Distributed storage deployment method and device based on cloud computing and medium

Abstract

The invention discloses a distributed storage deployment method and device based on cloud computing and a medium, and the method comprises the steps: determining the number of storage servers, determining a distribution model according to the number of storage servers, determining a plurality of rack servers according to the distribution model, and determining the storage number of the rack servers, loading the servers corresponding to the storage number into a rack server; a data deployment mode is determined according to the distribution model, the data deployment mode comprises global distribution and local distribution, and storage nodes are determined in the multiple rack servers according to the data deployment mode; and sending the written data to a storage node so as to store the data through the storage node. Through accurate resource configuration, a flexible data deployment mode, redundancy guarantee of data copies and dynamic resource adjustment, the performance and reliability of the storage system are remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of distributed software deployment technology, and in particular to a distributed storage deployment method, device, and medium based on cloud computing. Background Art

[0002] With the explosive growth of user data and the demand for data analysis and mining, more and more data needs to be stored long-term, placing tremendous pressure on existing network storage. Traditional storage performance is limited by controller performance, making horizontal scalability difficult. Existing distributed cloud storage server racking only considers server power consumption and rack server capacity. Data deployment also selects available storage nodes based on the established node distribution after racking, lacking consideration of the full lifecycle of distributed cloud storage. Summary of the Invention

[0003] In order to solve the above problems, the present application proposes a distributed storage deployment method based on cloud computing, including: determining the number of storage servers, determining a distribution model according to the number of storage servers, determining multiple rack servers according to the distribution model, and determining the storage quantity of the rack servers, and loading the servers corresponding to the storage quantity into the rack servers; determining a data deployment mode according to the distribution model, the data deployment mode includes global distribution and local distribution, and determining storage nodes in the multiple rack servers according to the data deployment mode; sending the written data to the storage node to store the data through the storage node.

[0004] In one example, before determining the distribution model based on the number of storage servers, the method further includes: determining a corresponding numerical characteristic based on the number of storage servers, and grouping the plurality of rack servers based on the numerical characteristic, wherein the numerical characteristic includes one or more of the following: a prime number and a composite number; and the expression for the grouped plurality of rack servers is:

[0005] α=[μi+2,μi+1,μi,μi-1,μi-2,μi-3]

[0006] Wherein, α represents the number of the rack server, i is a positive integer, and μ is a conventional coefficient.

[0007] In one example, determining a distribution model based on the number of storage servers specifically includes: determining the number of storages corresponding to the rack servers, and determining the distribution model based on the number of storage servers and the number of storages corresponding to the rack servers. The distribution model is expressed as follows:

[0008]

[0009] Among them, kα The storage capacity of the rack server.

[0010] In one example, determining the number of rack servers according to the distribution model specifically includes: determining the quantity model of the storage servers according to the distribution model, and determining the quantity model of the rack servers according to the quantity model of the storage servers; determining the maximum storage quantity of the rack servers, and determining the number of rack servers according to the maximum storage quantity and the quantity model of the rack servers.

[0011] In one example, the method further includes: installing the storage server into a rack server corresponding to the number of rack servers, and determining the number of rack servers in the rack servers that have the same number of storage servers, wherein the number of rack servers in the rack servers that have the same number of storage servers is greater than or equal to a preset number of data copies.

[0012] In one example, determining a data deployment mode according to the distribution model specifically includes: determining a first rack server quantity model and a second rack server quantity model according to the distribution model; determining that the deployment mode is global distribution according to the first rack server quantity model, and determining that the deployment mode is local distribution according to the second rack server quantity model.

[0013] In one example, after determining that the deployment mode is globally distributed according to the first rack server quantity model, the method further includes: obtaining write data, and determining a corresponding number of multiple rack servers according to a preset backup quantity, using the corresponding number of multiple rack servers as the storage nodes, and sending the write data to each storage node for storage; obtaining new write data, and re-determining new storage nodes to send the new write data to the new storage nodes for storage.

[0014] In one example, after determining that the deployment mode is locally distributed according to the second rack server quantity model, the method further includes: dividing multiple storage servers according to a preset backup quantity to obtain multiple server groups, and the number of servers in each server group is the same; determining a corresponding number of multiple rack servers according to the backup quantity, and determining the storage quantity of the rack servers, and deploying a corresponding number of storage servers according to the storage quantity; obtaining write data, and sending the write data to multiple rack servers for storage through the storage servers in the multiple rack servers.

