CN114490086A - Method, device, electronic equipment, medium and program product for dynamically adjusting resources - Google Patents

Abstract

The present disclosure provides a resource dynamic adjustment method, apparatus, electronic device, medium and program product for a multi-cluster architecture. The above method and apparatus can be used in the fields of cloud computing and financial technology. The method includes: acquiring a deployment request of an application for a container resource, wherein the container resource requested to be deployed includes n containers, each container has a container feature, and n is an integer greater than or equal to 1; and count the resource usage information of the s clusters, Among them, s is an integer greater than or equal to 2, and each cluster has cluster characteristics; compare the container resources requested for deployment with the resource usage information of the s clusters, to select at least one cluster from the s clusters, and use the at least one cluster as the The cluster is actually deployed; the physical deployment area of the host corresponding to the actual deployment cluster is determined according to the actual deployment cluster; and the physical resources are dynamically adjusted according to the physical deployment area.

Description

Resource dynamic adjustment method, apparatus, electronic device, medium and program product

technical field

The present disclosure relates to the technical field of cloud computing, and more particularly, to a method, apparatus, electronic device, computer-readable storage medium, and computer program product for dynamic resource adjustment in a multi-cluster architecture.

Background technique

With the transformation of IT architecture, more and more businesses are deployed in cloud clusters, and various companies and institutions have built private clouds and completed the pooling design of physical resources. The pooling design greatly improves resource utilization, but also causes resources management difficulties. In addition, the single-cluster private cloud architecture no longer meets the requirements due to restrictions on the diversity of network types, the grayscale of cluster versions, or the expansion of the company's business scale. Considering the difference in resource usage status of each cluster and the diversity of cluster attributes, and considering that the private cloud architecture does not have the concept of tenants, the application for each user's container resources should comprehensively consider the resource usage of the entire private cloud, so as to provide user containers. A reasonable deployment scope for the purpose.

SUMMARY OF THE INVENTION

In view of this, the present disclosure provides a resource dynamic adjustment method, apparatus, electronic device, computer-readable storage medium and computer program product for a multi-cluster architecture that are managed in an orderly manner and facilitate dynamic expansion.

One aspect of the present disclosure provides a method for dynamically adjusting resources for a multi-cluster architecture, including: acquiring a deployment request of an application for a container resource, wherein the container resource requested to be deployed includes n containers, each of which has a container feature, n is an integer greater than or equal to 1; count the resource usage information of s clusters, where s is an integer greater than or equal to 2, and each cluster has cluster characteristics; compare the container resources requested to be deployed with the s resource usage information of each cluster, so as to select at least one cluster from the s clusters, and use the at least one cluster as the actual deployment cluster; determine the physical properties of the host corresponding to the actual deployment cluster according to the actual deployment cluster a deployment area; and dynamically adjusting physical resources according to the physical deployment area.

The comparing the container resources requested to be deployed and the resource usage information of the s clusters to select at least one cluster from the s clusters, and use the at least one cluster as the actual deployment cluster, specifically includes: Match the container characteristics of each container in the container resources requested to be deployed with the cluster characteristics of the s clusters; take the t clusters that are successfully matched as pre-deployed clusters, where t is greater than or equal to 1 and less than or equal to s and selecting at least one cluster from the pre-deployed clusters according to the requested deployment container resources and resource usage information of the pre-deployed clusters, and using the at least one cluster as an actual deployment cluster.

According to the method for dynamically adjusting resources for a multi-cluster architecture according to an embodiment of the present disclosure, based on the multi-cluster architecture, the application details of applications for container resources are monitored in real time, and according to the resource usage information of the current s clusters, the application for deployment is analyzed in real time. The scope of the cluster in which the container resources should be deployed. If the resources are not satisfied, the physical resources of the physical resource pool are dynamically called for host application and management, so as to achieve the purpose of automatic and dynamic expansion. In addition, the method can manage and allocate cluster resources and physical deployment areas in an orderly manner, and can solve the problems in the prior art that cluster resources are chaotic and management and application are incoherent.

In some embodiments, the resource usage information of each of the clusters includes used resources and unused resources, the used resources include historical container deployment situations of the application, and the statistical resource usage information of the s clusters includes : Count the affinity score of the application to each of the s clusters according to the historical container deployment situation of the application; and according to the unused resources of each of the s clusters, give each Cluster scores to get resource scores.

In some embodiments, at least one cluster is selected from the pre-deployed clusters according to the requested deployment container resources and resource usage information of the pre-deployed clusters, and the at least one cluster is used as the actual deployment cluster, which specifically includes: : calculate the weighted score of the t pre-deployed clusters according to the affinity score and the resource score; and select at least one cluster from the t pre-deployed clusters according to the weighted score, and select the at least one cluster from the t pre-deployed clusters. A cluster as the actual deployment cluster.

In some embodiments, the determining, according to the actual deployment cluster, the physical deployment area of the host corresponding to the actual deployment cluster includes: generating a host feature according to the requested deployment container resource; Resource characteristics, wherein the physical resource pool includes k construction areas, each of the construction areas has the physical resource characteristics, and k is an integer greater than or equal to 1; the host characteristics and the k construction areas are combined Perform feature matching on the physical resource features of the The physical deployment area of the host.

In some embodiments, the determining, according to the pre-built area, the physical deployment area of the host corresponding to the actual deployment cluster includes: acquiring area feature information of each pre-build area; The pre-built areas are scored to obtain a regional score; and according to the regional score, at least one of the g pre-built areas is selected as the physical deployment area of the host corresponding to the actual deployment cluster.

In some embodiments, the area feature information includes at least one of optional IP numbers, CPU resource usage, memory resource usage, and physical storage resource usage.

In some embodiments, scoring each pre-built area according to the area feature information, and obtaining the area score includes: assigning the optional IP number, the CPU resource usage rate, the memory resource usage rate The physical storage resource utilization rate and the physical storage resource usage rate are respectively scored to obtain a plurality of regional characteristic scores; and a weighted summation of the plurality of regional characteristic scores is obtained to obtain a regional score.

In some embodiments, the host machine characteristics include at least one of a corresponding network region, central processing unit architecture, cloud type, central processing machine size, memory size, and physical storage size; and/or, the physical resource characteristics Include at least one of corresponding network area, central processing unit architecture, cloud type, central processing machine size, memory size, and physical storage size.

In some embodiments, the container feature includes at least one of a network area corresponding to the container, a network feature, a CPU architecture and a platform version on which the cluster is deployed; and/or, the cluster feature includes a network area corresponding to the cluster, At least one of network characteristics, central processing unit architecture, and platform version.

In some embodiments, acquiring the deployment request of the application for the container resource includes: classifying the container resource requested to be deployed according to the container feature; The matching of the cluster features of the s clusters includes: matching the container features of each container in the classified container resources with the features of the s clusters.

