage node 410. The conversion from a secondary storage node to a primary storage node may be temporary. For example, a secondary primary node 420 may operate as a primary storage node for a period of time until the failed primary storage node 410 recovers or until a new primary storage node 410 is added to the storage system.

[0262] In one embodiment, one or more device hosts ( Figure 4 The secondary storage node 420 may include one or more device hosts. The device host may be hardware, software (e.g., service, process, thread, daemon, driver, etc.), firmware, or a combination thereof that can manage, control, etc., one or more non-volatile memory modules 423. The device host may be described in more detail below.

[0263] In various embodiments, the storage system 400 allows for dynamic configuration of primary storage nodes 410 and secondary storage nodes 420. For example, primary storage nodes 410 and / or secondary processing storage devices 420 may be added and / or removed from a storage cluster. In addition, non-volatile memory modules may also be removable and replaceable, further increasing the configurability of the storage system. Because the primary storage node 410 may control / manage the operation of the storage system 400 (e.g., storage operations), more secondary storage nodes 420 may be used in the storage system. The secondary storage node 420 may include a non-volatile memory module with less processing power and / or lower performance. This may allow the storage system 400 to operate more efficiently (e.g., using less power and at a lower cost) when providing access to data. The secondary storage node 420 also allows for more efficient increases in the storage capacity of the storage system (e.g., using less power and at a lower cost).

[0264] In some embodiments, one or more storage operations (including one or more of the illustrative storage management operations described herein) may be containerized. For example, one or more storage operations may be implemented as one or more containerized applications configured to be executed to perform the storage operations. Such containerized storage operations may be executed in any suitable runtime environment to manage any storage system, including any of the illustrative storage systems described herein.

[0265] The storage system described herein may support various forms of data replication. For example, two or more storage systems may synchronously replicate a data set between each other. In synchronous replication, different copies of a particular data set may be maintained by multiple storage systems, but all accesses (e.g., reads) to the data set should produce consistent results regardless of which storage system the access is directed to. For example, reads directed to any of the storage systems that are synchronously replicating the data set should return the same results. Thus, although updates to data set versions do not need to occur completely simultaneously, precautions must be taken to ensure consistent access to the data set. For example, if an update (e.g., a write) directed to a data set is received by a first storage system, the update can only be confirmed to be complete if all storage systems that are synchronously replicating the data set have applied the update to their copies of the data set. In this example, synchronous replication can be implemented by using I / O forwarding (e.g., a write received at a first storage system is forwarded to a second storage system), communication between storage systems (e.g., each storage system indicates that it has completed the update), or in other ways.

[0266] In other embodiments, a data set may be replicated by using checkpoints. In checkpoint-based replication (also known as 'almost synchronous replication'), a set of updates to a data set (e.g., one or more write operations directed to the data set) may occur between different checkpoints, such that the data set is updated to a particular checkpoint only when all updates to the data set prior to the particular checkpoint have been completed. Consider an example in which a first storage system stores a real-time copy of a data set that is being accessed by a data set user. In this example, assume that the data set is replicated from the first storage system to a second storage system using checkpoint-based replication. For example, the first storage system may send a first checkpoint (at time t=0) to the second storage system, followed by a first set of updates to the data set, followed by a second checkpoint (at time t=1), followed by a second set of updates to the data set, followed by a third checkpoint (at time t=2). In this example, if the second storage system has executed all updates in the first set of updates but has not yet executed all updates in the second set of updates, then the copy of the data set stored on the second storage system may be up to date up to the second checkpoint. Alternatively, if the second storage system has performed the first set of updates and all updates in the second set of updates, then the copy of the data set stored on the second storage system may be up to date until the third checkpoint. The reader will recognize that various types of checkpoints may be used (e.g., metadata-only checkpoints), that checkpoints may be distributed based on various factors (e.g., time, number of operations, RPO settings), etc.

[0267] In other embodiments, the data set may be replicated via snapshot-based replication (also referred to as 'asynchronous replication'). In snapshot-based replication, a snapshot of the data set may be sent from a replication source (e.g., a first storage system) to a replication target (e.g., a second storage system). In this embodiment, each snapshot may include the entire data set or a subset of the data set, such as (for example) only the portion of the data set that has changed since the last snapshot was sent from the replication source to the replication target. The reader will appreciate that snapshots may be sent on demand based on a policy that takes various factors (e.g., time, number of operations, RPO settings) into account, or in some other manner.

[0268] The storage systems described above may be configured, alone or in combination, to be used as continuous data protection storage. Continuous data protection storage is a feature of a storage system that records updates to a data set in such a way that a consistent image of the previous contents of the data set can be accessed at a low temporal granularity (usually on the order of seconds or even less), and extended back for a reasonable period of time (usually hours or days). These allow access to the most recent consistent point in time of the data set, and also allow access to a point in time of the data set where an event may have just occurred (e.g., causing part of the data set to be damaged or otherwise lost), while keeping close to the maximum number of updates before that event. Conceptually, they are like a sequence of snapshots of a data set that are taken very frequently and kept for a long period of time, but continuous data protection storage is typically implemented quite differently from snapshots. A storage system that implements data continuous data protection storage may also further provide a means of accessing these points in time, accessing one or more of these points in time as a snapshot or clone copy, or restoring the data set back to one of these recorded points in time.

[0269] Over time, to reduce overhead, some of the time points maintained in the continuous data protection storage may be merged with other nearby time points, essentially removing some of these time points from the storage. This may reduce the capacity required for storage updates. A limited number of these time points may also be converted into longer duration snapshots. For example, this storage may maintain a low-granularity sequence of time points dating back a few hours from the present, with some time points merged or deleted to reduce overhead by up to an additional day. Going back further into the past, some of these time points may be converted to snapshots representing consistent time point images from only every few hours.

[0270] Although some embodiments are primarily described in the context of storage systems, readers in the art will recognize that embodiments of the present disclosure may also take the form of a computer program product disposed on a computer-readable storage medium for use with any suitable processing system. Such computer-readable storage media may be any storage medium for machine-readable information, including magnetic media, optical media, solid-state media, or other suitable media. Examples of such media include magnetic disks in hard drives or floppy disks, optical disks in optical drives, magnetic tapes, and others as will occur to those skilled in the art. Those skilled in the art will immediately recognize that any computer system with suitable programming components will be able to perform the steps described herein as embodied in a computer program product. Those skilled in the art will also recognize that although some embodiments described in this specification are directed to software installed and executed on computer hardware, alternative embodiments implemented as firmware or hardware are also within the scope of the present disclosure.

