Data backup method and device, electronic equipment, storage medium and program product

Abstract

The present disclosure provides a data backup method and device, electronic equipment, storage medium and program product, relates to the field of cloud computing, and can be applied to cloud storage, data backup, data disaster recovery and the like. The method comprises the following steps: in the case that a data backup request is received, a first mapping relationship between a plurality of cloud storage units and a plurality of physical resource pools is acquired; in response to the first mapping relationship not satisfying a preset distribution condition, a second mapping relationship between a plurality of target objects and the plurality of physical resource pools is determined to satisfy the preset distribution condition, the preset distribution condition representing the distribution of the cloud storage units mapped to each physical resource pool, and the target objects comprising subjects that can access associated cloud storage units; based on the second mapping relationship and an association relationship between the plurality of target objects and the cloud storage units, the first mapping relationship is adjusted; and the data backup request is processed by the plurality of target objects based on the adjusted first mapping relationship.

Description

Technical Field

[0001] This disclosure relates to the field of cloud computing and can be applied to scenarios such as cloud storage, data backup, and data disaster recovery. More specifically, this disclosure provides a data backup method, apparatus, electronic device, storage medium, and program product. Background Technology

[0002] With the development of cloud service technology, cloud storage units can be used to provide storage services such as object storage, file storage, and databases with disaster recovery capabilities. For example, multiple cloud storage units can be used as multiple storage components for data backup, providing high availability support with disaster recovery capabilities for business data storage. Summary of the Invention

[0003] This disclosure provides data backup methods, apparatus, electronic devices, storage media, and program products.

[0004] According to one aspect of this disclosure, a data backup method is provided, comprising: upon receiving a data backup request from a target object, obtaining a first mapping relationship between multiple cloud storage units and multiple physical resource pools, wherein the cloud storage unit includes virtual storage space for backing up business data of the target business, the physical resource pool includes multiple physical storage units for providing physical storage space for the mapped cloud storage units, and the target object includes a subject that can access the associated cloud storage units; in response to the first mapping relationship not satisfying a preset distribution condition, determining a second mapping relationship between the multiple target objects and the multiple physical resource pools to satisfy the preset distribution condition, the preset distribution condition characterizing the distribution of cloud storage units mapped to each physical resource pool, and the target object including a subject that can access the associated cloud storage units; adjusting the first mapping relationship based on the second mapping relationship and the association relationship between the multiple target objects and the cloud storage units; and processing the data backup request using the multiple target objects based on the adjusted first mapping relationship.

[0005] According to another aspect of this disclosure, a data backup apparatus is provided, comprising: an acquisition module, configured to acquire a first mapping relationship between multiple cloud storage units and multiple physical resource pools upon receiving a data backup request, wherein the cloud storage units include virtual storage space for backing up business data of a target business, and the physical resource pools include multiple physical storage units for providing physical storage space for the mapped cloud storage units; a determination module, configured to determine a second mapping relationship between multiple target objects and multiple physical resource pools to satisfy the preset distribution conditions in response to the first mapping relationship not satisfying preset distribution conditions, wherein the preset distribution conditions characterize the distribution of cloud storage units mapped to each physical resource pool, and the target objects include entities that can access associated cloud storage units; an adjustment module, configured to adjust the first mapping relationship based on the second mapping relationship and the association relationship between the multiple target objects and the cloud storage units; and a processing module, configured to process the data backup request using the multiple target objects based on the adjusted first mapping relationship.

[0006] According to another aspect of this disclosure, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform a method provided according to this disclosure.

[0007] According to another aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing a computer to perform the methods provided according to this disclosure.

[0008] According to another aspect of this disclosure, a computer program product is provided, including a computer program that, when executed by a processor, implements the method provided according to this disclosure.

[0009] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0010] The accompanying drawings are provided to better understand this solution and do not constitute a limitation of this disclosure. Wherein:

[0011] Figure 1 This is a schematic diagram illustrating an application scenario of a data backup method and apparatus according to an embodiment of the present disclosure;

[0012] Figure 2 This is a flowchart of a data backup method according to an embodiment of the present disclosure;

[0013] Figure 3This is a schematic diagram of the architecture between a cloud storage unit, a target object, and a physical resource pool according to an embodiment of the present disclosure.

[0014] Figure 4 This is a schematic diagram illustrating the determination of a second mapping relationship according to an embodiment of the present disclosure;

[0015] Figure 5 This is a schematic diagram of the architecture of a data backup method according to an embodiment of the present disclosure;

[0016] Figure 6 This is a timing diagram of a data backup method according to an embodiment of the present disclosure;

[0017] Figure 7 This is a block diagram of a data backup apparatus according to an embodiment of the present disclosure;

[0018] Figure 8 A schematic block diagram of an example electronic device that can be used to implement embodiments of the present disclosure is shown. Detailed Implementation

[0019] The exemplary embodiments of this disclosure are described below with reference to the accompanying drawings, including various details of the embodiments to aid understanding, and should be considered merely exemplary. Therefore, those skilled in the art will recognize that various changes and modifications can be made to the embodiments described herein without departing from the scope and spirit of this disclosure. Similarly, for clarity and brevity, descriptions of well-known functions and structures are omitted in the following description.