In some embodiments, the acquiring the deployment request for the container resource by the application further includes: unifying the specifications of the n containers.

Another aspect of the present disclosure provides a resource dynamic adjustment device for a multi-cluster architecture, including: an acquisition module, the acquisition module is configured to execute a deployment request for acquiring an application for container resources, wherein the container resources requested to be deployed include: n containers, each container has container characteristics, n is an integer greater than or equal to 1; a statistics module, the statistics module is used to perform statistics on resource usage information of s clusters, where s is an integer greater than or equal to 2, Each of the clusters has cluster characteristics; a comparison module is configured to perform a comparison between the container resources requested to be deployed and the resource usage information of the s clusters, so as to select at least one cluster from the s clusters a cluster, using the at least one cluster as an actual deployment cluster; a determining module, the determining module is configured to determine the physical deployment area of the host corresponding to the actual deployment cluster according to the actual deployment cluster; and an adjustment module, the The adjustment module is configured to perform dynamic adjustment of physical resources according to the physical deployment area.

Wherein, the comparison module specifically includes: a matching unit, the matching unit is configured to perform matching between the container feature of each container in the container resources requested to be deployed and the cluster feature of the s clusters; a command unit, the The command unit is used to execute the t clusters that are matched successfully as pre-deployed clusters, where t is an integer greater than or equal to 1 and less than or equal to s; and a selection unit, the selection unit is used to execute the container resources deployed according to the request and resource usage information of the pre-deployed clusters, at least one cluster is selected from the pre-deployed clusters, and the at least one cluster is used as the actual deployment cluster.

Another aspect of the present disclosure provides an electronic device including one or more processors and one or more memories, wherein the memories are used to store executable instructions, the executable instructions being executed by the processor When executed, the method described above is implemented.

Another aspect of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, which when executed, are used to implement the method as described above.

Another aspect of the present disclosure provides a computer program product comprising a computer program comprising computer-executable instructions that, when executed, are used to implement the method as described above.

Description of drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the following description of embodiments of the present disclosure with reference to the accompanying drawings, in which:

FIG. 1 schematically shows an exemplary system architecture to which methods and apparatuses according to embodiments of the present disclosure can be applied;

FIG. 2 schematically shows a flowchart of a method for dynamic resource adjustment in a multi-cluster architecture according to an embodiment of the present disclosure;

FIG. 3 schematically shows a flow chart of counting resource usage information of s clusters according to an embodiment of the present disclosure;

FIG. 4 schematically illustrates comparing the container resources requested to be deployed and the resource usage information of s clusters according to an embodiment of the present disclosure, so as to select at least one cluster from the s clusters, and use the at least one cluster as the actual deployment cluster. flow chart;

FIG. 5 schematically shows a process of selecting at least one cluster from the pre-deployed clusters and using the at least one cluster as the actual deployment cluster according to the container resources deployed on request and the resource usage information of the pre-deployed clusters according to an embodiment of the present disclosure picture;

6 schematically shows a flowchart of determining a physical deployment area of a host machine corresponding to an actual deployment cluster according to an actual deployment cluster according to an embodiment of the present disclosure;

7 schematically shows a flowchart of determining a physical deployment area of a host machine corresponding to an actual deployment cluster according to a pre-built area according to an embodiment of the present disclosure;

8 schematically shows a flow chart of scoring each pre-built area according to the area feature information to obtain the area score according to an embodiment of the present disclosure;

FIG. 9 schematically shows a flow chart of acquiring a deployment request of an application for a container resource according to an embodiment of the present disclosure;

FIG. 10 schematically shows a flowchart of matching the container feature of each container in the container resource requested to be deployed with the cluster feature of s clusters according to an embodiment of the present disclosure;

FIG. 11 schematically shows a structural block diagram of an apparatus for dynamic resource adjustment for a multi-cluster architecture according to an embodiment of the present disclosure;

FIG. 12 schematically shows a structural block diagram of a comparison module according to an embodiment of the present disclosure;

FIG. 13 schematically shows a structural diagram of an apparatus for dynamic resource adjustment according to an embodiment of the present disclosure;

FIG. 14 schematically shows an internal structure diagram of an application details statistics device according to an embodiment of the present disclosure;

FIG. 15 schematically shows an internal structure diagram of a cluster resource statistics apparatus according to an embodiment of the present disclosure;

FIG. 16 schematically shows an internal structure diagram of an application resource analysis device according to an embodiment of the present disclosure;

FIG. 17 schematically shows an internal structure diagram of a device for applying for capacity expansion according to an embodiment of the present disclosure;

FIG. 18 schematically shows a block diagram of an electronic device according to an embodiment of the present disclosure.

Detailed ways

Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. It should be understood, however, that these descriptions are exemplary only, and are not intended to limit the scope of the present disclosure. In the following detailed description, for convenience of explanation, numerous specific details are set forth in order to provide a thorough understanding of the embodiments of the present disclosure. It will be apparent, however, that one or more embodiments may be practiced without these specific details. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concepts of the present disclosure.

In the technical solution of the present disclosure, the acquisition, storage and application of the user's personal information involved all comply with the relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good customs. In the technical solution of the present disclosure, the acquisition, collection, storage, use, processing, transmission, provision, disclosure and application of data are all in compliance with the provisions of relevant laws and regulations, and necessary confidentiality measures have been taken, and do not violate public order and good customs.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the present disclosure. The terms "comprising", "comprising" and the like as used herein indicate the presence of stated features, steps, operations and/or components, but do not preclude the presence or addition of one or more other features, steps, operations or components.

Where expressions like "at least one of A, B, or C, etc.," are used, they should generally be interpreted in accordance with the meaning of the expression as commonly understood by those skilled in the art (eg, "has A, B, or C, etc." At least one of the "systems" shall include, but not be limited to, systems with A alone, B alone, C alone, A and B, A and C, B and C, and/or A, B, C, etc. ). The terms "first" and "second" are only used for descriptive purposes, and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second" may expressly or implicitly include one or more of said features.

With the transformation of IT architecture, more and more businesses are deployed in cloud clusters, and various companies and institutions have built private clouds and completed the pooling design of physical resources. The pooling design greatly improves resource utilization, but also causes resources management difficulties. For example, the fast elastic scaling capability provided by the cloud platform provides strong support for dealing with sudden traffic growth scenarios. However, with the reduction of traffic or the decrease of application scale, the statistics and reuse of idle resources after the reduction will be reduced. It's a resource management problem.