[0271] In some examples, a non-transitory computer-readable medium storing computer-readable instructions may be provided according to the principles described herein. When executed by a processor of a computing device, the instructions may direct the processor and / or the computing device to perform one or more operations, including one or more of the operations described herein. Any of a variety of known computer-readable media may be used to store and / or transmit such instructions.

[0272] The non-transitory computer-readable media referred to herein may include any non-transitory storage media that participates in providing data (e.g., instructions) that can be read and / or executed by a computing device (e.g., by a processor of a computing device). For example, non-transitory computer-readable media may include, but are not limited to, any combination of non-volatile storage media and / or volatile memory media. Exemplary non-volatile storage media include, but are not limited to, read-only memory, flash memory, solid-state drives, magnetic storage devices (e.g., hard disks, floppy disks, magnetic tapes, etc.), ferroelectric random access memory ("RAM"), and optical disks (e.g., compact disks, digital video disks, Blu-ray disks, etc.). Exemplary volatile storage media include, but are not limited to, RAM (e.g., dynamic RAM).

[0273] The advantages and features of the present disclosure can be further described by the following statements:

[0274] 1. A storage device configured to: receive a read request with a high priority indication; determine whether an ongoing flash memory programming operation will delay processing of the read request by a threshold amount of time; and in response to determining that the ongoing flash memory programming operation will delay processing of the read request by the threshold amount of time, interrupt the ongoing flash memory programming operation, wherein the storage device will process the read request after interrupting the ongoing flash memory programming operation.

[0275] 2. The storage device of statement 1 wherein the ongoing flash memory programming operation is a write operation to an erase block programmed to store at least three bits per cell.

[0276] 3. The storage device of statement 1 wherein the ongoing flash memory programming operation is an erase operation.

[0277] 4. The storage device of statement 1 wherein the ongoing flash programming operation is performed internally within the storage device as part of internal maintenance.

[0278] 5. The storage device of statement 1, wherein in response to determining that the ongoing flash programming operation cannot be interrupted, the storage device is further configured to fail the read request to indicate that the read request cannot be processed within the threshold amount of time.

[0279] 6. The storage device of statement 1 further configured to: receive one or more subsequent read requests; and process the read request and the one or more subsequent read requests during the interruption.

[0280] 7. The storage device of statement 1 further configured to: process the read request; and continue the ongoing flash programming operation after processing the read request is complete.

[0281] 8. The storage device of statement 1, wherein the storage device is a direct mapped storage device.

[0282] 9. The storage device of statement 1, wherein the storage device supports partitioned namespaces.

[0283] 10. The storage device of statement 1, wherein the storage device supports an abstraction that organizes a virtual address space of the storage device such that virtual segments have a size that enables the virtual segments to be written sequentially within the same erase block of the storage device.

[0284] 11. A storage device configured to: receive a first request to program a first erase block in a first programming mode; receive a second request to program a second erase block in a second programming mode, wherein the second programming mode is different from the first programming mode; receive a third request to store first data in the first erase block; receive a fourth request to store second data in the second erase block; store the first data in the first erase block using the first programming mode; and store the second data in the second erase block using the second programming mode.

[0285] 12. The storage device of statement 11, wherein the storage device is a direct mapped storage device.

[0286] 13. The storage device of statement 11, wherein the storage device supports partitioned namespaces.

[0287] 14. The storage device of statement 11, wherein the storage device supports an abstraction that organizes a virtual address space of the storage device such that virtual segments have a size that enables the virtual segments to be written sequentially within the same erase block of the storage device.

[0288] 15. A storage device comprising a plurality of erase blocks, the storage device being configured to: provide a plurality of available cells through an interface, each cell representing a set of the erase blocks programmable in a plurality of modes, each erase block having a set of characteristics including a different effective capacity; receive a first request to form a first cell set, the first cell set comprising a first number of the available cells and configured to be programmed in a first mode of the plurality of flash modes, resulting in a first virtual addressable capacity, the first virtual addressable capacity corresponding to a first effective capacity for the first number of the available cells when programmed in the first mode; receive a second request to form a second cell set of at least a second number of the available cells , and is configured to be programmed in a second mode of the plurality of flash modes, resulting in a second virtual addressable capacity, the second virtual addressable capacity corresponding to a second effective capacity for the second number of the available cells when programmed in the second mode; receiving a third request to store first data to a first virtual address associated with the first cell set; receiving a fourth request to store second data to a second virtual address associated with the second cell set; storing the first data in an erase block of one or more cells of the first cell set and programming in the first mode; and storing the second data in an erase block of one or more cells of the second cell set and programming in the second mode.

[0289] 16. The storage device of statement 15, wherein each of the available cells represents a programming mode applied to one or more flash memory dies.

[0290] 17. A storage device according to statement 15, wherein each of the available cells can be operated in parallel with every other cell, and wherein read, erase, write and maintenance operations of a first cell in the available cells do not prevent parallel read, erase, write or storage device background operations of a second cell in the available cells.

[0291] 18. The storage device of statement 15 wherein a request to the storage device prevents and subsequently allows execution of storage device background operations for particular ones of the available units.

[0292] 19. The memory device of statement 15 wherein the cells can be programmed with additional internal redundancy to increase their internal reliability.

[0293] 20. The storage device of statement 15 wherein a set of cells is programmable by the storage device controller to store data segments across the flash dies in the set using an erasure code.

[0294] 21. A storage system comprising: a plurality of storage nodes in a chassis, wherein each of the plurality of storage nodes comprises a processing device, a memory, and a plurality of slots for hosting a flash-based storage device, wherein each of the flash-based storage devices provides an interface, the interface being configured to allow the plurality of storage nodes to optimize the use of flash memory on the flash-based storage device, wherein the processing device of each of the plurality of storage nodes is configured to: receive read requests and write requests for data of the storage system; direct the read requests to a first group of storage nodes among the plurality of storage nodes, the first group of storage nodes hosting one or more flash-based storage devices storing data for the read requests; store the write data in erasure coded segments distributed in shards across a group of the flash-based storage devices hosted by the plurality of storage nodes, wherein a shard pointing to a specific flash-based storage device is delivered to and written by a specific storage node hosting the specific flash-based storage device; and balance write activity to independent performance areas of the flash memory of the flash-based storage device to ensure that the read request is processed within a threshold amount of time.