[0020] In the technical solution disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, application, and application of related data all comply with relevant laws, regulations, and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entry points for users to choose to authorize or refuse.

[0021] Typically, multiple cloud storage units belonging to the same type of business may be randomly distributed across the physical resource pools of a block storage system. In extreme scenarios, multiple cloud storage units for the same business may be distributed across a few physical resource pools, or even randomly distributed into the same physical resource pool. If one physical resource pool fails, all cloud storage units for the entire business will be affected.

[0022] Some embodiments of this disclosure provide a data backup method that uses a target object associated with a cloud storage unit as a basis. Based on a second mapping relationship between the target object and a physical resource pool, one or more cloud storage units associated with the same object can be distributed to the same physical resource pool according to preset distribution conditions. Cloud storage units associated with different target objects can be better isolated and dispersed by physical resources.

[0023] Figure 1 This is a schematic diagram illustrating an application scenario of a data backup method and apparatus according to an embodiment of this disclosure. It should be noted that... Figure 1 The examples shown are merely examples of system architectures that can be applied to the embodiments of this disclosure, in order to help those skilled in the art understand the technical content of this disclosure, but do not mean that the embodiments of this disclosure cannot be used in other devices, systems, environments or scenarios.

[0024] like Figure 1 As shown, the system architecture 100 according to this embodiment may include a cloud storage unit 101, a network 102, a server 103, and a migration service 104. The migration service 104 can obtain a mapping relationship between the cloud storage unit 101 and at least one of a deployment set, instance, host machine, and physical resource pool, and adjust this mapping relationship for data backup. The network 102 serves as a medium to provide a communication link between the cloud storage unit 101 and the server 103. The network 102 may include various connection types, such as wired and / or wireless communication links, etc.

[0025] For example, migration service 104 can adjust the mapping relationship between cloud storage unit 101 and physical resource pool to realize the migration of cloud storage unit 101 relative to the mapped physical resource pool. The data stored in the migrated cloud storage unit 101 is also migrated to the physical storage unit of the corresponding physical resource pool.

[0026] Server 103 can be a server that provides various services, such as migration services deployed on server 103. Server 103 can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud computing, network services, and middleware services.

[0027] It should be noted that the above are merely examples, and this disclosure is not limited thereto. The data backup method provided in the embodiments of this disclosure can generally be executed by server 103. Accordingly, the data backup device provided in the embodiments of this disclosure can generally be set in server 103. The data backup method provided in the embodiments of this disclosure can also be executed by a server or server cluster different from server 103. Accordingly, the data backup device provided in the embodiments of this disclosure can also be set in a server or server cluster different from server 103.

[0028] It should be understood that Figure 1 The number of cloud storage units, networks, and servers shown is merely illustrative. Depending on implementation needs, there can be any number of terminal devices, networks, and servers, as well as at least one of the following: deployment sets, instances, hosts, and physical resource pools (not shown).

[0029] Figure 2 This is a flowchart of a data backup method according to an embodiment of the present disclosure.

[0030] like Figure 2 As shown, the method 200 may include operations S210 to S240.

[0031] In operation S210, upon receiving a data backup request, a first mapping relationship between multiple cloud storage units and multiple physical resource pools is obtained. The cloud storage unit includes a virtual storage space for backing up business data of the target business, the physical resource pool includes multiple physical storage units for providing physical storage space for the mapped cloud storage units, and the target object includes a subject that can access the associated cloud storage unit.

[0032] For example, data backup requests can be generated in response to user actions or periodic data backup tasks. A physical resource pool, serving as a physical fault domain for a distributed block storage service, is a collection of physical hard drives that hosts the storage resources of cloud server instances.

[0033] In some embodiments, cloud server information can be periodically obtained from the console and management services of the front-end interface. This cloud server information includes the user's unique identifier, availability zone, deployment set's unique identifier, deployment dimension of the deployment set, unique identifier of the cloud server instance, unique identifier of the host machine corresponding to each cloud server instance, unique identifier of the cloud storage unit corresponding to each cloud server instance, and the type of each cloud storage unit. Based on this information, a first mapping relationship between the target object and the cloud storage unit can be obtained. Physical storage units may include hard drives (such as disks) that provide physical storage media. The management services include services for managing and controlling cloud components such as cloud server instances, switches, physical host machines, and cloud storage units.

[0034] In operation S220, in response to the first mapping relationship not satisfying the preset distribution conditions, a second mapping relationship between multiple target objects and multiple physical resource pools is determined to satisfy the preset distribution conditions. The preset distribution conditions characterize the distribution of cloud storage units mapped to each physical resource pool, and the target objects include the main body that can access the associated cloud storage units.

