Container-based strategy arrangement response method and system and computer storage medium

Abstract

The invention relates to the technical field of container arrangement, in particular to a container-based strategy arrangement response method and system and a computer storage medium, and the method comprises the steps: constructing a container mirror image through a container engine; obtaining a resource use sequence of each load parameter of each container under the same priority on the nodes in the cluster; acquiring a local feature vector and a dynamic feature value of each sub-sequence; determining a first relative difference and a second relative difference; determining the relative dynamic difference of each load parameter of each container under the same priority; determining a resource adjustment coefficient of each container under the same priority; carrying out resource configuration adjustment on the containers with the same priority; and deploying and monitoring the container through a container arrangement tool based on the container mirror image and the resource allocation strategy adjusted by the resource configuration. According to the invention, when the resource configurations with the same priority conflict, the resource configurations can be dynamically adjusted, and the response speed, accuracy and efficiency of the network security event are improved.

Description

Technical Field

[0001] The present application relates to the technical field of container orchestration, and in particular to a container-based policy orchestration response method, system, and computer storage medium. Background Art

[0002] In cybersecurity incidents, the research and application of automation technologies are particularly important. Most existing cybersecurity incident automation technologies rely on preset rules and patterns, which lack flexibility and accuracy, resulting in incomplete and inaccurate response strategies. Traditional response methods usually refer to the means and strategies taken to respond to cybersecurity threats in environments without containerization and cloud-native architecture. These methods mainly rely on traditional IT infrastructure, such as physical servers, virtual machines, network devices, etc. If attacked, the entire server or virtual machine may need to be isolated, repaired, or rebuilt, resulting in greater business interruption and longer recovery time.

[0003] As a lightweight, independent operating environment, containers can ensure that applications can run consistently in any environment. However, when container orchestration tools allocate resources, they use a single-threshold resource configuration that does not take into account the dynamic changes in container demand for resources. When resource conflicts occur, low-priority containers will be evicted to ensure normal resource supply for high-priority containers. However, resource conflicts for containers of the same priority cannot be avoided. Summary of the invention

[0004] In order to solve the above technical problems, a container-based policy orchestration response method, system and computer storage medium are provided to solve the existing problems.

[0005] The solution to the technical problem of this application is to provide a container-based policy orchestration response method, system and computer storage medium, including the following steps:

[0006] In a first aspect, an embodiment of the present application provides a container-based policy orchestration response method, the method comprising the following steps:

[0007] Build a container image through the container engine; obtain resource usage data of each load parameter of each container at the same priority on the nodes in the cluster, and form a resource usage sequence for each load parameter, wherein the load parameters include: CPU usage, memory usage, and network bandwidth usage;

[0008] The resource usage sequence is divided into a plurality of subsequences; a central trend of element changes in each subsequence of the resource usage sequence and fluctuations of elements in the subsequence are analyzed to obtain a local feature vector of each subsequence; and a dynamic feature value of each subsequence in the resource usage sequence for each load parameter of each container at the same priority is determined according to similarities in changes of the local feature vectors between different subsequences in the resource usage sequence;

[0009] According to the distance relationship of all the dynamic characteristic values ​​between different load parameters of each container and the difference in the number of elements in all the subsequences, respectively determine the first relative difference and the second relative difference between each load parameter of each container and the other load parameters at the same priority; based on the first relative difference and the second relative difference, determine the relative dynamic difference of each load parameter of each container at the same priority;

[0010] Determine a resource adjustment coefficient for each container at the same priority level according to similarities of all the relative dynamic differences between each container at the same priority level and the other containers; and adjust resource configuration for the containers at the same priority level based on the resource adjustment coefficient;

[0011] Based on the container image and the resource allocation strategy adjusted by the resource configuration, the container is deployed and monitored through the container orchestration tool.

[0012] Preferably, the obtaining of the local feature vector of each subsequence includes:

[0013] Calculating trend statistics and coefficient of variation for each subsequence within the resource usage sequence;

[0014] The trend statistic and the coefficient of variation are combined to form a local feature vector of each subsequence.

[0015] Preferably, the dynamic characteristic value of each subsequence in the resource usage sequence of each load parameter of each container at the same priority is the average of the dot products of the local characteristic vectors of each subsequence in the resource usage sequence and all other subsequences.

[0016] Preferably, determining the first relative difference between each load parameter of each container and the remaining load parameters at the same priority level includes:

[0017] The dynamic feature values ​​of all subsequences in the resource usage sequence are combined into a dynamic feature sequence;

[0018] The distance of the dynamic feature sequence between each load parameter of each container under the same priority and the remaining load parameters is taken as the first relative difference between each load parameter of each container under the same priority and the remaining load parameters.

[0019] Preferably, the method for obtaining the second relative difference is:

[0020] The average of the number of elements in all subsequences in the resource usage sequence of each load parameter of each container at the same priority level is used as the relative characteristic coefficient of each load parameter of each container;

[0021] Calculate the difference value of the relative characteristic coefficient between each load parameter of each container under the same priority and the remaining load parameters, and use the calculation result of an exponential function with a natural constant as the base and the difference value as the exponent as the second relative difference between each load parameter of each container under the same priority and the remaining load parameters.

[0022] Preferably, determining the relative dynamic difference of each load parameter of each container at the same priority level includes:

[0023] calculating a ratio of the first relative difference to the second relative difference;

[0024] The average of the ratios between each load parameter of each container at the same priority and all other load parameters is taken as the relative dynamic difference of each load parameter of each container at the same priority.