In addition, the single-cluster private cloud architecture no longer meets the requirements due to restrictions on the diversity of network types, the grayscale of cluster versions, or the expansion of the company's business scale. Considering the difference in resource usage of each cluster and the diversity of cluster attributes, and considering that the private cloud architecture does not have the concept of tenants, the application for each user's container resources should comprehensively consider the resource usage of the entire private cloud, so as to give the user container For the purpose of a reasonable deployment range, statistical analysis across multiple clusters will be another major challenge in resource management.

Embodiments of the present disclosure provide a resource dynamic adjustment method, apparatus, electronic device, computer-readable storage medium, and computer program product for a multi-cluster architecture. A resource dynamic adjustment method for a multi-cluster architecture includes: acquiring a deployment request of an application for a container resource, wherein the container resource requested to be deployed includes n containers, each container has container characteristics, and n is an integer greater than or equal to 1; statistics s resource usage information of each cluster, where s is an integer greater than or equal to 2, and each cluster has cluster characteristics; compare the container resources requested for deployment with the resource usage information of s clusters to select at least one cluster from s clusters , using the at least one cluster as the actual deployment cluster; determining the physical deployment area of the host machine corresponding to the actual deployment cluster according to the actual deployment cluster; and dynamically adjusting the physical resources according to the physical deployment area.

Wherein, comparing the container resources requested to be deployed and the resource usage information of the s clusters, to select at least one cluster from the s clusters, and use the at least one cluster as the actual deployment cluster, which specifically includes: The container characteristics of each container are matched with the cluster characteristics of s clusters; the t clusters that are successfully matched are used as pre-deployed clusters, where t is an integer greater than or equal to 1 and less than or equal to s; and the container resources deployed according to the request and Resource usage information of the pre-deployed cluster, at least one cluster is selected from the pre-deployed cluster, and the at least one cluster is used as the actual deployment cluster.

It should be noted that the method, apparatus, electronic device, computer-readable storage medium and computer program product for dynamic adjustment of resources for multi-cluster architecture of the present disclosure can be used in the field of cloud computing technology, and can also be used in the field of cloud computing technology. In any field, such as the financial field, the field of the present disclosure is not limited here.

1 schematically illustrates an exemplary system architecture 100 to which a resource dynamic adjustment method, apparatus, electronic device, computer-readable storage medium, and computer program product for a multi-cluster architecture may be applied according to an embodiment of the present disclosure. It should be noted that FIG. 1 is only an example of a system architecture to which the embodiments of the present disclosure can be applied, so as to help those skilled in the art to understand the technical content of the present disclosure, but it does not mean that the embodiments of the present disclosure cannot be used for other A device, system, environment or scene.

As shown in FIG. 1 , the system architecture 100 according to this embodiment may include terminal devices 101 , 102 , and 103 , a network 104 and a server 105 . The network 104 is a medium used to provide a communication link between the terminal devices 101 , 102 , 103 and the server 105 . The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages and the like. Various communication client applications may be installed on the terminal devices 101 , 102 and 103 , such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only examples).

The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop computers, desktop computers, and the like.

The server 105 may be a server that provides various services, such as a background management server (just an example) that provides support for websites browsed by users using the terminal devices 101 , 102 , and 103 . The background management server can analyze and process the received user requests and other data, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal device.

It should be noted that, the method for dynamic resource adjustment for a multi-cluster architecture provided by the embodiments of the present disclosure may generally be executed by the server 105 . Correspondingly, the resource dynamic adjustment apparatus for the multi-cluster architecture provided by the embodiment of the present disclosure may generally be set in the server 105 . The resource dynamic adjustment method for the multi-cluster architecture provided by the embodiment of the present disclosure may also be executed by a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101 , 102 , 103 and/or the server 105 . Correspondingly, the resource dynamic adjustment apparatus for the multi-cluster architecture provided by the embodiments of the present disclosure may also be set in a server or server cluster that is different from the server 105 and can communicate with the terminal devices 101 , 102 , 103 and/or the server 105 .

It should be understood that the numbers of terminal devices, networks and servers in FIG. 1 are merely illustrative. There can be any number of terminal devices, networks and servers according to implementation needs.

Based on the scenario described in FIG. 1 , the following will describe in detail the method for dynamic resource adjustment for a multi-cluster architecture according to an embodiment of the present disclosure with reference to FIGS. 2 to 11 .

FIG. 2 schematically shows a flowchart of a method for dynamic resource adjustment in a multi-cluster architecture according to an embodiment of the present disclosure.

As shown in FIG. 2 , the method for dynamic resource adjustment for a multi-cluster architecture in this embodiment includes operations S210 to S250.

In operation S210, a deployment request for a container resource by an application is obtained, wherein the container resource requested to be deployed includes n containers, each container has a container characteristic, and n is an integer greater than or equal to 1.

In operation S220, the resource usage information of s clusters is counted, where s is an integer greater than or equal to 2, and each cluster has cluster characteristics.

As a possible implementation method, the resource usage information of each cluster includes used resources and unused resources, and the used resources include the historical container deployment situation of the application. It can be understood that before receiving the deployment request of the application, s Containers may have been deployed on each cluster, and the deployed containers can be understood as the historical containers mentioned above. The historical containers may be deployed by the application that made the request, or by other applications other than the application that made the request. Here No specific limitation is made.

As shown in FIG. 3 , the operation S220 to count the resource usage information of the s clusters may include operation S221 and operation S222.

In operation S221, the affinity score of the application to each of the s clusters is counted according to the historical container deployment situation of the application. The statistics of the affinity score of the application to each of the s clusters according to the historical container deployment situation of the application can be understood as the statistics of the affinity score of the application to each cluster according to the number of historical containers deployed in each cluster. In other words, the number of historical containers deployed by an application in a cluster can be counted. The more historical containers deployed, the higher the affinity between the application and a cluster, and the higher the affinity score. For example, application 1 has deployed 3 historical containers on cluster a, and application 1 has deployed 1 historical container on cluster b. Therefore, it is determined that application 1 has a high affinity with cluster a, and application 1 has a high affinity with cluster a. The sex score is greater than the affinity score for cluster b with application 1.

In operation S222, each cluster is scored according to the unused resources of each of the s clusters to obtain a resource score. Among them, the unused resources can be understood as the situation that the historical container is not deployed on the cluster. The more resources that are not deployed in the historical container, that is, the more unused resources, the higher the resource score.

Through operation S221 and operation S222, it is convenient to implement statistics of resource usage information of s clusters to obtain an affinity score and a resource score.

In operation S230, the container resources requested to be deployed are compared with resource usage information of the s clusters, so as to select at least one cluster from the s clusters, and use the at least one cluster as the actual deployment cluster.

As a possible implementation manner, the container feature may include at least one of the network area corresponding to the container, the network feature, the CPU architecture and platform version on which the cluster is deployed; and/or the cluster feature may include the network area corresponding to the cluster, At least one of network characteristics, central processing unit architecture, and platform version. Wherein, the network area can be an internal network or an external network, but is not limited to this; the network feature can be a local area network or a wide area network, but is not limited to this; the platform version can be the k8s version, etc., but is not limited to this.