[0035] Target objects can include one or more cloud server instances, switches, and physical host machines. Preset distribution conditions indicate the quantity distribution of multiple cloud storage units mapped to various physical resource pools, the location of each cloud storage unit mapped to a physical resource pool, and other requirements. This allows for the deduction of how target objects associated with multiple cloud storage units should be distributed. The original quantity distribution and mapped physical resource pool locations can be obtained through the first mapping relationship and matched against the preset distribution conditions to determine if they are satisfied. Target objects can access associated cloud storage units and read / write data from the corresponding physical resource pools based on their read / write commands for the associated cloud storage units, thus forming a second mapping relationship.

[0036] For example, it can periodically determine whether preset distribution conditions are met. Compared to specifying resource pools when creating and mounting cloud storage units, this reduces complexity and the frequency of cloud storage unit migrations. For instance, when a cloud storage unit is remounted to another cloud server instance, the mapping relationship between the instance, cloud storage unit, and physical resource pool changes, requiring the backend cloud storage unit to migrate accordingly, increasing complexity and reducing system maintainability.

[0037] In operation S230, the first mapping relationship is adjusted based on the second mapping relationship and the association between multiple target objects and cloud storage units.

[0038] For example, the target object could be host machines, with a total of 10 host machines distributed across 5 physical resource pools in a 5:1:1:1:2 pattern. Based on preset distribution conditions, the cloud storage units associated with these host machines are migrated to 3 physical resource pools for a roughly even distribution. The number of host machines mapped to the first physical resource pool can be set to 4, the second to 3, and the third to 3, forming a second mapping relationship between 10 host machines and 3 physical resource pools in a 4:3:3 pattern. Then, for the first physical resource pool, a mapping is established for all associated cloud storage units of the 4 host machines it maps to. The same operation is performed for other physical resource pools, and will not be elaborated further.

[0039] In operation S240, data backup requests are processed using multiple target objects based on the adjusted first mapping relationship.

[0040] According to embodiments of this disclosure, based on the target object associated with the cloud storage unit, and according to the second mapping relationship between the target object and the physical resource pool, one or more cloud storage units associated with the same object can be distributed to the same physical resource pool according to preset distribution conditions, and cloud storage units associated with different target objects can be better isolated and dispersed by physical resources.

[0041] Figure 3This is a schematic diagram illustrating the mapping relationship between a cloud storage unit, a target object, and a physical resource pool according to an embodiment of this disclosure.

[0042] like Figure 3 As shown, target service 301 is allocated multiple cloud server instances. These multiple cloud server instances include first cloud server instance 302, second cloud server instance 303, and Nth cloud server instance 304. The first cloud server instance 302 can mount multiple cloud storage units, such as the first cloud storage unit 3021 and the Lth cloud storage unit 3022. The second cloud server instance can mount multiple cloud storage units, such as the second cloud storage unit 3031 and the Mth cloud storage unit 3032. The third cloud server instance can mount multiple cloud storage units, such as the third cloud storage unit 3041 and the Nth cloud storage unit 3042. The first cloud server instance can be mapped to the first host machine 305, and the second cloud server instance can also be mapped to the first host machine 305. The Nth cloud server instance can be mapped to the second host machine 306. The first cloud storage unit can be mapped to the first physical resource pool 307. The Lth cloud storage unit can be mapped to the second physical resource pool 308. The second cloud storage unit can be mapped to the first physical resource pool. The Mth cloud storage unit can be mapped to the third physical resource pool 309. The third cloud storage unit can be mapped to the third physical resource pool. The Nth cloud storage unit can be mapped to the second physical resource pool.

[0043] Depending on the business scale and disaster recovery requirements, the target business can deploy its cloud storage configuration according to the deployment set, which can include multiple cloud server instances. Figure 3 The mapping relationship shown illustrates that multiple cloud server instances can be configured on different host machines. Each host machine can map computing resources such as memory, CPU, and network bandwidth, and a preset number of cloud server instances can be mapped to a single host machine.

[0044] Figure 4 This is a schematic diagram illustrating the determination of a second mapping relationship according to an embodiment of the present disclosure.

[0045] In some embodiments, refer to Figure 4 Determining a second mapping relationship between multiple target objects and multiple physical resource pools to satisfy preset distribution conditions includes: obtaining the mapping distribution of multiple target objects relative to multiple physical resource pools, wherein the mapping distribution represents the distribution of target objects mapped to each physical resource pool; according to the association relationship 401, under the condition of satisfying preset distribution conditions 402, performing operation S410 to adjust the mapping distribution; and based on the adjusted mapping distribution, adjusting the target objects mapped to each physical resource pool to obtain the second mapping relationship 403.

[0046] For example, the host machine distribution was adjusted from 5, 1, 1, 1, 2 to 4, 3, 3.

[0047] According to embodiments of this disclosure, the distribution of target objects is first made to meet the requirements based on preset distribution conditions, thereby achieving effective isolation and dispersion of cloud storage units.

