Cluster resource balancing method and device, electronic equipment and storage medium

Abstract

The invention provides a cluster resource balancing method and device, electronic equipment and a storage medium, and relates to the technical field of cloud vehicle machines. The method comprises the following steps: grouping a plurality of user instances according to relevancy to determine instance groups, and distributing an initial cluster for each instance group; and when the number of the instances in any instance group is greater than the maximum concurrency number of the initial cluster, grouping the user instances in the instance groups again, and respectively distributing a corresponding cluster for each instance group which is grouped again. Through a resource balance scheduling mechanism, the user instances in the instance group before secondary grouping can be scattered and distributed to other clusters located in the same machine room as the initial cluster or clusters located in different machine rooms from the initial cluster, high concurrency of the instances concentrated in a certain cluster is avoided, load balance between different clusters is ensured, and user experience is improved. The use requirements of the user on cloud resources are met, and the resource load of each cluster is utilized to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the field of cloud vehicle machine technology, and in particular to a cluster resource balancing method, device, electronic device and storage medium. Background Art

[0002] With the rapid development of Internet of Vehicles technology, more and more car companies have begun to focus on the creation of car-machine applications, upgrading car-machines from traditional means of transportation to intelligent third spaces. Whether car-machines can provide more, richer, and smarter applications has become an important indicator for users to evaluate vehicle performance.

[0003] However, due to the limited storage capacity of the car hardware, a large number of applications cannot be installed on the car at the same time; in addition, the limitation of computing resources makes it easy for large games to run on the car, which makes it difficult to run smoothly. Therefore, migrating services to the cloud has become an inevitable choice. Considering cost control, the user's cloud resources (usually provided by server clusters) cannot be expanded infinitely. When using the cloud, users will still be affected by the lack of cloud resources, resulting in a poor user experience. Summary of the invention

[0004] The problem solved by the present invention is how to meet the user's demand for using cloud resources.

[0005] In order to solve the above problems, the present invention provides a cluster resource balancing method, device, electronic device and storage medium.

[0006] In a first aspect, the present invention provides a cluster resource balancing method, comprising:

[0007] Grouping multiple user instances according to relevance to determine instance groups, and assigning an initial cluster to each instance group;

[0008] When the number of instances in any of the instance groups is greater than the maximum concurrency of the initial cluster, the user instances in the instance group are grouped again, and a corresponding cluster is allocated to each of the regrouped instance groups.

[0009] Optionally, grouping the multiple user instances according to the relevance to determine the instance group includes:

[0010] Calculate the relevance of the user instances according to the streaming time corresponding to the user instances, and determine the relevance between the multiple user instances;

[0011] The user instances are grouped according to the maximum concurrency number and the relevance to determine the instance group.

[0012] Optionally, calculating the correlation of the user instances according to the streaming time points corresponding to the user instances to determine the correlation between the multiple user instances includes:

[0013] The correlation between the multiple user instances is determined according to the co-occurrence frequency of the multiple user instances at the same streaming moment within a preset time period.

[0014] Optionally, grouping the user instances according to the maximum concurrency number and the relevance includes:

[0015] Determine the maximum number of instances that the initial cluster can handle according to the maximum concurrency number;

[0016] The user instances are grouped according to the relevance between the user instances and the maximum number of instances.

[0017] Optionally, grouping the user instances according to the relevance between the user instances and the maximum number of instances includes:

[0018] Sorting the user instances according to the relevance between the user instances;

[0019] The user instances sorted by relevance are grouped in sequence according to the maximum number of instances.

[0020] Optionally, the grouping the user instances in the instance group again includes:

[0021] The threshold of the correlation is adjusted to adjust the upper limit of the number of instances of the instance grouping.

[0022] Optionally, assigning a corresponding cluster to each regrouped instance group includes:

[0023] The instance groups obtained after the re-grouping are distributed to other clusters in the same computer room as the initial cluster, or clusters in different computer rooms from the initial cluster.