[0295] 22. The storage system of statement 21 wherein the processing device of each plurality of storage nodes is configured to: directly determine how erase blocks of the flash-based storage device are allocated, written to, and when erased.

[0296] 23. A storage system according to statement 22, wherein write operations are configured by the plurality of storage nodes to operate on a subset of the flash memory dies of the flash-based storage device during a time span to maintain the availability of a majority of the dies of the flash-based storage device for performing read operations.

[0297] 24. A storage system according to statement 21, wherein the plurality of storage nodes are configured to program a first number of erase blocks of the flash-based storage device in a single-level cell (SLC) mode and to program a second number of erase blocks of the flash-based storage device with at least 3 bits per cell.

[0298] 25. The storage system of statement 21 wherein the plurality of storage nodes are configured to configure a first subset of cells of a configurable flash-based storage device as SLC and to configure a second subset of cells of the configurable flash-based storage device as at least three bits per cell.

[0299] 26. The storage system of statement 21 wherein write operations are configured by the plurality of storage nodes to operate on a subset of the configuration cells of the flash-based storage device during a time span.

[0300] 27. The storage system of statement 21, wherein an internal read request associated with a front-end read request is issued to the flash-based storage device as a high priority read that interrupts a conflicting QLC modification operation that delays the internal read request.

[0301] 28. A method comprising any one of statements 1 to 27.

[0302] 29. A non-transitory computer-readable storage medium storing instructions that, when executed by a processing device, cause the processing device to perform any of Statements 1-27.

[0303] One or more embodiments may be described herein with the help of method steps that illustrate the performance of specified functions and their relationships. The boundaries and sequences of these functional building blocks and method steps are arbitrarily defined herein for ease of description. As long as the specified functions and relationships are properly performed, alternative boundaries and sequences may be defined. Any such alternative boundaries or sequences are therefore within the scope and spirit of the claims. In addition, the boundaries of these functional building blocks are arbitrarily defined for ease of description. As long as specific important functions are properly performed, alternative boundaries may be defined. Similarly, flowchart blocks are also arbitrarily defined herein to illustrate specific important functionality.

[0304] To the extent used, the flow diagram block boundaries and sequences may be defined in other ways and still perform the particular significant functionality. Such alternatives to both the functional building blocks and flow diagram blocks and sequences are therefore defined within the scope and spirit of the claims. Those skilled in the art will also recognize that the functional building blocks and other illustrative blocks, modules, and components herein may be implemented as described, or by discrete components, application specific integrated circuits, processors executing appropriate software, etc., or any combination thereof.

[0305] Although specific combinations of various functions and features of one or more embodiments have been explicitly described herein, other combinations of these features and functions are also possible. The present disclosure is not limited to the specific examples disclosed herein and explicitly incorporates these other combinations.

[0306] In an embodiment, a storage system may include a die-aware scheduler (hereinafter also referred to as a "scheduler") located between an authorization (e.g., authorization 168) of the storage system and one or more storage devices. The scheduler may be responsible for managing the flow of input-output (I / O) operations, such as read, write, and erase operations, flowing between the authorization and the storage devices. For example, the scheduler may receive an I / O operation from the authorization and assign the I / O operation to one or more storage devices of the storage system. The scheduler may receive a hint about the nature and importance of the operation from the authorization and receive constraints that need to be processed from the storage device. Different types of operations performed by the storage system may take different amounts of time to complete. For example, a write operation that stores data at a storage device may take longer to complete than a read operation that reads data previously stored at the storage device. In addition, different types of operations may have different priorities. For example, a read operation that reads data from a storage device may have a higher priority than a write operation that stores data at the storage device.

[0307] In a conventional storage system, once a write operation is initiated to store data at one storage device of the storage system, subsequent operations are delayed until the storage device completes the write operation. For example, a user may submit a request to read data from a storage device of the storage system. However, the storage device is currently performing a write operation and cannot read the data until the write operation is complete. This may result in unnecessary delays in providing data to users of the storage system.

[0308] Turning now to an embodiment of a storage system that intelligently schedules operations to perform a read operation to read data from a storage device while the storage device is performing a write operation. In some embodiments, a write operation to store data at a storage device may include multiple phases. For example, a write operation programmed in a quad-level cell (QLC) mode may include five phases to store data at the storage device. In this embodiment, one or more read operations may be performed between two phases. For example, after a first phase of a multi-phase write operation, a read operation may be performed to read data from the storage device before a second phase of the multi-phase write operation begins.

[0309] In an embodiment, the scheduler may interrupt (also referred to hereinafter as "pause") a write operation to enable the storage device to perform one or more read operations to read data from the storage device. For example, after receiving one or more requests to read data from the storage device, the scheduler may assign a read operation to the storage device and transmit an indication that the storage device will pause the ongoing write operation, perform the read operation, and then resume the write operation once the read operation has completed.

[0310] Therefore, embodiments of the present disclosure provide an improved storage system by performing a read operation during a write operation. Performing a read operation during a write operation, rather than waiting until the write operation is completed, reduces latency and improves the performance of the storage system.

[0311] It should be noted that the use of read operations performed during write operations in the following examples is shown for illustrative purposes only. Embodiments of the present disclosure may have other types of operations performed during write operations of storage devices of a storage system. In addition, in some embodiments, operations may be performed during erase operations rather than write operations.

[0312] Figure 5A is an illustration of an example in which a scheduler of a storage system 500 according to an embodiment of the present disclosure receives a request to read data from a storage device of the storage system. In an embodiment, the storage system 500 may correspond to the previously described Figures 1A to 4 The storage system 500 includes a scheduler 388 and storage devices 356a to n. The storage devices 356a to n represent any number of storage devices of the storage system 500. In some embodiments, the storage devices 356a to n may be Figure 3F . The storage devices 356a to n include corresponding data 504a to n and queues 506a to n. It should be noted that the data 504a to n and queues 506a to n are shown for illustrative purposes only and are not physical components of the storage devices 356a to n.