[0048] In some embodiments, adjusting the target objects mapped to each physical resource pool based on the adjusted mapping distribution to obtain a second mapping relationship includes: obtaining a first order of the number of target objects mapped to each physical resource pool according to the mapping distribution; obtaining a second order of the number of target objects mapped to each physical resource pool according to the adjusted mapping distribution; and adjusting the number of target objects mapped to each physical resource pool according to the first order until it is consistent with the second order.

[0049] For example, the deployment dimension is cloud server instances. Under this deployment dimension, there are 10 cloud server instances. The mapping distribution of these 10 cloud server instances to physical resource pools is as follows: the first physical resource pool maps to 5 cloud server instances, the second physical resource pool maps to 4 cloud server instances, and the third physical resource pool maps to 1 cloud server instance. The first order is 5, 4, 1, which does not meet the requirement of the preset distribution conditions that the target objects associated with the cloud storage units are roughly evenly distributed. Therefore, these 10 cloud server instances are redistributed. Thus, the second order of the adjusted mapping distribution is 4, 3, 3.

[0050] For example, if 10 cloud server instances are divided into 3 groups, with each group receiving 3 instances, there will be 1 instance remaining. Therefore, at most one physical resource pool can map 4 cloud server instances, while the other two physical resource pools each map 3 instances. Following the first order 5, 4, 1, the 5 cloud server instances mapped from the first physical resource pool are adjusted by moving one cloud storage unit away, resulting in the first physical resource pool mapping 4 instances. Then, the 4 cloud server instances mapped from the second physical resource pool are adjusted by moving one cloud storage unit away, resulting in the second physical resource pool mapping 3 instances. The remaining 3 instances are mapped from the third physical resource pool. This effectively reduces the number of migrations and lowers the consumption of computing resources.

[0051] In some embodiments, the data backup method further includes: obtaining multiple deployment dimensions of multiple target services, wherein the deployment dimensions indicate target objects for processing data backup requests; deploying target cloud storage units among multiple cloud storage units according to the associated target objects based on at least two of the multiple deployment dimensions, and deploying to form a mapping relationship between target cloud storage units and physical resource pools; generating multiple data backup requests based on the multiple deployment dimensions, wherein the deployment dimensions have preset distribution conditions.

[0052] For example, data backup requests can be generated periodically for each deployment dimension. These requests include preset distribution conditions to ensure that cloud storage units are sufficiently distributed across that deployment dimension. It's understood that the preset distribution conditions for each deployment dimension can vary between different data backup requests.

[0053] For example, each deployment set has a corresponding deployment dimension. A deployment dimension can be one or more, such as a host machine, a cloud server instance, or a switch. Assuming the deployment dimension is a host machine, its deployment parameters include a maximum of one concurrent instance on the host machine, meaning each host machine runs at most one cloud server instance. If the deployment dimension is a switch, the cloud server instances are distributed approximately evenly across different switches. Therefore, different preset distribution conditions can be determined for different deployment dimensions, ensuring that even when the same cloud storage unit belongs to different deployment sets, the host machines or switches mapped to those cloud storage units still meet their respective preset distribution conditions.

[0054] According to embodiments of this disclosure, if a single cloud storage unit is cross-mapped with target objects of different deployment dimensions, the cross-mapping situation can be fully considered to achieve distributed deployment of cloud storage units.

[0055] In some embodiments, adjusting the first mapping relationship based on the second mapping relationship and the association between multiple target objects and cloud storage units includes: adjusting the physical resource pool mapped to the target cloud storage unit based on at least one of at least two preset distribution conditions that satisfy at least two deployment dimensions as constraints.

[0056] For example, a cloud storage unit may be associated with a first deployment set, a second deployment set, and a third deployment set. The deployment dimensions of these three deployment sets may be the same or different. For instance, the deployment dimension of the first and second deployment sets may both be the host machine, while the deployment dimension of the third deployment set may be the switch. Or, the deployment dimension of the first and third deployment sets may both be the host machine, while the deployment dimension of the second deployment set may be the switch. Based on the mapping relationship between the deployment sets, the cloud storage unit, and the physical resource pool, as well as the above multiple deployment dimensions, multiple preset distribution conditions can be determined. These preset distribution conditions (i.e., constraints) can be adjusted by the physical resource pool mapped to the target cloud storage unit.

[0057] According to embodiments of this disclosure, it is advantageous to meet the distribution requirements of at least one cloud storage unit among multiple preset distribution conditions.

[0058] In some embodiments, adjusting the physical resource pool mapped to the target cloud storage unit includes: obtaining target distribution conditions that simultaneously satisfy at least two preset distribution conditions; adjusting a second mapping relationship based on the target distribution conditions when the preset distribution conditions and the target distribution conditions are inconsistent; and adjusting the physical resource pool mapped to the target cloud storage unit based on the adjusted second mapping relationship and the associated relationship.