[0024] In a second aspect, the present invention provides a cluster resource balancing device, comprising:

[0025] The first module is used to group multiple user instances according to relevance to determine instance groups, and assign an initial cluster to each instance group;

[0026] The second module is used for grouping the user instances in the instance group again when the number of instances in any of the instance groups is greater than the maximum concurrency number of the initial cluster, and respectively assigning a corresponding cluster to each of the regrouped instance groups.

[0027] In a third aspect, the present invention provides an electronic device, including a memory and a processor;

[0028] The memory is used to store computer programs;

[0029] The processor is used to implement the cluster resource balancing method as described in the first aspect when executing the computer program.

[0030] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the cluster resource balancing method as described in the first aspect is implemented.

[0031] The beneficial effect of the cluster resource balancing method of the present invention is: after grouping user instances according to relevance, an initial cluster is assigned to the instance group to ensure that the user instances in each group can share similar resources and are scheduled through the resources in the initial cluster. Then, when high concurrency occurs, for example, the number of instances in any instance group is greater than the maximum concurrency of the initial cluster, the cluster resources may be overloaded. At this time, the user instances in the instance group are grouped again through the resource balancing scheduling mechanism, and the corresponding cluster is assigned to each instance group after the second grouping. The user instances in the instance group before the second grouping can be scattered and assigned to other clusters in the same computer room as the initial cluster, or clusters in different computer rooms from the initial cluster, to avoid high concurrency of instances concentrated in a certain cluster, ensure load balancing between different clusters, meet users' demand for cloud resource usage, and maximize the utilization of the resource load of each cluster, avoid excessive congestion of individual clusters, and cause performance degradation. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A schematic diagram of a cluster resource balancing method according to an embodiment of the present invention;

[0033] Figure 2 A schematic diagram of a computer room and a cluster according to an embodiment of the present invention;

[0034] Figure 3 Schematic diagram of the process of grouping user instances according to an embodiment of the present invention Figure 1 ;

[0035] Figure 4 Schematic diagram of the process of grouping user instances according to an embodiment of the present invention Figure 2 ;

[0036] Figure 5 Schematic diagram of the process of grouping user instances according to an embodiment of the present invention Figure 3 ;

[0037] Figure 6A system architecture diagram of a cluster resource balancing device according to an embodiment of the present invention;

[0038] Figure 7 4 is a system architecture diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0039] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. Although certain embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be interpreted as being limited to the embodiments described herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present invention. It should be understood that the drawings and embodiments of the present invention are only for exemplary purposes and are not intended to limit the scope of protection of the present invention.

[0040] It should be understood that the various steps described in the method embodiments of the present invention may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present invention is not limited in this respect.

[0041] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to"; the term "based on" means "based at least in part on"; the term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments"; the term "optionally" means "optional embodiments". The relevant definitions of other terms will be given in the following description. It should be noted that the concepts of "first", "second", etc. mentioned in the present invention are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0042] It should be noted that the modifications of "one" and "plurality" mentioned in the present invention are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0043] The names of the messages or information exchanged between multiple devices in the embodiments of the present invention are only used for illustrative purposes, and are not used to limit the scope of these messages or information.

[0044] like Figure 1 As shown, a cluster resource balancing method provided by an embodiment of the present invention includes:

[0045] S100: Grouping multiple user instances according to relevance to determine instance groups, and assigning an initial cluster to each instance group.

[0046] Specifically, multiple user instances are grouped according to the correlation between them. For example, the greater the frequency of co-occurrence of streaming in the same period, the higher the correlation, ensuring that instances in the same instance group have certain similarities in resource requirements. For example, user instances in each group can share similar resources and are scheduled through the resources in the initial cluster to meet the resource requirements of user instances.