As a possible implementation manner, as shown in FIG. 4 , operation S230 compares the container resources requested to be deployed with the resource usage information of the s clusters, so as to select at least one cluster from the s clusters, and use the at least one cluster as the actual cluster. Deploying the cluster specifically includes operations S231 to S233.

In operation S231, the container characteristic of each container in the container resource requested to be deployed is matched with the cluster characteristic of the s clusters.

In operation S232, the t clusters that are successfully matched are used as pre-deployed clusters, where t is an integer greater than or equal to 1 and less than or equal to s.

In operation S233, at least one cluster is selected from the pre-deployed clusters according to the container resources requested to be deployed and the resource usage information of the pre-deployed clusters, and the at least one cluster is used as the actual deployment cluster.

It can be understood that both the container feature and the cluster feature may include one or more elements, the container feature element and the cluster feature element are in one-to-one correspondence, and each element may be matched one by one.

For example, each container feature may include four elements, which are the network area corresponding to the container, network characteristics, and the central processing unit architecture and platform version of the deployed cluster. Correspondingly, each cluster feature may include four elements, which are the network area corresponding to the cluster, network characteristics, and the architecture and platform version of the central processor where the cluster is deployed.

For further example, the network area of container 1 is an internal network, the network feature is a local area network, the architecture of the central processor for deploying the cluster is X86, and the platform version is the k8s version. The network area of cluster a is the external network, the network characteristic is WAN, the CPU architecture of the cluster is X86, and the platform version is the k8s version; the network area of cluster b is the internal network, the network characteristic is the local area network, and the CPU of the cluster is deployed. The architecture is X86, and the platform version is the k8s version; the network area of cluster c is the internal network, and the network feature is the local area network. The central processor architecture of the cluster is X86, and the platform version is the k8s version.

Match the four elements of container 1 with the four elements of cluster a, respectively match the four elements of container 1 with the four elements of cluster b, and match the four elements of container 1 with the four elements of cluster c. , thus, cluster b and cluster c that are successfully matched with the characteristics of container 1 can be used as pre-deployed clusters of container 1.

As a possible implementation manner, as shown in FIG. 5 , operation S233 selects at least one cluster from the pre-deployed clusters according to the requested deployment container resource and the resource usage information of the pre-deployed cluster, and uses the at least one cluster as the actual deployment The cluster specifically includes operations S2331 and S2332.

In operation S2331, the weighted scores of the t pre-deployed clusters are calculated according to the affinity score and the resource score. It can be understood that the affinity score of the application to each cluster has been calculated in operation S221. Here, if the application has not deployed a history container on a certain cluster, the affinity score of the application to the cluster can be Record it as 0. Thus, the affinity score of the application for each pre-deployed cluster can be obtained, and the resource score of each pre-deployed cluster can be obtained by operation S222, and then each pre-deployed cluster can be calculated according to the affinity score and the resource score. The weighted score of the pre-deployed cluster. The weighted score can be understood as weighting the affinity score and the resource score respectively to obtain the affinity weighted score and the resource weighted score, and then calculate the sum of the affinity weighted score and the resource weighted score. .

In operation S2332, according to the weighted score, at least one cluster is selected from the t pre-deployed clusters, and the at least one cluster is used as the actual deployment cluster. Wherein, selecting at least one cluster from the t pre-deployed clusters according to the weighted score, and taking the at least one cluster as the actual deployment cluster can be understood as determining one or more actual deployment clusters according to the ranking of the weighted scores. In other words, after obtaining the weighted score of each pre-deployed cluster, multiple pre-deployed clusters can be sorted. Here, the sorting can be in ascending order or descending order. According to the sorting order, r pre-deployment clusters with high to low scores are selected. The deployment cluster is used as the actual deployment cluster, and r is an integer greater than or equal to 1 and less than or equal to t, so the cluster where the application can actually deploy containers can be obtained.

Through operations S2331 and S2332, it is convenient to implement the container resources deployed according to the request and the resource usage information of the pre-deployed clusters, select at least one cluster from the pre-deployed clusters, and use the at least one cluster as the actual deployment cluster.

In operation S240, the physical deployment area of the host machine corresponding to the actual deployment cluster is determined according to the actual deployment cluster.

As an implementable manner, as shown in FIG. 6 , the operation S240 to determine the physical deployment area of the host machine corresponding to the actual deployment cluster according to the actual deployment cluster includes operations S241 to S245 .

In operation S241, a host feature is generated according to the container resource requested to be deployed.

In operation S242, physical resource characteristics of the physical resource pool are acquired, wherein the physical resource pool includes k construction areas, each construction area has a physical resource characteristic, and k is an integer greater than or equal to 1.

In operation S243, feature matching is performed between the host machine feature and the physical resource features of the k build areas.

As some specific examples, the host machine characteristics may include at least one of corresponding network region, central processing unit architecture, cloud type, central processing machine size, memory size, and physical storage size; and/or physical resource characteristics may include corresponding At least one of network area, central processing unit architecture, cloud type, central processing machine size, memory size, and physical storage size.

In operation S244, the g construction areas with successful feature matching are used as pre-construction areas, where g is an integer greater than or equal to 1 and less than k.

It can be understood that each host feature and each physical resource feature may include one or more elements, the host feature elements and the physical resource feature elements are in one-to-one correspondence, and each element may be matched one by one.

For example, each host characteristic may include six elements, namely network area, CPU architecture, cloud type, CPU size, memory size, and physical storage size. Correspondingly, each physical resource characteristic may include six elements, namely network area, central processing unit architecture, cloud type, central processing machine size, memory size, and physical storage size. After matching each element of each host feature with each element of each physical resource feature one by one, g construction areas that are successfully matched with the applied host characteristics can be obtained, and then the g construction areas can be used as the applied host. pre-built area.

In operation S245, the physical deployment area of the host machine corresponding to the actual deployment cluster is determined according to the pre-built area.

As a possible implementation manner, as shown in FIG. 7 , the operation S245 to determine the physical deployment area of the host machine corresponding to the actual deployment cluster according to the pre-built area includes operations S2451 to S2453 .

In operation S2451, area feature information of each pre-built area is acquired. In some specific examples, the area characteristic information may include at least one of an optional IP number, a CPU resource usage rate, a memory resource usage rate, and a physical storage resource usage rate.

In operation S2452, each pre-built region is scored according to the region feature information to obtain a region score.

In some specific examples, as shown in FIG. 8 , operation S2452 scores each pre-built area according to the area feature information, and obtaining the area score includes operation S24521 and operation S24522.