[0059] For example, different deployment dimensions have their corresponding preset distribution conditions. Although the preset distribution conditions in this embodiment are met—for example, 10 cloud server instances are distributed in 3 physical resource pools in the order of 4, 3, 3—it's possible that the target cloud storage unit mapped to one of the 10 cloud server instances is mapped to the first physical resource pool. The preset distribution conditions for other deployment dimensions might be, for example, 8 cloud server instances distributed in 3 physical resource pools in the order of 2, 3, 3, with the target cloud storage unit mapped to the third physical resource pool. Therefore, the cloud server instance associated with the target cloud storage unit can be adjusted to be mapped to the third physical resource pool, and another cloud server instance mapped to the third physical resource pool can be moved to be mapped to the first physical resource pool.

[0060] According to embodiments of this disclosure, if a cross-mapping situation is encountered, a target distribution condition that is compatible with multiple preset distribution conditions is recalculated to simultaneously satisfy multiple preset distribution conditions, which is beneficial to improving disaster recovery capabilities.

[0061] In some embodiments, adjusting the physical resource pool mapped to the target cloud storage unit includes: obtaining at least two priorities of at least two preset distribution conditions; and, if the preset distribution condition has the highest priority, adjusting the physical resource pool mapped to the target cloud storage unit based on a second mapping relationship and an association relationship.

[0062] In some embodiments, if the preset distribution conditions do not have the highest priority, the second mapping relationship is adjusted based on the preset distribution conditions with the highest priority; based on the adjusted second mapping relationship and the association relationship, the physical resource pool mapped to the target cloud storage unit is adjusted.

[0063] For example, there are cross-mappings between multiple deployment sets, multiple cloud storage units, and multiple physical resource pools. Suppose that deployment set A is associated with cloud server instance A, cloud server instance B, and cloud server instance C, and deployment set B manages cloud server instance B, cloud server instance C, and cloud server instance D. Different cloud server instances may correspond to different deployment dimensions or the same deployment dimension. The same cloud server instance in different deployment sets may correspond to the same or different deployment dimensions. This makes it possible for cloud storage units to be associated with the same or different deployment dimensions, resulting in different preset distribution conditions to be met.

[0064] For example, you can first determine the priorities of multiple deployment sets, then the deployment dimensions, preset distribution conditions, and their respective deployment sets will have corresponding priorities. Priority indicates the degree to which preset distribution conditions need to be met. It's possible to determine which preset distribution condition should be satisfied first, based on the priority order of the deployment dimensions.

[0065] According to embodiments of this disclosure, efficiency and high availability can be improved, and data backup of relatively important target business data can be prioritized in complex mapping relationships.

[0066] Figure 5 This is a schematic diagram of the architecture of a data backup method according to an embodiment of the present disclosure.

[0067] The compute deployment set, host machine, and instances all have many-to-many relationships. Adding storage-side considerations further increases the maintenance complexity of the management service 502. Furthermore, the mapping relationship between cloud storage units, instances, and physical resource pools is not static. Besides creation, remounting to different instances and changes in cloud storage unit types will alter the mapping relationship. Maintaining this mapping relationship would significantly impact the mounting and unmounting of the existing management service and changes in cloud storage unit types. Large code modifications could also lead to frequent data migrations. Therefore, a separate cloud storage unit migration service 503, independent of management service 502, is set up for maintenance.

[0068] like Figure 5 As shown, the cloud storage unit migration service 503 periodically retrieves the association relationship 504 between the target object and the cloud storage unit from the console 501, such as the user's unique identifier, availability zone, deployment set unique identifier, deployment set dimension, instance unique identifier within the deployment set, host machine unique identifier corresponding to the instance, cloud storage unit unique identifier corresponding to the instance, and cloud storage unit type. Through this information, the mapping relationship between deployment sets, instances, hosts, and cloud storage units can be obtained.

[0069] The cloud storage unit migration service 503 obtains the first mapping relationship 505 between the physical resource pool and the cloud storage unit from the management and control service 502, and determines the migration strategy that matches the association relationship 504 and the first mapping relationship 505 from the candidate strategy set 506 in the cloud storage unit migration service 503. That is, it determines the second mapping relationship between multiple target objects and multiple physical resource pools to meet the preset distribution conditions to obtain the target strategy 507. Then, it executes operation S520, and according to the target strategy, the cloud storage unit migration service 503 requests the management and control service 502 to adjust the first mapping relationship of the cloud storage unit based on the second mapping relationship and the association relationship between multiple target objects and the cloud storage unit.

[0070] First, operation S510 can be executed to configure migration strategies, such as a deployment set migration strategy for a specific user. This can include a unique user identifier, migration dimension (used to specify the type of target object, such as host machine or instance), and number of groups (used to determine the number of physical resource pools). Subsequently, this strategy can be selected as the target strategy for a specific user, and then the second mapping relationship is determined based on the target strategy. The migration dimension is also used to constrain the distribution of cloud storage units corresponding to the host machine or instance to the same physical resource pool. This allows for better physical resource isolation and distribution of cloud storage units from different host machines or instances. The number of groups indicates the minimum number of resource pools to which they are distributed. For example, a deployment set strategy for a specific cloud storage unit type can include the cloud storage unit type, migration dimension (host machine or instance), and number of groups. Multiple migration strategies form a candidate strategy set 506, from which a target strategy 507 (used to determine the type of target object and the second mapping relationship) can be selected.