[0313] In an embodiment, the storage devices 356a to n may be solid-state storage devices capable of suspending operations. The storage devices 356a to n may provide an interface that the scheduler 388 may use to transmit high priority operations such as read operations, and indications that the storage device is to suspend an ongoing write operation to perform a read operation or to perform a read operation between stages of a multi-stage write operation, as will be described in further detail below.

[0314] In an embodiment, the interface may allow different priorities to be provided to data that trigger different actions of the storage devices 356a-n. For example, a request with an "immediate" priority may trigger preemption or interruption of an ongoing write operation. In some embodiments, the immediate priority indication may further mean that if there is an ongoing flash operation that does not complete quickly and cannot be interrupted, then the read fails immediately and indicates that the read cannot be completed in time.

[0315] In another example, a request with a "medium" priority may cause a certain amount of time to be waited in the expectation that multiple read requests may be received, which may be grouped together in some way that is beneficial to the operation of the storage device (e.g., by reducing the number of interrupted ongoing flash operations). In another example, a request with an even lower priority (e.g., a "background" priority) may indicate that the operation may be scheduled after any ongoing flash operations are completed. In some embodiments, the background priorities may be further divided into subsets. For example, a "throughput" priority indication may cause the storage device to ensure reasonable progress across a set of "throughput" priority requests (but where individual operation latencies may be high if this helps the device) and "idle" priorities, where operations may be postponed until an appropriate time without having to worry too much about making reasonable progress across all "idle" requests.

[0316] The storage system 500 can utilize these different priority indications to intelligently schedule operations to be performed by the storage devices 356a to n. During operation of the storage system 500, there is an almost constant flow of background read operations, whether from data refresh, obsolete item collection, or space adjustment. In order to reduce front-end read latency, the scheduler 388 of the storage system 500 can utilize these priorities to distinguish front-end operations while ensuring that back-end operations are not exhausted.

[0317] For example, the storage system 500 may utilize preemptive reads for high priority reads (e.g., when alternative content sources are unavailable), and immediate priority for the most latency-sensitive reads, and medium priority for most other reads. In another example, the storage system 500 may utilize throughput priority for latency-insensitive background tasks where forward progress is still required for the storage system 500 service to continue operating (e.g., waste collection, particularly if free blocks are not otherwise accumulating fast enough to keep up with incoming writes), and idle priority for latency-insensitive background tasks where high throughput is less of a concern (e.g., cleanup).

[0318] In an embodiment, immediate priority may also be used for reads that should be performed immediately (if possible), and if a read is not possible due to an ongoing, uninterruptible flash operation, then a failure indicates an abort. In some embodiments, a failure may trigger an alternative means of reading the data.

[0319] Furthermore, in storage system 500, the throughput of single-level cell (SLC) write operations should be less than or equal to the throughput of quad-level cell (QLC) write operations. This is because the SLC write throughput represents incoming user data, while the QLC write throughput represents writing SLC data onto QLC flash cells and maintenance operations (e.g., obsolete item collection, data flushing, metadata processing, etc.). Scheduler 388 can be configured to track and average QLC throughput, and reduce SLC throughput as needed once scheduler 388 has identified that SLC throughput exceeds QLC throughput.

[0320] The data 504a-n may correspond to data stored at the corresponding storage devices 356a-n. For example, the data 504a-n may be stored at one or more data blocks of the storage devices 356a-n. The queues 506a-n may correspond to data structures that include a list of operations to be performed by the corresponding storage devices 506a-n. In some embodiments, the queues 506a-n may include a list of read operations (or other types of operations) to be performed during a write operation at the storage devices 356a-n, as will be described in additional detail below.

[0321] refer to Figure 5A , the scheduler 388 may receive a read request 502 to read data (eg, data 504a-n) from one or more of the storage devices 356a-n. For example, the scheduler 388 may receive a request to read data from a user of the storage system 500.

[0322] Figure 5B is an illustration of an example in which a scheduler of a storage system 550 receives queue information according to an embodiment of the present disclosure. The storage system 550 includes the same Figure 5A Components similar to those of the storage system 500 described.

[0323] The storage devices 356a-n may transmit information associated with the queues 506a-n to the scheduler 388. The scheduler 388 may receive the queue information and utilize the queue information to determine to which of the storage devices 356a-n the read request should be sent. In an embodiment, the information associated with the queues 506a-n may be a data structure that includes a list of operations to be performed during a write operation of the storage devices 356a-n. For example, the queues 506a-n may include one or more read operations to be performed intermittently during the performance of a write operation.

[0324] In some embodiments, the scheduler 388 may transmit a request for queue information to the storage devices 356a-n. In an embodiment, the storage devices 356a-n may transmit the queue information to the scheduler 388 after a trigger condition occurs. For example, if the queue is empty and does not contain any operations to be performed during a write operation, the storage devices 356a-n may transmit the queue information to the scheduler 388. In another example, if a determined amount of time has passed since the queue information was previously transmitted to the scheduler 388, the storage devices 356a-n may transmit the queue information to the scheduler 388. In an embodiment, other trigger conditions may be used to cause the queue information to be transmitted from the storage devices 356a-n to the scheduler 388.

[0325] Fig. 6A is an illustration of an example in which a scheduler of a storage system 600 inserts a read operation into a queue of a storage device according to some embodiments of the present disclosure. The storage system 600 includes the same Figure 5A and 5B Describes components similar to the storage system.

[0326] exist Fig. 6A In, the scheduler 388 has previously received queue information from the storage devices 356a to n, as previously described in Figure 5B Description. Fig. 6A , queues 506a to n are data structures that include a number of read operations to be performed during write operations of corresponding storage devices 356a to n. For example, queue 506a includes three read operations (e.g., read operation 1, read operation 2, and read operation 3) to be performed by storage device 356a during one or more write operations to write data to storage device 356a. Similarly, queue 356n includes two read operations (e.g., read operation 4 and read operation 5) to be performed by storage device 356n during one or more write operations to write data to storage device 356n. Meanwhile, queue 506b is empty, indicating that there are no operations to be performed by storage device 356b during one or more write operations to write data to storage device 356b. In some embodiments, after identifying an empty queue 506b of storage device 356b, scheduler 388 may transmit a request to storage device 356b to perform one or more read operations 602. The request may include an indication that the storage device 356b is to perform one or more read operations during an ongoing write operation to write data to the storage device 356b. For example, the indication may indicate that the one or more read operations are high priority operations that should be performed by the storage device 356b during an ongoing write operation.