In operation S24521, the number of optional IPs, the CPU resource utilization rate, the memory resource utilization rate, and the physical storage resource utilization rate are respectively scored to obtain a plurality of regional characteristic scores.

In operation S24522, a weighted summation of a plurality of regional feature scores is performed to obtain a regional score.

It should be noted that the larger the number of optional IPs, the higher the score; the lower the CPU resource usage, the higher the score; the lower the memory resource usage, the higher the score; the lower the physical storage resource usage, the higher the score high. After scoring, you can weight the score of the optional IP number (that is, the regional feature score), weight the score of the CPU resource utilization rate (that is, the regional feature score), and score the memory resource utilization rate. The weighted sum of the score (that is, the regional feature score) weights the scoring score (that is, the regional feature score) of the physical storage resource utilization rate. Regional score.

Through the operations S24521 and S24522, it is convenient to score each pre-built area according to the area characteristic information, and obtain the area score.

In operation S2453, according to the area score, at least one of the g pre-built areas is selected as the physical deployment area of the host corresponding to the actual deployment cluster. Among them, according to the area score, selecting at least one of the g pre-built areas as the physical deployment area of the host corresponding to the actual deployment cluster can be understood as selecting at least one of the g pre-build areas as the actual deployment area according to the sorting of the area scores. The physical deployment area of the host corresponding to the deployment cluster. It should be noted that the sorting can be in ascending order or descending order. According to the sorting order, f pre-built areas with regional scores from high to low can be selected as physical deployment areas, where f is an integer greater than or equal to 1 and less than or equal to g.

Therefore, through operations S2451 to S2453, it can be facilitated to more accurately and efficiently realize that the physical deployment area of the host corresponding to the actual deployment cluster is determined according to the pre-built area.

In operation S250, the physical resources are dynamically adjusted according to the physical deployment area.

According to the method for dynamically adjusting resources for a multi-cluster architecture according to an embodiment of the present disclosure, based on the multi-cluster architecture, the application details of applications for container resources are monitored in real time, and according to the resource usage information of the current s clusters, the application for deployment is analyzed in real time. The scope of the cluster in which the container resources should be deployed. If the resources are not satisfied, the physical resources of the physical resource pool are dynamically called for host application and management, so as to achieve the purpose of automatic and dynamic expansion. In addition, through operations S210 to S250, cluster resources and physical deployment areas can be managed and allocated in an orderly manner, which can solve the problems in the prior art that cluster resources are chaotic and management and application are incoherent.

According to some embodiments of the present disclosure, as shown in FIG. 9 , operation S210 to obtain a deployment request of an application for container resources includes operation S211 : classifying the container resources requested to be deployed according to container characteristics. For example, each container characteristic can include four elements, namely network area, network characteristics, CPU architecture and platform version of the deployed cluster. When the four elements of the two containers are the same, the two containers are classified into one class.

For further example, suppose there are 5 containers applying for deployment, the network area of container 1 is the internal network, the network characteristic is the local area network, the CPU architecture of the deployment cluster is X86, and the platform version is the k8s version; the network area of container 2 is the internal network, The network feature is WAN, the CPU architecture of the cluster is X86, and the platform version is the k8s version; the network area of container 3 is the internal network, the network feature is the local area network, the CPU architecture of the cluster is X86, and the platform version is the k8s version ; The network area of container 4 is the external network, the network characteristic is the local area network, the CPU architecture of the cluster is X86, and the platform version is the k8s version; the network area of the container 5 is the external network, the network characteristic is the local area network, and the central processing unit of the cluster is deployed. The server architecture is X86, and the platform version is the k8s version.

Comparing the network area, network characteristics, CPU architecture and platform version of the deployment cluster of the above five containers respectively, it can be found that the network area, network characteristics, CPU architecture and platform version of the deployment cluster are the same for container 1 and container 3. , so container 1 and container 3 are classified into one category; it can be obtained that the network area, network characteristics, central processor architecture and platform version of the deployment cluster of container 4 and container 5 are the same, so container 4 and container 5 are classified into one class; container 2 is a class.

As shown in FIG. 10 , the operation S231 to match the container feature of each container in the container resource requested to be deployed with the cluster feature of the s clusters includes operation S2311 : matching the container feature of each container in the classified container resource with the The features of the s clusters are matched. Specifically, feature matching can be performed on each container feature of the same container and the features of the s clusters. It can be understood that the four elements of the same type of container can be respectively matched with the corresponding elements of each cluster feature, and when the matching is successful, the cluster corresponding to the cluster feature can be used as the pre-deployed cluster of the same type of container. By classifying n containers, the deployment efficiency and accuracy of containers can be improved.

In some examples, container characteristics may also include container type and container role. Among them, container types can include common containers, system containers, and security containers, and container roles can include providers and consumers. Thereby, the types of container features can be enriched, so that the classification of containers is more accurate.

According to some embodiments of the present disclosure, as shown in FIG. 9 , the operation S210 to obtain the deployment request for the container resource by the application further includes operation S212 : unify the specifications of the n containers. The deployment efficiency and accuracy of containers can also be improved by unifying the specifications of n containers.

Based on the above method for dynamic resource adjustment for a multi-cluster architecture, the present disclosure also provides a resource dynamic adjustment apparatus 10 for a multi-cluster architecture. The resource dynamic adjustment apparatus 10 for the multi-cluster architecture will be described in detail below with reference to FIG. 11 .

FIG. 11 schematically shows a structural block diagram of a resource dynamic adjustment apparatus 10 for a multi-cluster architecture according to an embodiment of the present disclosure.

The resource dynamic adjustment device 10 for multi-cluster architecture includes an acquisition module 1 , a statistics module 2 , a comparison module 3 , a determination module 4 and an adjustment module 5 .

Obtaining module 1. The obtaining module 1 is configured to perform operation S210: obtaining a deployment request of an application for a container resource, wherein the container resource requested to be deployed includes n containers, each container has container characteristics, and n is an integer greater than or equal to 1.

Statistics module 2. The statistics module 2 is configured to perform operation S220: count resource usage information of s clusters, where s is an integer greater than or equal to 2, and each cluster has cluster characteristics.

Comparing module 3, the comparing module 3 is configured to perform operation S230: compare the container resources requested to be deployed and the resource usage information of the s clusters, to select at least one cluster from the s clusters, and use the at least one cluster as the actual deployment cluster.

Determining module 4, the determining module 4 is configured to perform operation S240: determine the physical deployment area of the host machine corresponding to the actual deployment cluster according to the actual deployment cluster.

Adjustment module 5. The adjustment module 5 is configured to perform operation S250: dynamically adjust the physical resources according to the physical deployment area.

Wherein, as shown in FIG. 12 , the comparison module 3 specifically includes a matching unit 301 , a command unit 302 and a selection unit 303 .