[0071] For example, a customer's target business uses a standard 3-replica cloud storage unit, but has extremely high requirements for data high availability and reliability. They also implement multi-replica redundancy at the application layer. As long as less than half of the cloud server instances' associated cloud storage units are functioning correctly, the business can operate normally. For this type of scenario, the target strategy can include the customer's unique identifier, with the migration dimension being cloud server instances and the grouping number being 3. Subsequently, based on the second mapping relationship, the cloud storage units associated with the cloud server instances within the deployment set will be distributed across at least 3 physical resource pools. Even if one physical resource pool fails, it will not affect the user's business.

[0072] In another example, consider an elastic temporary disk, a single-replica cloud storage unit with no data redundancy. It offers high elasticity, low cost, and high performance, but suffers from poor data reliability due to its single-replica nature. The customer's configured compute deployment set dimension is at least at the host level. For this type of cloud storage unit, the target strategy can include the cloud storage unit type of the elastic temporary disk, the migration dimension as the host, and the number of groups as all physical resource pools. In this case, the host cloud storage units within the deployment set will be distributed across multiple single-replica physical resource pools as much as possible. If the user's compute deployment set dimension is a switch, the cloud storage units can be further distributed across multiple switch single-replica physical resource pools.

[0073] According to embodiments of this disclosure, cloud storage units associated with different host machines or cloud server instances can be better physically isolated and dispersed to avoid the problem of business interruption due to the failure of a host machine or a cloud server instance.

[0074] Figure 6 This is a timing diagram of a data backup method according to an embodiment of the present disclosure.

[0075] like Figure 6As shown, based on the target business 601, the cloud storage unit migration service 603 performs operation S610, configuring a migration strategy according to the target business; then, operation S620 is performed to start and parse the configuration, so that the cloud storage unit migration service 603 can obtain a set of candidate strategies; next, operation S630 is performed to periodically obtain cloud server deployment set information from the console 604. The cloud server deployment set information may include the user's unique identity, availability zone, deployment set unique identity, deployment dimension of the deployment set, unique identity of the cloud server instance, unique identity of the host machine corresponding to each cloud server instance, unique identity of the cloud storage unit corresponding to each cloud server instance, and the type of each cloud storage unit; based on this cloud server information, the mapping relationship between the cloud storage unit and the target object (host machine or cloud server instance) can be obtained, and operation S640 is performed to classify the migration strategies according to the cloud server information, and the target strategy is subsequently determined based on the classification results. For example, if the cloud storage unit type is an elastic temporary cloud storage unit, the migration strategy classification includes the strategy corresponding to this type of cloud storage unit. Similarly, if the user is a large customer, the migration strategy classification should include the strategy corresponding to the large customer's business. Then, operation S650 is executed to obtain the first mapping relationship between the physical resource pool and the cloud storage unit from the management service 602. Based on the mapping relationship obtained from the cloud server information and the management service, the target strategy is determined (i.e., operation S660 is executed). The second mapping relationship is determined according to the target strategy, and subsequently, the management service is requested to migrate the cloud storage unit to the corresponding physical resource pool (i.e., operation S670 is executed). Finally, operation S680 is executed, the cloud storage unit migration service prints logs, and the client corresponding to the target business 601 executes operation S690 to check the logs to determine whether the cloud storage unit has been correctly migrated according to the target strategy.

[0076] For example, the distribution of cloud storage units before and after migration (or dismantling) can be shown to customers to enhance intuitiveness. Moreover, migration services, cloud servers, instances, and host machines can be flexibly restarted and upgraded, avoiding the problem of large-scale code modifications affecting system stability. In addition, the periodic migration of cloud storage units disclosed in this publication reduces various problems caused by frequent migrations.

[0077] By acquiring deployment set information of cloud servers, the high availability requirements of the business can be perceived, and the corresponding cloud storage units can then be distributed across physical resource pools. Furthermore, by employing an asynchronous migration solution implemented through a migration service independent of the management service, it is possible to avoid significant modifications to the management service code that could impact system stability. Additionally, periodic processing prevents the frequent triggering of data migration for cloud storage units in scenarios where different cloud server instances are frequently unmounted and remounted.

[0078] Figure 7This is a block diagram of a data backup apparatus according to an embodiment of the present disclosure.

[0079] like Figure 7 As shown, the data backup device 700 may include an acquisition module 710, a determination module 720, an adjustment module 730, and a processing module 740.

[0080] The acquisition module 710 can perform operation S210, which, upon receiving a data backup request, acquires a first mapping relationship between multiple cloud storage units and multiple physical resource pools. The cloud storage unit includes a virtual storage space for backing up business data of the target business, and the physical resource pool includes multiple physical storage units for providing physical storage space for the mapped cloud storage units.

[0081] The determination module 720 can perform operation S220 to determine a second mapping relationship between multiple target objects and multiple physical resource pools to satisfy the preset distribution conditions in response to the first mapping relationship not satisfying the preset distribution conditions. The preset distribution conditions characterize the distribution of cloud storage units mapped to each physical resource pool, and the target objects include the main body that can access the associated cloud storage units.