[0327] In some embodiments, the scheduler 388 may preemptively transmit an indication to the storage device 356b that one or more read requests have been received and are about to be sent to the storage device 356b. The preemptive transmission of the indication may cause the storage device 356b to suspend ongoing write operations (or prevent the storage device 356b from starting new write operations) in anticipation of receiving and performing one or more read operations. The indication may also cause the storage device 356b to suspend or delay the execution of other types of operations that are considered to have a lower priority than the read request.

[0328] In an embodiment, the scheduler 388 may group multiple read requests together in various ways that benefit the operation of the storage device 356b. For example, the scheduler 388 may group read requests together to reduce the number of interrupted operations.

[0329] In an embodiment, the scheduler 388 may preemptively transmit an indication in response to the occurrence of an event. For example, if the data requested for a read is only available on the storage device 356b, or if an alternative source of data is not available, the scheduler 388 may preemptively transmit an indication to the storage device 356b.

[0330] After receiving the one or more read operations, the storage device 356b may insert the one or more read operations into the queue 506b for subsequent execution during the ongoing write operation.

[0331] Figure 6B is an illustration of an example in which a scheduler of a storage system 650 transmits a read operation to a storage device according to an embodiment of the present disclosure. The storage system 650 includes the same Figure 5A , 5B and components similar to the storage system described in 6A.

[0332] exist Figure 6B In, the scheduler 388 has previously received queue information from the storage devices 356a to n, as previously described in Figure 5B Description. Figure 6B, queues 506a to n are data structures that include a number of read operations (or other types of higher priority operations) to be performed during write operations of corresponding storage devices 356a to n. For example, queue 506a includes three read operations (e.g., read operation 1, read operation 2, and read operation 3) to be performed by storage device 356a during one or more write operations to write data to storage device 356a. Similarly, queue 356b includes two read operations (e.g., read operation 4 and read operation 5) to be performed by storage device 356b during one or more write operations to write data to storage device 356n. Queue 506n includes one read operation (e.g., read operation 6) to be performed by storage device 356n during one or more write operations to write data to storage device 356n. In some embodiments, after identifying queue 506n of storage device 356n as having the least number of read operations, scheduler 388 may transmit one or more read operations 602 to storage device 356n. The storage device 356n may insert a read operation into the queue 506n for execution during an ongoing write operation.

[0333] Figure 7 7 is an illustration of a timeline 700 of a storage device of a storage system performing a read operation during a multi-phase write operation according to an embodiment of the present disclosure. Timeline 700 includes vertical arrows (e.g., time 702) representing the passage of time, where the top of time 702 represents the beginning of timeline 700 and the bottom represents the end of timeline 700. Figure 7 Also included is a queue 506, which represents the Figures 5A to 6B One of the queues 506a to n of the storage devices 356a to n described in .

[0334] In some embodiments, a write operation may include multiple phases of programming a block of a storage device (not shown) before the data is persisted and stored by the storage device. For example, for a write operation that writes data using a quad-level cell (QLC) mode, there may be 5 phases. Figure 7 , a multi-phase write operation 704 is performed during time 702. The multi-phase write operation includes four phases: phase A 706, phase B 708, phase C 710, and phase D 712. This allows three opportunities between the four phases to temporarily interrupt the multi-phase write operation 704 to perform a higher priority read operation contained in queue 506.

[0335] refer to Figure 7, queue 506 includes three read operations (read operation 1, read operation 2, and read operation 3) to be performed during multi-phase write operation 704. After phase A 706 has been completed, multi-phase write operation 704 may be interrupted to perform read operation 1 to read data from the storage device. Once the data for read operation 1 has been read, phase B 708 of multi-phase write operation 704 may be performed. After phase B 708 has been completed, multi-phase write operation 704 may be interrupted to perform read operation 2 to read data from the storage device. Once the data for read operation 2 has been read, phase C 710 of multi-phase write operation 704 may be performed. After phase C 710 has been completed, multi-phase write operation 704 may be interrupted to perform read operation 3 to read data from the storage device. Once the data for read operation 3 has been read, phase D 712 of multi-phase write operation 704 may be performed, and multi-phase write operation 704 is completed.

[0336] It should be noted that the number of phases of the multi-phase write operation 704 and the number of read operations performed between the phases are shown for illustrative purposes only. Embodiments of the present disclosure may be applied to multi-phase write operations having any number of phases. Embodiments of the present disclosure may perform any number of operations, such as read operations, between the phases of the multi-phase write operation 704.

[0337] Figure 8 800 is an illustration of a storage device of a storage system according to an embodiment of the present disclosure suspending a write operation to perform a read operation. The timeline 800 includes vertical arrows (e.g., time 802) representing the passage of time, where the top of time 802 represents the beginning of the timeline 800 and the bottom represents the end of the timeline 800. Figure 8 Also included is a queue 506, which represents the Figures 5A to 6B One of the queues 506a to n of the storage devices 356a to n described in .

[0338] In some embodiments, a scheduler (not shown) of the storage system may pause the write operation 804 at any point during the write operation 804, rather than executing a higher priority operation between stages of a multi-stage write operation. This may provide additional improvements in latency for the storage system, as the storage device (not shown) no longer has to wait until the end of a stage to execute a higher priority operation. In an embodiment, the scheduler may pause the write operation 804 by transmitting an indication to the storage device that the write operation 804 is to be paused to execute a read operation in the queue 506. Upon receiving the indication, the storage device may pause the write operation 806. After pausing the write operation 806, the storage device may execute a read operation 808 from the queue 506 to read data from the storage device. Once the read operation is complete, the storage device may continue the write operation 810.

[0339] It should be noted that the number of pauses of the write operation 804 and the number of read operations performed during the pauses are shown for illustrative purposes only. Embodiments of the present disclosure may include any number of pauses during the write operation 804. Embodiments of the present disclosure may perform any number of operations, such as read operations, during the pauses of the write operation 804.

[0340] In some embodiments, background read operations may be performed between one or more phases of a multi-phase write, or during a pause in a write operation, as previously described in Figures 6 and 8. For example, a background read operation may be performed if the increase in latency caused by the background read does not cause the latency to exceed a threshold amount.