[0047] S200: When the number of instances in any of the instance groups is greater than the maximum concurrency of the initial cluster, the user instances in the instance group are grouped again, and a corresponding cluster is allocated to each of the regrouped instance groups.

[0048] Specifically, in high concurrency situations, cluster resources may be overloaded, especially when a large number of instances are concentrated in a certain cluster, which can easily lead to the cluster's resources being fully utilized, causing performance bottlenecks. Therefore, when the number of instances in any instance group is greater than the maximum concurrency of the initial cluster (the resource load is close to full load), the user instances in the instance group are grouped again, and a corresponding cluster is assigned to each instance group after the second grouping. For example, the user instances in the strength group before the second grouping can be scattered and assigned to other clusters in the same computer room as the initial cluster, or to clusters in different computer rooms from the initial cluster. That is, through the resource balancing scheduling mechanism, high concurrency of instances concentrated in a certain cluster is avoided. The amount of resources occupied by user instances in each group in the cluster load is relatively controllable, ensuring load balancing between different clusters, meeting users' demand for cloud resources, and maximizing the utilization of the resource load of each cluster, avoiding excessive congestion of individual clusters and resulting in performance degradation.

[0049] Combination Figure 2 As shown, taking the initial cluster Shenzhen 01-1 cluster as an example, other clusters can be the same computer room, that is, Shenzhen 01-3 cluster of Zone (01 computer room), or different computer rooms, that is, x cluster or y cluster of Zone (xx computer room).

[0050] Among them, the setting of the maximum concurrency is based on the capabilities of the resource system, which usually includes hardware resources (such as network bandwidth, CPU performance, storage capacity, etc.) and software resources (such as load balancing, scheduling strategy, etc.); network bandwidth is an important factor affecting the maximum concurrency. Each user instance usually consumes a certain amount of bandwidth (depending on the amount of network activity of the instance, such as data transmission, API requests, etc.). Therefore, if the network bandwidth is limited, the number of concurrencies that the cluster can carry will be limited; the processing power of each user instance depends on CPU resources, and high-concurrency requests require sufficient CPU processing power to ensure response speed and system stability; each instance consumes a certain amount of storage resources during operation, including disk space, etc. The bottleneck of storage resources may limit the number of instances that can run concurrently in the cluster, especially in high-data read and write scenarios, such as database operations, big data analysis, etc., the limitations of storage performance and capacity are more significant.

[0051] In this embodiment, after user instances are grouped according to relevance, an initial cluster is assigned to the instance group to ensure that user instances in each group can share similar resources and are scheduled through the resources in the initial cluster. Then, when high concurrency occurs, for example, the number of instances in any instance group is greater than the maximum concurrency of the initial cluster, the cluster resources may be overloaded. At this time, the user instances in the instance group are grouped again through the resource balancing scheduling mechanism, and a corresponding cluster is assigned to each instance group after the re-grouping. User instances that exceed the carrying capacity of the initial cluster can be allocated to other clusters in the same computer room as the initial cluster, or to clusters in different computer rooms from the initial cluster, to avoid high concurrency of instances concentrated in a certain cluster, ensure load balancing between different clusters, meet users' demand for cloud resource usage, and maximize the utilization of the resource load of each cluster, avoid excessive congestion of individual clusters, and cause performance degradation.

[0052] Optionally, grouping the multiple user instances according to the relevance to determine the instance group includes:

[0053] S110: Calculate the relevance of the user instances according to the streaming time corresponding to the user instances, and determine the relevance between the multiple user instances.

[0054] Specifically, combined Figure 3 As shown in the figure, relevance refers to the similarity between different user instances. The similarity is usually based on historical behavior patterns, such as the push time and resource usage pattern of the user instance.

[0055] S120: Grouping the user instances according to the maximum concurrency number and the relevance to determine the instance grouping.