[0015] On the other hand, the present application also proposes a distributed storage deployment device based on cloud computing, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the distributed storage deployment device based on cloud computing can execute: determining the number of storage servers, determining a distribution model based on the number of storage servers, determining multiple rack servers based on the distribution model, and determining the storage quantity of the rack server, and loading the servers corresponding to the storage quantity into the rack server; determining a data deployment mode based on the distribution model, the data deployment mode includes global distribution and local distribution, so as to determine a storage node in the multiple rack servers based on the data deployment mode; sending the written data to the storage node so that the data is stored through the storage node.

[0016] On the other hand, the present application also proposes a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to: determine the number of storage servers, determine a distribution model based on the number of storage servers, determine multiple rack servers based on the distribution model, and determine the storage quantity of the rack server, and load the servers corresponding to the storage quantity into the rack server; determine a data deployment mode based on the distribution model, wherein the data deployment mode includes global distribution and local distribution, so as to determine a storage node in the multiple rack servers based on the data deployment mode; and send the written data to the storage node so as to store the data through the storage node.

[0017] This application optimizes storage resource allocation by precisely calculating the number of storage servers and the distribution model. Grouping rack servers and utilizing a complex distribution model to determine storage capacity ensures efficient storage resource utilization, avoiding resource waste and bottlenecks. This application provides flexible data deployment modes, including global and local distribution. Global distribution helps evenly distribute data across multiple rack servers, improving data reliability and access speed; local distribution is suitable for specific scenarios, such as requiring rapid access to large amounts of related data. This flexibility enables the storage system to adapt to diverse application requirements. This application also considers the number of data replicas, ensuring data redundancy and security. By determining the number of rack servers with the same number of storage servers, this method ensures that data replicas are distributed across multiple rack servers, thereby improving data fault tolerance. Storage nodes can be dynamically reassigned based on newly written data, enabling dynamic adjustment and optimization of storage resources. This not only increases the flexibility of the storage system but also ensures continuous and efficient system operation. Through precise resource configuration, flexible data deployment models, data replica redundancy, and dynamic resource adjustment, this application significantly improves the performance and reliability of the storage system. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1 This is a flow chart of a distributed storage deployment method based on cloud computing in an embodiment of the present application;

[0020] Figure 2 This is a schematic diagram of a distributed storage deployment device based on cloud computing in an embodiment of the present application. DETAILED DESCRIPTION

[0021] To make the purpose, technical solutions, and advantages of this application more clear, the technical solutions of this application will be clearly and completely described below in conjunction with the specific embodiments of this application and the corresponding drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0022] The following describes in detail the technical solutions provided by various embodiments of the present application in conjunction with the accompanying drawings.

[0023] like Figure 1To solve the above problems, an embodiment of the present application provides a distributed storage deployment method based on cloud computing, which includes:

[0024] S101. Determine the number of storage servers, determine a distribution model based on the number of storage servers, determine multiple rack servers based on the distribution model, determine the storage quantity of the rack servers, and install the servers corresponding to the storage quantity into the rack servers.

[0025] Distributed cloud storage systems achieve efficient and reliable data storage by building a storage cluster consisting of multiple storage servers. Each server acts as an independent storage node, which not only improves the system's scalability and flexibility but also enhances overall storage capacity through clustering. These storage nodes typically mount the same number and capacity of hard drives as storage media to ensure consistent storage performance across all nodes. This design not only simplifies storage management but also ensures a more balanced distribution of data within the cluster, improving overall system performance. To ensure data security, distributed cloud storage systems generally employ a three-replica storage redundancy mechanism, meaning three copies of the data are stored across three different storage nodes. This redundancy not only enhances data reliability but also enables the system to rapidly recover data from other functioning nodes if one or more storage nodes fail, ensuring data integrity and preventing data loss. Furthermore, the three-replica storage redundancy mechanism provides high data availability. Even if a storage node becomes temporarily inaccessible due to hardware failure, network issues, or other reasons, the system can still access data from the other two replicas, ensuring business continuity.

[0026] In one embodiment, a certain number of storage servers is configured based on business needs. When the number of servers is not an exact multiple of 3, a different racking strategy is indeed necessary to ensure data redundancy and storage resource utilization. If the number of servers is a prime number, since prime numbers cannot be evenly divided into 3 or multiples of 3, perfect 3-replica redundancy cannot be achieved. In this case, consider dividing the servers into groups that are as close to a multiple of 3 as possible, and reserving some spare servers. For example, if there are 13 servers, 12 of them can be divided into 4 groups of 3, each with 1 reserved as a spare. If the number of servers is a composite number but not an exact multiple of 3, such as 4, 6, 8, or 10, storage resources can be optimized through grouping and redundancy configuration. For these numbers, try to divide them into groups that are multiples of 3, or as close to a multiple of 3 as possible, to achieve higher storage redundancy. For example, if there are 10 servers, they can be divided into 5 groups of 2, but this does not meet the redundancy requirement of 3 replicas. A better strategy might be to divide the nine servers into three groups of three, with one server reserved as a backup. Regardless of whether the number of servers is a prime or composite number, the redundancy configuration should be optimized to improve data reliability and availability. Whenever possible, a three-copy redundancy mechanism should be given priority because it provides a higher level of data protection. Flexibly divide servers into different groups based on the number of servers and business needs. When grouping, redundancy and load balancing should be considered to ensure consistent storage performance and data access speed across groups. When configuring servers, consider reserving some backup servers to deal with possible hardware failures or data loss. Backup servers can be quickly connected to the system when needed to ensure business continuity and data integrity.