[0341] Fig. 9 900 is an example method for intelligently processing a read request according to an embodiment of the present disclosure. In general, the method 900 may be performed by processing logic, which may include hardware (e.g., a processing device, a circuit system, a dedicated logic, a programmable logic, a microcode, hardware of a device, an integrated circuit, etc.), software (e.g., instructions running or executed on a processing device), or a combination thereof. In some embodiments, a storage device (e.g., Figures 1A to 8 The processing logic of the storage device described in (a) may execute method 900.

[0342] In some embodiments, a storage device of a storage system may include an interface that allows a deadline to be applied to a read request. In an embodiment, the deadline may be received from a storage controller or scheduler of the storage system. In some embodiments, if the read request is not completed before the deadline, the deadline may cause the storage device to abort the read request. For example, if the deadline to read data from the storage device is 50 milliseconds (ms), and 51 ms has passed and the data has not been read, the storage device aborts the read request. In an embodiment, if the storage device determines that the read request will not be completed before the deadline, the storage device may immediately abort the read request.

[0343] In some embodiments, deadlines or other general scheduling hints may be used to group read requests together that may be completed together in some manner. For example, multiple read operations may be grouped together to be executed during the same interrupt operation.

[0344] Method 900 may begin at block 902 , where processing logic receives a read request including a high priority indication to read data from a storage device.

[0345] At block 904, processing logic determines whether a flash programming operation performed by processing logic will delay processing of a read request by at least a threshold amount of time if the read will wait for the flash programming operation to complete (typically the case if the read is directed to the same flash die of the flash package). Execution of a flash programming operation may lock out the read request or cause a significant delay in processing of the read request. In some embodiments, the threshold amount of time may be received from a user of the storage device. In an embodiment, the threshold may be received from a storage controller of the storage system. In an embodiment, a flash programming operation may have a lower priority than a read request.

[0346] In an embodiment, a flash memory programming operation may be a write operation to store data at a storage device. In some embodiments, the write operation may be a high bit per cell write operation. A high bit per cell write operation may be a write operation in which at least three bits are stored per cell of the flash memory. For example, a write operation may be performed using a three-level cell (TLC), a four-level cell (QLC), a five-level cell (PLC), or a higher bit per cell programming mode. The write operation may store a page of data, where a page is a layer of these bits, and sequential pages typically have cells with different ranges rather than different bits of the same cell. In an embodiment, the write operation may be the result of one or more requests to store data in the flash memory of the storage device.

[0347] In some embodiments, the flash programming operation may be an erase operation. In an embodiment, the erase operation may be the result of one or more requests to erase data stored in the flash memory of the storage device. In an embodiment, the flash programming operation may be performed internally in the storage device as part of internal maintenance.

[0348] At block 906, if processing logic determines that the flash programming operation did not delay the read request by the threshold amount of time, processing logic completes the flash programming operation and then processes the read request.

[0349] At block 908 , processing logic determines whether the flash programming operation can be interrupted after determining that the flash programming operation delayed the read request by the threshold amount of time.

[0350] The flash programming operation may be interrupted depending on the specific characteristics of the implementation of the particular flash chip, wherein the implementation has different limits on the number of times or duration that the flash programming operation may be interrupted. At block 910, if the processing logic determines that the ongoing operation cannot be interrupted, then the processing logic aborts the read request. In an embodiment, after aborting the read request, the processing logic may generate an indication that the read request has been aborted. For example, the indication may indicate that the storage device is busy and cannot process the read request. The indication may then be provided to a user of the storage device, a storage controller of the storage system, or any other recipient. In some embodiments, the processing logic may fault the read request to indicate that the read request could not be processed within a threshold amount of time.

[0351] In some embodiments, the indication may further indicate that the read request is aborted because the read request cannot be executed in a timely manner. This may be an optional sub-behavior of the high priority indication of block 902 .

[0352] At block 912, processing logic interrupts the flash programming operation after determining that the flash programming operation can be interrupted.

[0353] At block 914, processing logic processes the read request. In some embodiments, processing logic may receive one or more subsequent read requests to read data from the storage device. Processing logic may decide to process these subsequent read requests during the interruption of the flash programming operation along with the read request received at block 902.

[0354] At block 916, processing logic continues the flash programming operation after completing processing of the read request.

[0355] Fig.10is an illustration of an example of reducing latency of a storage system 1000 using a die-aware scheduler according to an embodiment of the present disclosure. As previously described, a die-aware scheduler (hereinafter also referred to as a "scheduler") is located between an authorization (e.g., authorization 168) and one or more storage devices of a storage system. The scheduler may be responsible for managing the flow of input-output (I / O) operations, such as read, write, and erase operations, flowing between the authorization and the storage device. For example, the scheduler may receive different types of I / O operations from the authorization and assign the I / O operations to one or more storage devices of the storage system. The scheduler may receive hints about the nature and importance of the operation from the authorization and receive constraints that need to be processed from the storage device. In an embodiment, a die-aware scheduler may be able to reduce latency of a storage system. There are multiple constraints from the storage devices of the storage system. Generally speaking, each die can only perform one operation at a time. There is also a limit of 100 operations per drive due to energy constraints (e.g., power token limits associated with the available amount of stored energy in the form of a battery or supercapacitor that can be used to complete a portion of an operation when powered off to ensure that the flash memory is in a usable state when the device is powered on in the future). In addition, there is a slower write speed for certain programming modes of the storage device. For example, the write speed of a write operation using a quad-level cell (QLC) mode is typically slower than the write speed of a write operation using a single-level cell (SLC) mode, and is typically substantially slower.

[0356] In addition, there are multiple constraints from the storage system's authorization. For a user-visible read operation that reads data stored at the storage system, if the read is queued behind a 60 millisecond (ms) write operation, the latency may become unacceptable to the user of the storage system.

[0357] In addition, in the storage system, write operations for data exceeding a certain size are written directly to the flash memory (hereinafter also referred to as "bypass writes"). If these write operations are stuck after the 60ms write operation, the latency for the end user may become unacceptable (even if the write is staged, the flash memory will eventually be written frequently, but it may become a "background" operation, which may occur at an unpredictable future time from the perspective of the user of the storage device). For example, a write operation with 128K of data may be written directly to the flash memory of the storage device. In some embodiments, the data may be written to the flash memory using the SLC programming mode. In other embodiments, the data may be written to the flash memory using the QLC programming mode at a die that is not currently performing a previous 128K write. In an embodiment, if the number of idle dies of the storage device is higher than the threshold amount of available operations limited by the power token of a given storage device, a write operation with 128K of data may be performed.