[0056] Specifically, combined Figure 3As shown, when grouping, the user instances can be divided into several groups according to certain rules based on the relevance sorting results of the user instances. The goal is to ensure that the user instances in the same group have similar resource requirements so that they can effectively share cluster resources. Taking instances 1 to 5 as examples, the relevance between instance 1 and instance 2 is 0.95, the relevance between instance 1 and instance 3 is 0.90, the relevance between instance 1 and instance 4 is 0.80, and the relevance between instance 1 and instance 5 is 0.75. The order after relevance sorting can be: [instance 1, instance 2], [instance 1, instance 3], [instance 1, instance 4], [instance 1, instance 5]. The maximum number of concurrent instances can support three instances, so instance 1, instance 2 and instance 3 can be divided into the first group (high relevance, similar resource requirements), instance 4 can be divided into the second group, and instance 5 can be divided into the third group. If the relevance between instance 4 and instance 5 meets the conditions, instance 4 and instance 5 can also be divided into the same group.

[0057] In this optional embodiment, the correlation of the user instances is calculated according to the streaming time corresponding to the user instances, so that user instances with similar resource requirements can be found and reasonably allocated to different clusters through grouping strategies, thereby achieving load balancing and efficient resource utilization.

[0058] Optionally, calculating the correlation of the user instances according to the streaming time points corresponding to the user instances to determine the correlation between the multiple user instances includes:

[0059] The correlation between the multiple user instances is determined according to the co-occurrence frequency of the multiple user instances at the same streaming moment within a preset time period.

[0060] Specifically, for example, at time t1, instance 1, instance 2, and instance 3 co-appear, at time t2, instance 1, instance 3, and instance 5 co-appear, and at time t3, instance 1, instance 2, and instance 5 co-appear. Then the co-occurrence frequency of instance 1 and instance 2 is 2 times, the co-occurrence frequency of instance 1 and instance 3 is 2 times, the co-occurrence frequency of instance 1 and instance 5 is 2 times, the co-occurrence frequency of instance 2 and instance 5 is 1 time, and the co-occurrence frequency of instance 3 and instance 5 is 1 time. Therefore, the correlation between multiple user instances can be calculated by combining the streaming time and the co-occurrence frequency.

[0061] In this optional embodiment, the correlation between multiple user instances is determined according to the co-occurrence frequency of the multiple user instances at the same streaming moment, so that user instances with similar resource requirements can be accurately found.

[0062] Optionally, grouping the user instances according to the maximum concurrency number and the relevance includes:

[0063] S121: Determine the maximum number of instances that the initial cluster can handle according to the maximum concurrency number.

[0064] Specifically, combined Figure 4 As shown in the figure, the maximum concurrency number represents the maximum number of instances that the initial cluster can handle. Therefore, the upper limit of the number of instances in a group can be determined based on the maximum concurrency number.

[0065] S122: Grouping the user instances according to the correlation between the user instances and the maximum number of instances.

[0066] Specifically, combined Figure 4 As shown, the correlation between instance 1 and instance 2 is 0.95, which meets the valid grouping interval condition and is placed in the same group. The correlation between instance 1 and instance 3 is 0.90, which meets the valid grouping interval condition and instance 3 is also placed in the current group. There are three instances in the current group (instance 1, instance 2, instance 3); the correlation between instance 1 and instance 4 is 0.80, and the correlation between instance 1 and instance 5 is 0.75. Since the correlation threshold is 0.85, instance 4 and instance 5 are placed in different groups respectively.

[0067] In this optional embodiment, after determining the maximum number of instances that the initial cluster can handle based on the maximum concurrency, the user instances are grouped based on the relevance between the user instances and the maximum number of instances to ensure efficient and balanced distribution of the user instances in the cluster.

[0068] Optionally, grouping the user instances according to the relevance between the user instances and the maximum number of instances includes:

[0069] S1221: Sort the user instances by relevance according to the relevance between the user instances.