Matching unit 301, the matching unit 301 is configured to perform operation S231: match the container feature of each container in the container resource requested for deployment with the cluster feature of s clusters.

Command unit 302, the command unit 302 is configured to perform operation S232: take t clusters that are successfully matched as pre-deployed clusters, where t is an integer greater than or equal to 1 and less than or equal to s.

Selection unit 303, the selection unit 303 is configured to perform operation S233: select at least one cluster from the pre-deployed clusters according to the requested deployment container resource and resource usage information of the pre-deployed cluster, and use the at least one cluster as the actual deployment cluster.

Since the above-mentioned dynamic resource adjustment apparatus 10 for multi-cluster architecture is set based on the method for dynamic resource adjustment of multi-cluster architecture, the beneficial effects of the above-mentioned dynamic resource adjustment apparatus 10 for multi-cluster architecture are similar to those used in multi-cluster architecture The method of dynamic resource adjustment is the same, and will not be repeated here.

In addition, according to the embodiment of the present disclosure, any number of modules among the acquisition module 1, the statistics module 2, the comparison module 3, the determination module 4, and the adjustment module 5 may be combined in one module to implement, or any one of the modules may be implemented by Split into multiple modules. Alternatively, at least part of the functionality of one or more of these modules may be combined with at least part of the functionality of other modules and implemented in one module.

According to an embodiment of the present disclosure, at least one of the acquisition module 1, the statistics module 2, the comparison module 3, the determination module 4, and the adjustment module 5 may be implemented at least partially as a hardware circuit, such as a field programmable gate array (FPGA), Programmable logic array (PLA), system-on-chip, system-on-substrate, system-on-package, application-specific integrated circuit (ASIC), or hardware or firmware that can be implemented by any other reasonable means of integrating or packaging circuits, Or it can be implemented in any one of the three implementation manners of software, hardware and firmware, or in an appropriate combination of any of them.

Alternatively, at least one of the acquisition module 1, the statistics module 2, the comparison module 3, the determination module 4, and the adjustment module 5 may be implemented at least in part as a computer program module that, when executed, may perform corresponding functions .

The apparatus for dynamic resource adjustment according to an embodiment of the present disclosure will be described in detail below with reference to FIG. 13 to FIG. 17 . It is to be understood that the following description is illustrative only, and not specific limitation of the present disclosure.

The present disclosure provides a resource dynamic adjustment device under the multi-cluster private cloud architecture, which analyzes user container application status and plans its deployment scope, mainly including the following devices.

Step 1): the application details statistics device integrates and normalizes the container resources applied by all users, and then triggers the cluster resource statistics device.

Step 2): Cluster resource statistics device, query Prometheus monitoring to obtain the total resource usage details of each cluster and each node, query the database to obtain cluster characteristics, and finally integrate and obtain the private cloud cluster resource view, and then trigger the application resource analysis device.

Step 3): apply for a resource analysis device, calculate the clusters that the user container should deploy and the number of nodes that each cluster should expand according to the private cloud cluster view, and then trigger the device to apply for the expansion device.

Step 4): The device applies for the expansion device, and according to the calculation result of the application for the resource analysis device in step 3), applies to the infrastructure cloud for a virtual machine or physical machine device and invokes the corresponding Ansible operation and maintenance job to expand the related cluster.

As shown in FIG. 13 , it is a structural diagram of the resource dynamic adjustment device of the present disclosure, including: an application details statistics device a, a cluster resource statistics device b, an application resource analysis device c, and an equipment application expansion device d. The application details statistics device a is in communication connection with the cluster resource statistics device b, the cluster resource statistics device b is in communication connection with the application resource analysis device c, and the application resource analysis device c is in communication connection with the equipment application expansion device d.

Application details statistics device a: Classify and integrate the container resources applied by all users according to characteristic values (including network area, cluster host CPU architecture, K8S version, cluster characteristics, cloud type), and uniformly use fixed specifications as the unit. The specification of the application container is normalized, and finally a resource application view of the entire user group is formed, and then the cluster resource statistics device b is triggered.

Cluster resource statistics device b: collects the details of cluster system resource usage through the monitoring component. In order to achieve the purpose of simplifying the calculation, the cluster used resources and redundant resource data are calculated in units of fixed container specifications. The calculation method is as follows.

Combine the cluster network area, cluster host CPU architecture, K8S version, cluster characteristics, and cloud type characteristic values to form a view of the resource utilization rate of the entire private cloud. In addition, according to the number of containers deployed by the user in each cluster, the affinity of the user to each cluster is calculated. The more containers the user deploys in the cluster, the higher the affinity, and vice versa, the lower the affinity. Then trigger to apply for resource conversion device c.

Application resource conversion device c: Compare the resource application view and the private cloud resource utilization rate view, first perform pre-selection, compare the user's application container characteristic value with the cluster characteristic value, and filter out the user's optional deployment range; The user's affinity for each cluster calculated by the statistics device b is used to score the cluster. The higher the affinity, the higher the score; otherwise, the lower the score. Scoring, the higher the redundancy rate, the higher the score, and vice versa, the lower the score; the scores are weighted and summed and sorted. The application resource conversion device c selects and deploys the clusters in sequence according to the sorting results under the condition that the cluster satisfies the fixed redundancy rate and the maximum number of cluster nodes until the user's application for resources is satisfied. The application resource conversion device c finally calculates the actual deployment cluster of the user and the number of physical machines required for application through preselection and optimization.

Device application expansion device d: Statistical analysis is used to obtain a view of physical resource utilization. First, pre-selection is performed, and the characteristic values of physical machines (ie network area, CPU architecture, cloud type, central processing machine size, memory size, physical storage size) and the characteristic values of physical resources (i.e. network area, central processing unit architecture, cloud type, central processing machine size, memory size, physical storage size) to screen out the user's optional application for physical machine construction area; The number of optional IPs, CPU resource utilization, memory resource utilization, physical storage resource utilization, etc. are scored for each physical resource logical area (there are different logical area divisions according to different schemes), and the scores are weighted and summed. sort. The physical resource logical area where the host is located is obtained through the two steps of pre-selection and optimization, and finally the infrastructure cloud is called to build the physical machine and install the cluster nodes.

FIG. 14 is an internal structure diagram of the application details statistics device a, including a container application specification unit 11 and a container application classification unit 12 .

Among them, the container application specification unit 11: uniformly reduces the specification of the application container by taking the fixed specification as a unit.

Container application classification unit 12: Classify container resources applied by all users according to characteristic values (including network area, network characteristics, cluster host CPU architecture, K8S version, cluster characteristics, cloud type).

FIG. 15 is an internal structure diagram of the cluster resource statistics device b, including a cluster resource details acquisition unit 21 and a cluster resource integration and classification unit 22 .