[0358] As previously in Figure 5A As described, in an embodiment of the storage system, the SLC write operation throughput should be less than or equal to the QLC write operation throughput to ensure sustained write operation performance. In addition, the scheduler 388 can also ensure that the read of SLC data has a sufficiently high priority so that the SLC reads can be processed unimpeded while managing the traffic of SLC writes. In an embodiment, the scheduler 388 can utilize separate queues (not shown) for read and write operations of only SLC dies to ensure that even when write operations are queued, they do not hinder the progress of clearing QLC writes.

[0359] In an embodiment, the queues may be configured to operate in response to one or more characteristics of the flash memory or the behavior of the storage device. In some embodiments, the storage device 356 may provide feedback to the scheduler 388 regarding the characteristics of the storage device 356. For example, the storage device 356 may provide several independent channels of the storage device 356, which may benefit from the use of multiple queues.

[0360] refer to Fig.10 , the storage system 1000 may correspond to and include Figures 1A to 4 Components of any storage system described in . Storage system 1000 includes storage device 356 having dies 1002a-h for storing data and die-aware scheduler 388, as previously described. To improve the latency of storage system 1000, scheduler 388 may split dies 1002a-h into having only SLC dies and only QLC dies. For example, in Fig.10 , dies 1002a-b are designated as SLC-only dies, which means that data written to dies 1002a-b is performed using the SLC programming mode. Similarly, dies 1002c-h are designated as QLC-only dies, which means that data written to dies 1002c-h is performed using the QLC programming mode.

[0361] The scheduler 388 may create one or more "dedicated lanes" for the SLC-only dies 1002a-b, which means that while only QLC operations need to contend for power tokens, only SLC operations have dedicated power tokens for each die, and therefore there are always outstanding SLC operations. This allows for reduced latency for bypassed writes and user-visible reads of data on the SLC dies 1002a-b.

[0362] It should be noted that the examples are shown for illustrative purposes only. Fig.10The programming modes described in , such as SLC and QLC, the number of dies of the storage device, and the ratio of SLC-only dies to QLC-only dies. In embodiments, different programming modes may be assigned to the dies of the storage device, and the characteristics and capabilities of actual flash memory implementations may vary significantly. For example, a first programming mode with lower latency may be assigned to a first group of dies, and a second programming mode with higher latency may be assigned to a second group of dies. Furthermore, the storage device may have any number of dies and different ratios of dies designated for particular programming modes. In some embodiments, some dies of the storage device may not be designated to store data using one programming mode, and may use any programming mode.

[0363] Fig.11 1 is an example method 1100 for improving storage system latency using different programming modes according to an embodiment of the present disclosure. In general, the method 1100 may be performed by processing logic, which may include hardware (e.g., a processing device, a circuit system, a dedicated logic, a programmable logic, a microcode, hardware of a device, an integrated circuit, etc.), software (e.g., instructions running or executed on a processing device), or a combination thereof. In some embodiments, as previously described in Figures 1A to 4 The processing logic of the storage device of the described storage system may execute the method 1100 .

[0364] Method 1100 may begin at block 1102, where processing logic receives a first request to program a first erase block in a first programming mode. In an embodiment, a first programming mode to be used is identified as part of the first request.

[0365] At block 1104, processing logic receives a second request to program a second erase block in a second programming mode. In an embodiment, a second programming mode to be used is identified as part of the second request.

[0366] At block 1106, processing logic receives a third request to store the first data in the first erase block.

[0367] At block 1108, processing logic receives a fourth request to store second data at a second erase block.

[0368] At block 1110, processing logic stores first data in a first erase block using a first programming mode.

[0369] At block 1112, processing logic stores second data in a second erase block using a second programming mode.

[0370] Fig.121 is an example method 1200 for optimizing data storage using different flash memory programming modes according to an embodiment of the present disclosure. In general, the method 1200 may be performed by processing logic, which may include hardware (e.g., a processing device, a circuit system, a dedicated logic, a programmable logic, a microcode, hardware of a device, an integrated circuit, etc.), software (e.g., instructions running or executed on a processing device), or a combination thereof. In some embodiments, as previously described in Figures 1A to 4 The processing logic of the storage device of the described storage system may execute the method 1200 .

[0371] Method 1200 may begin at block 1202, where processing logic provides a number of available cells of a storage device via an interface. Each cell may represent a set of erase blocks of the storage device, which may be programmed in different flash modes (e.g., SLC, MLC, TLC, QLC, etc.). Different flash modes may each have different effective capacities, performance characteristics, and susceptibility to failures from repeated erase / program cycles that result in, for example, a different number of complete cell writes per day over an expected useful life of five years (a variation of a common means of describing the useful life of a flash-based storage device). In some embodiments, if a sufficient number of cells are grouped together, processing logic may provide a programming mode that performs internal RAID or some other type of erasure coding across the available cells to increase the reliability of the data stored at the cells. It should be noted that embodiments of the present disclosure are not limited to flash programming modes, but may utilize other types of programming modes, such as added internal redundancy modes.

[0372] In some embodiments, a cell may simply represent a number of erase blocks (e.g., a group of 100 erase blocks). In some embodiments, each cell may represent one or more flash die of a storage device. In embodiments, each cell in the available cells may be operated in parallel with each other cell. For example, a read, erase, write, and maintenance operation of a first cell in the available cells does not block a parallel read, erase, write, or storage device background operation of a second cell in the available cells. In embodiments, a request received by a storage device may be able to prevent and subsequently allow background operations of a particular cell of the storage device to be performed (when blocked, a user of the storage device can ensure, for example, that read and write operations are not slowed down by background operations of the storage device).

[0373] At block 1204, processing logic receives a first request to form a first cell set that includes a first number of available cells configured to be programmed in a first flash memory programming mode. Forming the first cell set may result in a virtual addressable capacity that corresponds to an effective capacity of the first number of available cells when programmed in the first mode.

[0374] At block 1206, processing logic receives a second request to form a second cell set, the second cell set comprising a second number of available cells configured to be programmed in a second flash memory programming mode different from the first programming mode. Forming the second cell set may result in a virtual addressable capacity corresponding to an effective capacity of the second number of available cells when programmed in the second mode.