[0082] The adjustment module 730 can perform operation S230 to adjust the first mapping relationship based on the second mapping relationship and the association between multiple target objects and cloud storage units.

[0083] The processing module 740 can perform operation S240 to process data backup requests using multiple target objects based on the adjusted first mapping relationship.

[0084] In some embodiments, the determining module 720 may also be used to obtain the mapping distribution of multiple target objects relative to multiple physical resource pools, wherein the mapping distribution characterizes the distribution of target objects mapped to each physical resource pool; adjust the mapping distribution according to the association relationship and under the condition of satisfying a preset distribution condition; and adjust the target objects mapped to each physical resource pool based on the adjusted mapping distribution to obtain a second mapping relationship.

[0085] In some embodiments, the determining module 720 may further be used to obtain a first order of the number of target objects mapped to each physical resource pool according to the mapping distribution; obtain a second order of the number of target objects mapped to each physical resource pool according to the adjusted mapping distribution; and adjust the number of target objects mapped to each physical resource pool according to the first order until it is consistent with the second order.

[0086] In some embodiments, the data backup device 700 may further include: a deployment dimension module, configured to acquire multiple deployment dimensions of multiple target services, wherein the deployment dimensions indicate the target objects for processing data backup requests; a deployment module, configured to deploy target cloud storage units among multiple cloud storage units according to the associated target objects based on at least two of the multiple deployment dimensions, and to deploy a mapping relationship between the target cloud storage units and the physical resource pool; and a request generation module, configured to generate multiple data backup requests based on the multiple deployment dimensions, wherein the deployment dimensions have preset distribution conditions.

[0087] In some embodiments, the adjustment module 730 can also be used to adjust the physical resource pool mapped to the target cloud storage unit based on at least one of at least two preset distribution conditions that satisfy at least two deployment dimensions as constraints.

[0088] In some embodiments, the adjustment module 730 can also be used to obtain target distribution conditions that simultaneously satisfy at least two preset distribution conditions; if the preset distribution conditions and target distribution conditions are inconsistent, adjust the second mapping relationship based on the target distribution conditions; and adjust the physical resource pool mapped to the target cloud storage unit based on the adjusted second mapping relationship and the association relationship.

[0089] In some embodiments, the adjustment module 730 can also be used to obtain at least two priorities of at least two preset distribution conditions; if the preset distribution condition has the highest priority, adjust the physical resource pool mapped to the target cloud storage unit based on the second mapping relationship and the association relationship. Alternatively, if the preset distribution condition does not have the highest priority, adjust the second mapping relationship based on the preset distribution condition with the highest priority; adjust the physical resource pool mapped to the target cloud storage unit based on the adjusted second mapping relationship and the association relationship.

[0090] For any parts not mentioned in the apparatus section, please refer to the various embodiments of the above method for understanding. That is, the apparatus section includes modules for performing each step of any of the method embodiments described above. Furthermore, the implementation methods, technical problems solved, functions achieved, and technical effects of each module / unit / subunit in the apparatus section embodiments are the same as or similar to the implementation methods, technical problems solved, functions achieved, and technical effects of the corresponding steps in the method section embodiments, and will not be repeated here.

[0091] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0092] According to an embodiment of the present disclosure, an electronic device includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the method described above.

[0093] According to embodiments of the present disclosure, a non-transitory computer-readable storage medium stores computer instructions, wherein the computer instructions are used to cause a computer to perform the methods described above.

[0094] According to an embodiment of this disclosure, a computer program product includes a computer program that, when executed by a processor, implements the method described above.

[0095] Figure 8 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0096] like Figure 8 As shown, the electronic device 800 includes a computing unit 801, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 802 or a computer program loaded from a storage unit 808 into a random access memory (RAM) 803. The RAM 803 may also store various programs and data required for the operation of the device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via a bus 804. An input / output (I / O) interface 805 is also connected to the bus 804.

[0097] Multiple components in electronic device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of displays, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows electronic device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0098] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as task processing methods. For example, in some embodiments, the task processing method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program may be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the task processing method described above may be performed. Alternatively, in other embodiments, the computing unit 801 may be configured to perform a data backup method by any other suitable means (e.g., by means of firmware).

[0099] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0100] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0101] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0102] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0103] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0104] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, distributed system servers, or servers incorporating blockchain technology.