Among them, the cluster resource details obtaining unit 21: obtains each cluster, the CPU resource usage rate, and the memory resource usage rate by calling the Prometheus interface.

Cluster resource integration and classification unit 22: Using the fixed container specification as a unit, calculate the available resources, used resources and redundant resource data of the cluster, and combine the cluster network area, network characteristics, cluster host CPU architecture, K8S version, and cluster characteristics. , cloud type characteristic values to form the entire private cloud resource utilization view.

FIG. 16 is an internal structure diagram of the application resource analysis device c, including a user deployment range analysis unit 31 and a physical resource integration analysis unit 32 .

The user deployment scope analysis unit 31 compares the resource application view and the private cloud resource utilization rate view, scores and sorts the clusters, and obtains the user-selectable deployment scope through two steps of preselection and selection.

The physical resource integration analysis unit 32 : calculates the actual deployment of the cluster and the number of physical machines required by the user according to the cluster redundancy rate and the maximum limit number of cluster nodes, and then generates a corresponding application task.

FIG. 17 is an internal structure diagram of the device application expansion device d, including a device application unit 41 and a node management unit 42 .

The device application unit 41 : obtains a physical resource usage rate view by statistics, scores and sorts each physical resource logical area, obtains the physical deployment area of the host machine through two steps of pre-selection and optimization, and builds the physical machine according to the physical deployment area.

Node management unit 42: Use Ansible operation and maintenance operations to manage the physical machines built by the device application unit 41 to the corresponding cluster and deliver them for use.

FIG. 18 schematically shows a block diagram of an electronic device suitable for implementing the above method according to an embodiment of the present disclosure.

As shown in FIG. 18 , an electronic device 900 according to an embodiment of the present disclosure includes a processor 901 that can be loaded into a random access memory (RAM) 903 according to a program stored in a read only memory (ROM) 902 or from a storage portion 908 program to perform various appropriate actions and processes. The processor 901 may include, for example, a general-purpose microprocessor (eg, a CPU), an instruction set processor and/or a related chipset, and/or a special-purpose microprocessor (eg, an application-specific integrated circuit (ASIC)), and the like. The processor 901 may also include on-board memory for caching purposes. The processor 901 may include a single processing unit or multiple processing units for performing different actions of the method flow according to the embodiments of the present disclosure.

In the RAM 903, various programs and data necessary for the operation of the electronic device 900 are stored. The processor 901 , the ROM 902 and the RAM 903 are connected to each other through a bus 904 . The processor 901 performs various operations of the method flow according to the embodiment of the present disclosure by executing the programs in the ROM 902 and/or the RAM 903 . Note that the program may also be stored in one or more memories other than the ROM 902 and the RAM 903 . The processor 901 may also perform various operations of the method flow according to the embodiments of the present disclosure by executing programs stored in the one or more memories.

According to an embodiment of the present disclosure, the electronic device 900 may also include an input/output (I/O) interface 905 which is also connected to the bus 904 . Electronic device 900 may also include one or more of the following components connected to I/O interface 905: input portion 906 including keyboard, mouse, etc.; including components such as cathode ray tube (CRT), liquid crystal display (LCD), etc., and An output section 907 of speakers and the like; a storage section 908 including a hard disk and the like; and a communication section 909 including a network interface card such as a LAN card, a modem, and the like. The communication section 909 performs communication processing via a network such as the Internet. Drive 910 is also connected to input/output (I/O) interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is mounted on the drive 910 as needed so that a computer program read therefrom is installed into the storage section 908 as needed.

The present disclosure also provides a computer-readable storage medium. The computer-readable storage medium may be included in the device/apparatus/system described in the above embodiments; it may also exist alone without being assembled into the device/system. device/system. The above-mentioned computer-readable storage medium carries one or more programs, and when the above-mentioned one or more programs are executed, implement the method according to the embodiment of the present disclosure.

According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as, but not limited to, portable computer disks, hard disks, random access memory (RAM), read only memory (ROM) , erasable programmable read only memory (EPROM or flash memory), portable compact disk read only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing. In this disclosure, a computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of the present disclosure, a computer-readable storage medium may include one or more memories other than ROM 902 and/or RAM 903 and/or ROM 902 and RAM 903 described above.

Embodiments of the present disclosure also include a computer program product comprising a computer program containing program code for performing the method illustrated in the flowchart. When the computer program product is run in a computer system, the program code is used to cause the computer system to implement the methods of the embodiments of the present disclosure.

When the computer program is executed by the processor 901, the above-described functions defined in the system/apparatus of the embodiment of the present disclosure are performed. According to embodiments of the present disclosure, the systems, apparatuses, modules, units, etc. described above may be implemented by computer program modules.

In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device, a magnetic storage device, or the like. In another embodiment, the computer program may also be transmitted, distributed in the form of a signal over a network medium, and downloaded and installed through the communication section 909, and/or installed from a removable medium 911. The program code embodied by the computer program may be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the foregoing.

In such an embodiment, the computer program may be downloaded and installed from the network via the communication portion 909, and/or installed from the removable medium 911. When the computer program is executed by the processor 901, the above-described functions defined in the system of the embodiment of the present disclosure are performed. According to embodiments of the present disclosure, the above-described systems, apparatuses, apparatuses, modules, units, etc. can be implemented by computer program modules.

According to the embodiments of the present disclosure, the program code for executing the computer program provided by the embodiments of the present disclosure may be written in any combination of one or more programming languages, and specifically, high-level procedures and/or object-oriented programming may be used. programming language, and/or assembly/machine language to implement these computational programs. Programming languages include, but are not limited to, languages such as Java, C++, python, "C" or similar programming languages. The program code may execute entirely on the user computing device, partly on the user device, partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (eg, using an Internet service provider business via an Internet connection).

The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code that contains one or more logical functions for implementing the specified functions executable instructions. It should also be noted that, in some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It is also noted that each block of the block diagrams or flowchart illustrations, and combinations of blocks in the block diagrams or flowchart illustrations, can be implemented in special purpose hardware-based systems that perform the specified functions or operations, or can be implemented using A combination of dedicated hardware and computer instructions is implemented.

Those skilled in the art will appreciate that various combinations and/or combinations of features recited in various embodiments and/or claims of the present disclosure are possible, even if such combinations or combinations are not expressly recited in the present disclosure. In particular, various combinations and/or combinations of the features recited in the various embodiments of the present disclosure and/or in the claims may be made without departing from the spirit and teachings of the present disclosure. All such combinations and/or combinations fall within the scope of this disclosure.

Embodiments of the present disclosure have been described above. However, these examples are for illustrative purposes only, and are not intended to limit the scope of the present disclosure. Although the various embodiments are described above separately, this does not mean that the measures in the various embodiments cannot be used in combination to advantage. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and these substitutions and modifications should all fall within the scope of the present disclosure.