[0375] At block 1208 , processing logic receives a third request to store first data to a first virtual address associated with the first set of cells.

[0376] At block 1210 , processing logic receives a fourth request to store second data at a second virtual address associated with a second set of cells.

[0377] At block 1212, processing logic stores first data in an erase block of one or more cells of a first set of cells and programs in a first mode.

[0378] At block 1214, processing logic stores the second data in the erase block of one or more cells of the second set of cells and programs in the second mode.

[0379] Fig.13 1 is an example method 1300 for intelligently directing flash operations of a modular storage system according to an embodiment of the present disclosure. In general, the method 1300 may be performed by processing logic, which may include hardware (e.g., a processing device, a circuit system, a dedicated logic, a programmable logic, a microcode, hardware of a device, an integrated circuit, etc.), software (e.g., instructions running or executed on a processing device), or a combination thereof. In some embodiments, as previously described in Figures 1A to 4 The processing logic of a storage node of the described storage system may execute the method 1300 .

[0380] Method 1300 may begin at block 1302, where processing logic receives read requests and write requests for data to a storage system.

[0381] At block 1304 , processing logic directs the read request to a first set of storage nodes hosting flash-based storage devices storing data for the read request.

[0382] At block 1306, processing logic stores the write data in erasure coded segments distributed in shards across a set of storage devices hosted by a second set of storage nodes. The shards may be directed to a specific flash-based storage device and delivered to and written by a specific storage node hosting the specific flash-based storage device.

[0383] In an embodiment, write operations may be configured by multiple storage nodes of a storage system to operate on a subset of flash dies of a flash-based storage device during a time span to maintain availability of a majority of dies of the flash-based storage device for read operations to read data from the flash-based storage device.

[0384] At block 1308, processing logic balances write activity to independent performance zones of flash memory of the flash-based storage device to ensure that most read requests are processed within a threshold amount of time without requiring more write or erase operation interruptions than allowed by the flash implementation, as previously described.

[0385] Although this specification contains many specific implementation details, these details should not be interpreted as limiting the scope of any invention or claimable content, but should be interpreted as describing features specific to a particular embodiment of a particular invention. Certain features described in this specification in the context of a separate embodiment may also be implemented in combination in a single embodiment. Conversely, the various features described in the context of a single embodiment may also be implemented individually or in any appropriate sub-combination in multiple embodiments. In addition, although features may be described above as working in certain combinations and even initially claimed to be so, in some cases, one or more features from the claimed combination may be deleted from the combination, and the claimed combination may involve a sub-combination or a variation of a sub-combination.

[0386] Similarly, although operations are depicted in a particular order in the drawings, this should not be understood as requiring that the operations be performed in the particular order shown or in sequential order or that all illustrated operations be performed to achieve the desired result. In some cases, multitasking and parallel processing may be advantageous. In addition, the separation of various system components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products.

[0387] Thus, specific embodiments of the subject matter have been described. Other embodiments are within the scope of the appended claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing may be advantageous.

Claims

1. A storage device, configured to: receiving a read request with a high priority indication; determining whether an ongoing flash memory programming operation will delay processing the read request by a threshold amount of time; and In response to determining that the ongoing flash programming operation delays processing the read request by the threshold amount of time, interrupting the ongoing flash programming operation, wherein the storage device will process the read request after interrupting the ongoing flash programming operation.

2. The memory device of claim 1, wherein the ongoing flash memory programming operation is a write operation to an erase block programmed to store at least three bits per cell. 3 . The memory device of claim 1 , wherein the ongoing flash memory programming operation is an erase operation. 4 . The memory device of claim 1 , wherein the ongoing flash programming operation is performed internally within the memory device as part of internal maintenance.

5. The memory device of claim 1 , wherein in response to determining that the ongoing flash memory programming operation cannot be interrupted, the memory device is further configured to: The read request is failed to indicate that the read request cannot be processed within the threshold amount of time.

6. The storage device according to claim 1, further configured to: receiving one or more subsequent read requests; and The read request and the one or more subsequent read requests are processed during the interruption.

7. The storage device according to claim 1, further configured to: processing the read request; and The ongoing flash memory programming operation is continued after processing of the read request is completed.

8. The storage device of claim 1, wherein the storage device is a direct mapped storage device.

9. The storage device of claim 1, wherein the storage device supports a partitioned namespace.

10. The storage device of claim 1, wherein the storage device supports an abstraction that organizes a virtual address space of the storage device such that virtual segments have a size that enables the virtual segments to be sequentially written within the same erase block of the storage device.

11. A method comprising: receiving a read request with a high priority indication; determining, by a processing device of the storage device, whether an ongoing flash memory programming operation will delay processing of the read request by a threshold amount of time; and In response to determining that the ongoing flash programming operation delays processing the read request by the threshold amount of time, interrupting the ongoing flash programming operation, wherein the storage device will process the read request after interrupting the ongoing flash programming operation.

12. The method of claim 11, wherein the ongoing flash memory programming operation is a write operation to an erase block programmed to store at least three bits per cell.

13. The method of claim 11, wherein the ongoing flash memory programming operation is an erase operation.

14. The method of claim 11, wherein the ongoing flash programming operation is performed internally within the memory device as part of internal maintenance.

15. The method of claim 11, wherein in response to determining that the ongoing flash memory programming operation cannot be interrupted, the method further comprises: The read request is failed to indicate that the read request cannot be processed within the threshold amount of time.

16. The method according to claim 11, further comprising: receiving one or more subsequent read requests; and The read request and the one or more subsequent read requests are processed during the interruption.

17. The method according to claim 11, further comprising: Processing the read request; and The ongoing flash memory programming operation is continued after processing of the read request is completed.

18. The method of claim 11, wherein the storage device is a direct mapped storage device. The method of claim 11 , wherein the storage device supports a partitioned namespace.

20. A non-transitory computer-readable storage medium storing instructions that, when executed, cause a processing device of a storage device to: receiving a read request with a high priority indication; determining whether an ongoing flash memory programming operation will delay processing the read request by a threshold amount of time; and In response to determining that the ongoing flash programming operation delays processing the read request by the threshold amount of time, interrupting the ongoing flash programming operation, wherein the storage device will process the read request after interrupting the ongoing flash programming operation.

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