[0070] Specifically, combined Figure 5 As shown in FIG. 1 , the push time is usually used as the basis for relevance sorting. User instances with higher relevance are usually more similar in resource requirements, and lower relevance indicates that the resource requirements of the two user instances are quite different.

[0071] S1222: Grouping the user instances sorted by relevance in sequence according to the maximum number of instances.

[0072] Specifically, combined Figure 5 As shown, for example, the order after relevance sorting can be: [instance 1, instance 2], [instance 1, instance 3], [instance 1, instance 4], [instance 1, instance 5], and the maximum number of instances is 3. Then, instance 1, instance 2 and instance 3 can be divided into the first group, instance 4 can be divided into the second group, and instance 5 can be divided into the third group.

[0073] In this optional embodiment, user instances with similar resource requirements can be found through relevance sorting, thereby achieving rapid grouping.

[0074] Optionally, the grouping the user instances in the instance group again includes:

[0075] The threshold of the correlation is adjusted to adjust the upper limit of the number of instances of the instance grouping.

[0076] Specifically, the upper limit of the number of instances in the instance grouping is adjusted by adjusting the relevance threshold to ensure efficient and balanced distribution of user instances in the cluster.

[0077] In this optional embodiment, by adjusting the relevance threshold to adjust the upper limit of the number of instances in the instance grouping, the load requirements of different clusters can be flexibly responded to while maintaining efficient resource utilization to ensure stability and scalability under high concurrency conditions.

[0078] Optionally, assigning a corresponding cluster to each regrouped instance group includes:

[0079] The instance groups obtained after the re-grouping are distributed to other clusters in the same computer room as the initial cluster, or clusters in different computer rooms from the initial cluster.

[0080] Specifically, combined Figure 2 As shown, taking the initial cluster as Shenzhen 01-1 cluster as an example, other clusters can be the same computer room, that is, Shenzhen 01-3 cluster of Zone (01 computer room), or different computer rooms, that is, x cluster or y cluster of Zone (xx computer room), so the instance group can be assigned to Shenzhen 01-3 cluster, x cluster or y cluster.

[0081] In the related art, due to the physical limitations of the original computer room, the data synchronization requirements between different computer rooms cannot be met (data information cannot be shared or used in common). This embodiment can support the simultaneous operation of multiple computer rooms, ensuring that different computer rooms can share cloud resources and can perform effective data synchronization. User instances can be used normally in different clusters / computer rooms.

[0082] In this optional embodiment, by providing resources for user instances through other clusters in the same computer room or clusters in different computer rooms, it is possible to flexibly respond to the load requirements of different clusters while maintaining efficient resource utilization, ensuring stability and scalability under high concurrency conditions.

[0083] like Figure 6 As shown, a cluster resource balancing device 600 provided in an embodiment of the present invention includes:

[0084] The first module 610 is used to group multiple user instances according to relevance to determine instance groups, and assign an initial cluster to each instance group;

[0085] The second module 620 is configured to group the user instances in the instance group again when the number of instances in any of the instance groups is greater than the maximum concurrency number of the initial cluster, and assign a corresponding cluster to each of the regrouped instance groups.

[0086] like Figure 7 As shown, an electronic device 700 provided by an embodiment of the present invention includes a memory 720 and a processor 710; the memory 720 is used to store a computer program; the processor 710 is used to implement the cluster resource balancing method as described above when executing the computer program.

[0087] In other words, an electronic device 700 includes a memory 720 and a processor 710 coupled to the memory 720; the memory 720 is configured to store a computer program; and the processor 710 is configured to perform the following operations when executing the computer program:

[0088] Grouping multiple user instances according to relevance to determine instance groups, and assigning an initial cluster to each instance group;

[0089] When the number of instances in any of the instance groups is greater than the maximum concurrency of the initial cluster, the user instances in the instance group are grouped again, and a corresponding cluster is allocated to each of the regrouped instance groups.

[0090] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the cluster resource balancing method described above is implemented.