[0027] First, we divide the model into three types. Let the number of servers be α. Then the rack server model formula is α = 3n, α = 3n + 1, α = 3n + 2, where n is a positive integer. At this time, depending on the parity of the value of i, α can be a prime number or a composite number. The formula for i is i = 2n or i = 2n - 1, where n is a positive integer. The formula for the rack server α is:

[0028] α=[μi+2,μi+1,μi,μi-1,μi-2,μi-3]

[0029] Wherein, α represents the number of the rack server, i is a positive integer, and μ is a conventional coefficient with a value of 6.

[0030] In one embodiment, the number of storage nodes that can be installed in each rack server is limited, taking into account the available capacity of the rack server, including physical space and power load. Let this maximum storage capacity be k. Then for each rack server α, the number of storage nodes that can be installed, i.e., the storage capacity kα The following conditions must be met: 1≤k α ≤k. For any rack server α, the number of storage nodes that can be installed is k α is an integer between 1 and k. In practical applications, the specific storage quantity k of each rack server is determined x Several factors need to be considered, including the physical size of the rack server, power load, cooling requirements, network connectivity, business needs, and redundancy requirements. When determining the amount of storage per rack server, all of these factors need to be considered comprehensively, and some trade-offs may be necessary. For example, if business demand is very high, you may need to sacrifice some redundancy to install more storage nodes; if the power load of the rack server is close to its limit, you may need to reduce the amount of storage to avoid overheating. The distribution model expression for planning the number of rack servers based on the number of servers is:

[0031]

[0032] It can be concluded that the models of the number of rack servers are mainly divided into two categories. The first type of model (i.e. the first rack server number model) is The second model (i.e., the second rack server number model) is 3α. In the first model, the number of rack servers is any number greater than 3, but not an integer multiple of 3. The number of storage nodes in each rack server is consistent. In the second model, the number of rack servers is an integer multiple of 3. Among them, the storage nodes in at least three rack servers are consistent.

[0033] The maximum number of rack servers that can be installed in a rack server defines the maximum number of servers that can be installed in each rack server. When determining the number of rack servers, use the maximum number of rack servers as the upper limit and select an appropriate number of rack servers. The number of rack servers that are less than or equal to the maximum number of rack servers is determined based on the number of servers and the number of pre-set data replicas, and from an economic cost perspective.

[0034] In one embodiment, in a distributed cloud storage system, a corresponding rack server quantity model is determined based on a server quantity model, and the number of rack servers is calculated accordingly. First, the server quantity model is determined based on business requirements and system design. For example, the server quantity model may be 6i+2. After determining the server quantity model, the corresponding rack server quantity model needs to be determined. Since the maximum number of rack servers that can be installed is k, the rack server quantity model can be expressed as [6i+2 / k]. Based on the server quantity model and the maximum number of rack servers that can be installed, the required number of rack servers is calculated.

[0035] In one embodiment, when servers are loaded into a corresponding number of rack servers, it is necessary to ensure that the number of rack servers with the same number of servers in the loaded rack servers is greater than or equal to the preset number of data replicas. This is to ensure data redundancy and reliability. For example, if the preset number of data replicas is 3, then at least three rack servers must have the same number of servers to support triple-copy redundant storage of data.

[0036] S102: Determine a data deployment mode according to the distribution model, where the data deployment mode includes global distribution and local distribution, and determine storage nodes in the multiple rack servers according to the data deployment mode.

[0037] S103: Send the written data to the storage node so that the data is stored by the storage node.