[0105] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0106] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A data backup method, comprising: Upon receiving a data backup request, a first mapping relationship is obtained between multiple cloud storage units and multiple physical resource pools, wherein the cloud storage unit includes a virtual storage space for backing up business data of the target business, and the physical resource pool includes multiple physical storage units for providing physical storage space for the mapped cloud storage units; In response to the first mapping relationship not satisfying the preset distribution conditions, a second mapping relationship between multiple target objects and the multiple physical resource pools is determined to satisfy the preset distribution conditions. The preset distribution conditions characterize the distribution of cloud storage units mapped to each physical resource pool. The target objects include entities that can access associated cloud storage units. Based on the second mapping relationship and the association between the multiple target objects and the cloud storage unit, the first mapping relationship is adjusted; The data backup request is processed using multiple target objects based on an adjusted first mapping relationship, including: distributing one or more cloud storage units associated with the same target object to the same physical resource pool according to the preset distribution conditions, so as to physically isolate and disperse cloud storage units associated with different target objects. The step of determining the second mapping relationship between the multiple target objects and the multiple physical resource pools to satisfy the preset distribution conditions includes: Obtain the mapping distribution of multiple target objects relative to the multiple physical resource pools, wherein the mapping distribution characterizes the distribution of target objects mapped to each physical resource pool; Based on the aforementioned correlation, the mapping distribution is adjusted while satisfying the preset distribution conditions. Based on the adjusted mapping distribution, the target objects mapped to each physical resource pool are adjusted to obtain the second mapping relationship.

2. The method according to claim 1, wherein, The step of adjusting the target objects mapped to each physical resource pool based on the adjusted mapping distribution to obtain the second mapping relationship includes: Based on the mapping distribution, obtain the first order of the number of target objects mapped to each physical resource pool; Based on the adjusted mapping distribution, a second order is obtained of the number of target objects mapped to each physical resource pool; Adjust the number of target objects mapped to each physical resource pool according to the first order until it matches the second order.

3. The method according to claim 1, wherein, The method further includes: Obtain multiple deployment dimensions of the target services, wherein the deployment dimensions indicate the target objects used to process the data backup requests; Based on at least two of the multiple deployment dimensions, target cloud storage units among the multiple cloud storage units are deployed according to the associated target objects, and the deployment is used to form a mapping relationship between the target cloud storage units and the physical resource pool; Multiple data backup requests are generated based on the multiple deployment dimensions, and the deployment dimensions have the preset distribution conditions.

4. The method according to claim 3, wherein, The adjustment of the first mapping relationship based on the second mapping relationship and the association between the multiple target objects and the cloud storage unit includes: Based on at least one of at least two preset distribution conditions satisfying the at least two deployment dimensions as constraints, the physical resource pool mapped to the target cloud storage unit is adjusted.

5. The method according to claim 4, wherein, The adjustment of the physical resource pool mapped to the target cloud storage unit includes: Obtain the target distribution condition that simultaneously satisfies at least two of the preset distribution conditions; If the preset distribution conditions and the target distribution conditions are inconsistent, the second mapping relationship is adjusted based on the target distribution conditions; Based on the adjusted second mapping relationship and the aforementioned association relationship, the physical resource pool mapped to the target cloud storage unit is adjusted.

6. The method according to claim 4, wherein, The adjustment of the physical resource pool mapped to the target cloud storage unit includes: Obtain at least two priorities of the at least two preset distribution conditions; When the preset distribution conditions have the highest priority, the physical resource pool mapped to the target cloud storage unit is adjusted based on the second mapping relationship and the association relationship.

7. The method according to claim 6, wherein, The adjustment of the physical resource pool mapped to the target cloud storage unit includes: If the preset distribution condition does not have the highest priority, the second mapping relationship is adjusted based on the preset distribution condition with the highest priority. Based on the adjusted second mapping relationship and the aforementioned association relationship, the physical resource pool mapped to the target cloud storage unit is adjusted.

8. A data backup device, comprising: The acquisition module is used to acquire a first mapping relationship between multiple cloud storage units and multiple physical resource pools when a data backup request is received. The cloud storage unit includes a virtual storage space for backing up business data of the target business, and the physical resource pool includes multiple physical storage units for providing physical storage space for the mapped cloud storage units. A determination module is configured to, in response to the first mapping relationship not satisfying the preset distribution conditions, determine a second mapping relationship between multiple target objects and the multiple physical resource pools to satisfy the preset distribution conditions, wherein the preset distribution conditions characterize the distribution of cloud storage units mapped to each physical resource pool, and the target objects include entities that can access associated cloud storage units; An adjustment module is used to adjust the first mapping relationship based on the second mapping relationship and the association relationship between the multiple target objects and the cloud storage unit; The processing module is used to process the data backup request based on the adjusted first mapping relationship using multiple target objects, including: distributing one or more cloud storage units associated with the same target object to the same physical resource pool according to the preset distribution conditions, so as to physically isolate and disperse the cloud storage units associated with different target objects. The determining module is further configured to: obtain the mapping distribution of multiple target objects relative to multiple physical resource pools, wherein the mapping distribution characterizes the distribution of target objects mapped to each physical resource pool; Based on the correlation, the mapping distribution is adjusted while meeting the preset distribution conditions; Based on the adjusted mapping distribution, the target objects mapped to each physical resource pool are adjusted to obtain the second mapping relationship.

9. An electronic device, comprising: 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 to enable the at least one processor to perform the method of any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium storing computer instructions for causing the computer to perform the method of any one of claims 1 to 7.

11. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 7.

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