Claims (15)

1. A method for dynamically adjusting resources in a multi-cluster architecture, comprising:

acquiring a deployment request of an application for container resources, wherein the container resources requested to be deployed comprise n containers, each container has a container characteristic, and n is an integer greater than or equal to 1;

counting resource use information of s clusters, wherein s is an integer greater than or equal to 2, and each cluster has cluster characteristics;

comparing the container resource requested to be deployed with the resource use information of the s clusters to select at least one cluster from the s clusters, and taking the at least one cluster as an actual deployment cluster;

determining a physical deployment area of a host machine corresponding to the actual deployment cluster according to the actual deployment cluster; and

dynamically adjusting physical resources according to the physical deployment region,

wherein the comparing the container resource requested to be deployed with the resource usage information of the s clusters to select at least one cluster from the s clusters, and the taking the at least one cluster as an actual deployment cluster specifically includes:

matching container characteristics of each container in the container resources requested to be deployed with cluster characteristics of the s clusters;

taking t clusters which are successfully matched as pre-deployment clusters, wherein t is an integer which is greater than or equal to 1 and less than or equal to s; and

and selecting at least one cluster from the pre-deployment clusters according to the container resource requested to be deployed and the resource use information of the pre-deployment clusters, and taking the at least one cluster as an actual deployment cluster.

2. The method of claim 1, wherein the resource usage information of each cluster comprises used resources and unused resources, wherein the used resources comprise historical container deployment of the application, and wherein the counting resource usage information of the s clusters comprises:

counting affinity scores of the applications to each cluster in the s clusters according to the historical container deployment conditions of the applications; and

and scoring each cluster according to the unused resources of each cluster in the s clusters to obtain a resource score.

3. The method according to claim 2, wherein selecting at least one cluster from the pre-deployment clusters according to the container resource requested to be deployed and the resource usage information of the pre-deployment clusters, and taking the at least one cluster as an actual deployment cluster specifically includes:

calculating the weighted scores of the t pre-deployment clusters according to the affinity scores and the resource scores; and

and selecting at least one cluster from the t pre-deployment clusters according to the weighted scores, and taking the at least one cluster as an actual deployment cluster.

4. The method of claim 1, wherein determining, from the actual deployment cluster, a physical deployment area of a host corresponding to the actual deployment cluster comprises:

generating host machine characteristics according to the container resources requested to be deployed;

acquiring physical resource characteristics of a physical resource pool, wherein the physical resource pool comprises k building areas, each building area has the physical resource characteristics, and k is an integer greater than or equal to 1;

carrying out feature matching on the host machine features and the physical resource features of the k building areas;

g building areas with successfully matched features are used as pre-building areas, wherein g is an integer which is greater than or equal to 1 and smaller than k; and

and determining a physical deployment area of the host machine corresponding to the actual deployment cluster according to the pre-built area.

5. The method according to claim 4, wherein the determining a physical deployment area of a host corresponding to the actual deployment cluster according to the pre-built area comprises:

acquiring regional characteristic information of each pre-built region;

scoring each pre-built region according to the region characteristic information to obtain a region score; and

and selecting at least one physical deployment area from the g pre-built areas as a host machine corresponding to the actual deployment cluster according to the area score.

6. The method of claim 5, wherein the regional characteristic information comprises at least one of a selectable IP number, a central processor resource usage, a memory resource usage, and a physical storage resource usage.

7. The method according to claim 6, wherein the scoring each pre-built region according to the region feature information, and obtaining a region score comprises:

respectively scoring the selectable IP number, the CPU resource utilization rate, the memory resource utilization rate and the physical storage resource utilization rate to obtain a plurality of regional characteristic scores; and

and weighting and summing the plurality of regional feature scores to obtain regional scores.

8. The method of claim 4, wherein the host characteristics include at least one of a corresponding network region, central processor architecture, cloud type, central processing machine size, memory size, and physical storage size; and/or the presence of a gas in the gas,

the physical resource characteristics include at least one of a corresponding network region, a central processor architecture, a cloud type, a central processing machine size, a memory size, and a physical storage size.

9. The method of claim 1, wherein the container characteristics include at least one of a network area, network characteristics, a central processor architecture and a platform version of a deployment cluster to which the container corresponds; and/or the presence of a gas in the gas,

the cluster characteristics include at least one of a network area, network characteristics, a central processor architecture, and a platform version corresponding to the cluster.

10. The method of claim 1, wherein obtaining the deployment request of the application for the container resource comprises: classifying the container resources requested to be deployed according to the container characteristics;

the matching container characteristics of each container in the container resources requested to be deployed with the cluster characteristics of the s clusters comprises: matching the container characteristics of each container in the classified container resources with the characteristics of the s clusters.

11. The method of claim 10, wherein obtaining the deployment request of the application for the container resource further comprises: unifying the specifications of the n containers.

12. A dynamic resource adjustment apparatus for a multi-cluster architecture, comprising:

the system comprises an acquisition module, a management module and a management module, wherein the acquisition module is used for executing a deployment request of an acquisition application for container resources, the container resources requested to be deployed comprise n containers, each container has a container characteristic, and n is an integer greater than or equal to 1;

the statistical module is used for performing statistics on resource use information of s clusters, wherein s is an integer greater than or equal to 2, and each cluster has cluster characteristics;

a comparison module, configured to perform a comparison between the container resource requested to be deployed and the resource usage information of the s clusters, so as to select at least one cluster from the s clusters, and use the at least one cluster as an actual deployment cluster;

a determining module, configured to determine, according to the actual deployment cluster, a physical deployment area of a host corresponding to the actual deployment cluster; and

an adjustment module to perform dynamic adjustment of physical resources according to the physical deployment area,

wherein, the comparison module specifically comprises:

a matching unit, configured to perform matching of container characteristics of each container in the container resources requested to be deployed with cluster characteristics of the s clusters;

the command unit is used for executing t clusters which are successfully matched as pre-deployment clusters, wherein t is an integer which is greater than or equal to 1 and less than or equal to s; and

and the selecting unit is used for selecting at least one cluster from the pre-deployment clusters according to the container resource requested to be deployed and the resource use information of the pre-deployment clusters, and taking the at least one cluster as an actual deployment cluster.

13. An electronic device, comprising:

one or more processors;

one or more memories for storing executable instructions that, when executed by the processor, implement the method of any of claims 1-11.

14. A computer-readable storage medium having stored thereon executable instructions that when executed by a processor implement a method according to any one of claims 1 to 11.

15. A computer program product comprising a computer program comprising one or more executable instructions which, when executed by a processor, implement a method according to any one of claims 1 to 11.

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