[0091] In other words, a non-volatile computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor performs the following operations:

[0092] Grouping multiple user instances according to relevance to determine instance groups, and assigning an initial cluster to each instance group;

[0093] When the number of instances in any of the instance groups is greater than the maximum concurrency of the initial cluster, the user instances in the instance group are grouped again, and a corresponding cluster is allocated to each of the regrouped instance groups.

[0094] An electronic device 700 that can be used as a server or client of the present invention will now be described, which is an example of a hardware device that can be applied to various aspects of the present invention. The electronic device 700 is intended to represent various forms of digital electronic computer equipment, such as laptop computers, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device 700 can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples, and are not intended to limit the implementation of the present invention described herein and / or required.

[0095] The electronic device 700 includes a computing unit, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) or a computer program loaded from a storage unit into a random access memory (RAM). In the RAM, various programs and data required for the operation of the device can also be stored. The computing unit, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0096] A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment method can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM), etc. In the present application, the unit described as a separate component may or may not be physically separated, and the component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or it may be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present invention. In addition, each functional unit in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of a software functional unit.

[0097] Although the present invention is disclosed as above, the protection scope of the present invention is not limited thereto. Those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and these changes and modifications will fall within the protection scope of the present invention.

Claims

1. A cluster resource balancing method, characterized in that: include: Grouping multiple user instances according to relevance to determine instance groups, and assigning an initial cluster to each instance group; When the number of instances in any of the instance groups is greater than the maximum concurrency of the initial cluster, the user instances in the instance group are grouped again, and a corresponding cluster is allocated to each of the regrouped instance groups.

2. The cluster resource balancing method according to claim 1, characterized in that: The step of grouping multiple user instances according to the relevance to determine the instance grouping comprises: Calculate the relevance of the user instances according to the streaming time corresponding to the user instances, and determine the relevance between the multiple user instances; The user instances are grouped according to the maximum concurrency number and the relevance to determine the instance group.

3. The cluster resource balancing method according to claim 2, characterized in that: The calculating the correlation of the user instances according to the streaming time corresponding to the user instances to determine the correlation between the multiple user instances includes: The correlation between the multiple user instances is determined according to the co-occurrence frequency of the multiple user instances at the same streaming moment within a preset time period.

4. The cluster resource balancing method according to claim 2, characterized in that: The grouping of the user instances according to the maximum concurrency number and the relevance comprises: Determine the maximum number of instances that the initial cluster can handle according to the maximum concurrency number; The user instances are grouped according to the relevance between the user instances and the maximum number of instances.

5. The cluster resource balancing method according to claim 4, characterized in that: The grouping the user instances according to the relevance between the user instances and the maximum number of instances comprises: Sorting the user instances according to the relevance between the user instances; The user instances sorted by relevance are grouped in sequence according to the maximum number of instances.

6. The cluster resource balancing method according to claim 1, characterized in that: The regrouping of the user instances in the instance grouping comprises: The threshold of the correlation is adjusted to adjust the upper limit of the number of instances of the instance grouping.

7. The cluster resource balancing method according to claim 1, characterized in that: The step of assigning a corresponding cluster to each of the regrouped instance groups comprises: The instance groups obtained after the re-grouping are distributed to other clusters in the same computer room as the initial cluster, or clusters in different computer rooms from the initial cluster.

8. A cluster resource balancing device, characterized in that: include: The first module is used to group multiple user instances according to relevance to determine instance groups, and assign an initial cluster to each instance group; The second module is used for grouping the user instances in the instance group again when the number of instances in any of the instance groups is greater than the maximum concurrency number of the initial cluster, and respectively assigning a corresponding cluster to each of the regrouped instance groups.

9. An electronic device, characterized in that: including memory and processor; The memory is used to store computer programs; The processor is used to implement the cluster resource balancing method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by a processor, the cluster resource balancing method according to any one of claims 1 to 7 is implemented.

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