[0038] In one embodiment, for the first type of model, a global data distribution method is adopted. To enhance data reliability and fault tolerance, the same number of storage nodes in three rack servers are randomly selected from all rack servers as three logical rack servers. The formula for the number of logical rack servers is:

[0039]

[0040] Randomly select three rack servers from the f(α) rack servers. Within each of these selected rack servers, select the same number of storage nodes. These form three logical rack servers, and the data is stored there. When storing data, instead of selecting multiple groups of logical rack servers from the f(α) rack servers to store the same data, only one group, or three rack servers, is selected at a time to store a single copy of the data. This way, even if any one or two of the rack servers lose power, the data remains intact because copies of the data are stored in the remaining rack servers.

[0041] In one embodiment, for the second type of model, a local data distribution method is adopted. Among all rack servers, every three rack servers form a three-replica rack server group. Each data block will have a copy in these three rack servers, thereby providing data redundancy. The physical rack server and the logical rack server are consistent, that is, each replica rack server group is directly composed of three physical rack servers. First, the storage nodes are divided into three equal parts, and the number of nodes S in each equal part is 6i / 3. According to the capacity k of each rack server, the storage nodes in each equal part are further divided into specific rack servers. The number of rack servers required for the second type of model is:

[0042] ∑[(6i / 3) / k α ]×3=∑α×3

[0043] For example, there are 30 storage servers, divided into three equal parts, each with 10 servers. Based on the capacity of the rack server, each rack server can be installed with a maximum of 6 servers. In this case, 6 rack servers are used to form two storage rack server groups with 3 replicas.

[0044] like Figure 2 As shown, the embodiment of the present application also provides a distributed storage deployment device based on cloud computing, including:

[0045] at least one processor; and,

[0046] a memory communicatively connected to at least one processor; wherein,

[0047] The memory stores instructions that can be executed by at least one processor, and the instructions are executed by the at least one processor to enable a distributed storage deployment device based on cloud computing to perform:

[0048] Determining the number of storage servers, determining a distribution model based on the number of storage servers, determining a plurality of rack servers based on the distribution model, determining the storage quantity of the rack servers, and installing the servers corresponding to the storage quantity into the rack servers;

[0049] Determining a data deployment mode according to the distribution model, wherein the data deployment mode includes global distribution and local distribution, so as to determine storage nodes in the plurality of rack servers according to the data deployment mode;

[0050] The written data is sent to the storage node so that the data is stored by the storage node.

[0051] The embodiment of the present application further provides a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as follows:

[0052] Determining the number of storage servers, determining a distribution model based on the number of storage servers, determining a plurality of rack servers based on the distribution model, determining the storage quantity of the rack servers, and installing the servers corresponding to the storage quantity into the rack servers;

[0053] Determining a data deployment mode according to the distribution model, wherein the data deployment mode includes global distribution and local distribution, so as to determine storage nodes in the plurality of rack servers according to the data deployment mode;

[0054] The written data is sent to the storage node so that the data is stored by the storage node.

[0055] In the 1990s, technological improvements could be clearly distinguished as either hardware improvements (for example, improvements to circuit structures like diodes, transistors, and switches) or software improvements (improvements to process flows). However, with the advancement of technology, many process flow improvements today can now be considered direct improvements to hardware circuit structures. Designers almost always create the corresponding hardware circuit structure by programming the improved process flow into the hardware circuit. Therefore, it cannot be said that a process flow improvement cannot be implemented using hardware modules. For example, a programmable logic device (PLD), such as a field programmable gate array (FPGA), is an integrated circuit whose logical function is determined by user programming. Designers can "integrate" a digital system on a PLD through their own programming, without having to hire a chip manufacturer to design and manufacture a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly done using "logic compiler" software. This is similar to the software compiler used when developing programs. Before compilation, the original code must also be written in a specific programming language, called a hardware description language (HDL). There is not just one HDL, but many, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. The most commonly used ones are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art will also understand that by simply programming the method flow in one of these hardware description languages and then programming it into an integrated circuit, a hardware circuit that implements the logic method flow can be easily obtained.

[0056] The controller can be implemented in any suitable manner. For example, the controller can take the form of a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro)processor, logic gates, switches, application-specific integrated circuits (ASICs), programmable logic controllers, and embedded microcontrollers. Examples of controllers include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that in addition to implementing the controller in a purely computer-readable program code format, the controller can be implemented in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be considered as structures within the hardware component. Or even, the devices for implementing various functions can be considered as both software modules that implement the method and structures within the hardware component.

[0057] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0058] For the convenience of description, the above devices are described as being divided into various units according to their functions. Of course, when implementing this specification, the functions of each unit can be implemented in the same or multiple software and / or hardware.

[0059] The various embodiments in this application are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device and medium embodiments are generally similar to the method embodiments, so their descriptions are relatively simple. For relevant portions, refer to the descriptions of the method embodiments.

[0060] The devices and media provided in the embodiments of the present application correspond one-to-one to the methods. Therefore, the devices and media also have similar beneficial technical effects to their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0061] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0062] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0063] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0065] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0066] Memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

[0067] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0068] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0069] The above are merely embodiments of the present application and are not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application should all be included within the scope of the claims of the present application.

Claims

1. A distributed storage deployment method based on cloud computing, characterized in that: include: Determining the number of storage servers, determining a distribution model based on the number of storage servers, determining a plurality of rack servers based on the distribution model, determining the storage quantity of the rack servers, and installing the servers corresponding to the storage quantity into the rack servers; Determining a data deployment mode according to the distribution model, wherein the data deployment mode includes global distribution and local distribution, so as to determine storage nodes in the plurality of rack servers according to the data deployment mode; The written data is sent to the storage node so that the data is stored by the storage node.

2. The method according to claim 1, characterized in that Before determining the distribution model according to the number of storage servers, the method further includes: Determining a corresponding numerical characteristic according to the number of the storage servers, and grouping the plurality of rack servers according to the numerical characteristic, wherein the numerical characteristic includes one or more of the following: a prime number, a composite number; The expression of the grouped multiple rack servers is: α=[μi+2,μi+1,μi,μi-1,μi-2,μi-3] Wherein, α represents the number of the rack server, i is a positive integer, and μ is a conventional coefficient.

3. The method according to claim 2, characterized in that Determining a distribution model according to the number of storage servers specifically includes: Determine the storage quantity corresponding to the rack server, and determine the distribution model according to the number of the storage servers and the storage quantity corresponding to the rack server. The distribution model is expressed as follows: Among them, k α The storage capacity of the rack server.

4. The method according to claim 1, wherein Determining the number of rack servers according to the distribution model specifically includes: Determine the quantity model of the storage servers according to the distribution model, and determine the quantity model of the rack servers according to the quantity model of the storage servers; The maximum storage quantity of the rack server is determined, and the number of rack servers is determined according to the maximum storage quantity and a quantity model of the rack servers.

5. The method according to claim 1, characterized in that The method further comprises: The storage servers are installed in rack servers corresponding to the number of rack servers, and the number of rack servers with the same number of storage servers in the rack servers is determined, wherein the number of rack servers with the same number of storage servers in the rack servers is greater than or equal to a preset number of data copies.

6. The method according to claim 1, characterized in that Determining a data deployment mode according to the distribution model specifically includes: Determine a first rack server quantity model and a second rack server quantity model according to the distribution model; The deployment mode is determined to be global distribution according to the first rack server quantity model, and the deployment mode is determined to be local distribution according to the second rack server quantity model.

7. The method according to claim 6, characterized in that After determining that the deployment mode is global distribution according to the first rack server quantity model, the method further includes: Acquire write data, and determine a corresponding number of rack servers according to a preset backup quantity, use the corresponding number of rack servers as the storage nodes, and send the write data to each storage node for storage; New write data is acquired, and new storage nodes are re-determined, so as to send the new write data to the new storage nodes for storage.

8. The method according to claim 6, characterized in that After determining that the deployment mode is local distribution according to the second rack server quantity model, the method further includes: Dividing the multiple storage servers according to the preset number of backups to obtain multiple server groups, each server group having the same number of servers; Determine a corresponding number of multiple rack servers according to the backup quantity, determine the storage quantity of the rack servers, and deploy a corresponding number of storage servers according to the storage quantity; The write data is acquired and sent to a plurality of rack servers so as to be stored by storage servers in the plurality of rack servers.

9. A distributed storage deployment device based on cloud computing, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the distributed storage deployment device based on cloud computing to perform: Determining the number of storage servers, determining a distribution model based on the number of storage servers, determining a plurality of rack servers based on the distribution model, determining the storage quantity of the rack servers, and installing the servers corresponding to the storage quantity into the rack servers; Determining a data deployment mode according to the distribution model, wherein the data deployment mode includes global distribution and local distribution, so as to determine storage nodes in the plurality of rack servers according to the data deployment mode; The written data is sent to the storage node so that the data is stored by the storage node.

10. A non-volatile computer storage medium storing computer executable instructions, characterized in that: The computer executable instructions are configured to: Determining the number of storage servers, determining a distribution model based on the number of storage servers, determining a plurality of rack servers based on the distribution model, determining the storage quantity of the rack servers, and installing the servers corresponding to the storage quantity into the rack servers; Determining a data deployment mode according to the distribution model, wherein the data deployment mode includes global distribution and local distribution, so as to determine storage nodes in the plurality of rack servers according to the data deployment mode; The written data is sent to the storage node so that the data is stored by the storage node.

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