[0025] Preferably, determining the resource adjustment coefficient of each container at the same priority level includes:

[0026] Calculating the similarity of the relative dynamic differences of all kinds of load parameters between each container and other containers at the same priority level;

[0027] The average of the similarities between each container and all other containers at the same priority level is used as the resource adjustment coefficient of each container at the same priority level.

[0028] Preferably, adjusting resource configuration for containers of the same priority level includes:

[0029] When conflicts occur in resource configurations of the same priority, the resource adjustment coefficients of all containers of the same priority are sorted in descending order, and resource configurations of the containers of the same priority are adjusted in sequence according to the sorting results.

[0030] In a second aspect, an embodiment of the present application further provides a container-based policy orchestration response system, the system comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above-mentioned container-based policy orchestration response methods when executing the computer program.

[0031] In a third aspect, an embodiment of the present application further provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the program is executed by a processor, any one of the above-mentioned container-based policy orchestration response methods is implemented.

[0032] This application has at least the following beneficial effects:

[0033] The present application divides the resource usage sequence into multiple subsequences; analyzes the central trend of element changes in each subsequence in the resource usage sequence and the fluctuation of elements in the subsequence to obtain local feature vectors of each subsequence; determines the dynamic feature values ​​of each subsequence in the resource usage sequence for each load parameter of each container at the same priority level based on the similarity of the changes in the local feature vectors between different subsequences in the resource usage sequence. The beneficial effect is that the local change trend and local fluctuation of each load parameter of the container at the same priority level are taken into account, and the similarity of fluctuations at different local times is analyzed to reflect the stability of the container load at the same priority level in the local time, and further reflect the normal fluctuation degree of the container load at the corresponding local time; determines the first relative difference and the second relative difference between each load parameter of each container at the same priority level and the remaining load parameters based on the distance relationship between all the dynamic feature values ​​between different load parameters of each container and the difference in the number of elements in all the subsequences; based on the first relative difference and the second relative difference , determine the relative dynamic difference of each load parameter of each container under the same priority, which has the beneficial effect of taking into account the dynamic difference of fluctuations between different load parameters to reflect the significance of the impact of dynamic changes in load parameters on resource configuration of different containers of the same priority; determine the resource adjustment coefficient of each container under the same priority according to the similarity of all the relative dynamic differences between each container and other containers under the same priority; adjust the resource configuration of containers of the same priority based on the resource adjustment coefficient; deploy and monitor containers through container orchestration tools based on container images and resource allocation strategies of resource configuration adjustments, which has the beneficial effect of taking into account the dynamic change degree of load parameters of different containers of the same priority to reflect the impact of dynamic changes in time on container resource configuration and the stability of the change characteristics of load parameters within a longer time range, and then dynamically adjust the resource configuration when conflicts occur in resource configuration of the same priority, to ensure that applications can run consistently in any environment, and improve the response speed, accuracy and efficiency of network security incidents. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The container-based policy orchestration response method of the present application is further described in detail below with reference to the accompanying drawings.

[0035] Figure 1A flowchart of the steps of the container-based policy orchestration response method provided in an embodiment of the present application;

[0036] Figure 2 A flowchart of the steps of a method for obtaining dynamic characteristic values ​​of each subsequence in a resource usage sequence of each load parameter of each container at the same priority level provided in an embodiment of the present application;

[0037] Figure 3 A flowchart of the steps of a method for obtaining the relative dynamic difference of each load parameter of each container at the same priority level provided in an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical solution and advantages of this application more clear, the container-based policy orchestration response method, system and computer storage medium proposed in this application are further described in detail below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific embodiments described here are only used to explain this application and are not used to limit this application.

[0039] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0040] See also Figure 1 , which shows a flowchart of a container-based policy orchestration response method provided by an embodiment of the present application, the method comprising the following steps:

[0041] Step 1: Build a container image through the container engine; obtain resource usage data of each load parameter of each container at the same priority on the nodes in the cluster, and form a resource usage sequence for each load parameter, wherein the load parameters include: CPU usage, memory usage, and network bandwidth usage.

[0042] Among the current container technologies, Docker container runtime is the most widely used. A container is an independent, lightweight runtime environment that includes application code, runtime libraries, system tools, and system libraries. Containers are isolated, and each container has its own file system and process space, which is isolated from the host system and other containers. This isolation prevents containers from interfering with each other, even if they run on the same physical host. These basic concepts of container technology make it an ideal choice for modern application development and deployment, providing high flexibility, maintainability, and resource isolation, while reducing the complexity of deployment and management.

[0043] Containerization is a virtualization technology that allows developers to encapsulate an application and all its dependencies in an independent container, which is created, managed, and run using a container engine such as Docker.

[0044] First, install the Docker engine operating environment in the application system, and create a Dockerfile according to the requirements of the application components. The Dockerfile is a text file that defines how to build a Docker container. The Dockerfile includes steps such as the base image, working directory, installation of dependencies, and copying of application code. Use the container building tool Docker technology to build a container image based on the Dockerfile.

[0045] It should be noted that the method of creating a Docker container image using Docker technology is a well-known technology and will not be described in detail here.

[0046] Furthermore, the built container images are deployed and managed through container orchestration tools. Container orchestration is a key automation tool and method for effectively managing and coordinating the deployment and operation of containerized applications. Its main goal is to simplify the management of container clusters and provide high availability, scalability, and automated container management. Container orchestration tools are responsible for allocating containers to available computing resources and automatically handling communication and load balancing between containers to ensure application availability. Container orchestration tools, such as Kubernetes, improve the management efficiency of containerized applications and implement automatic scaling, automatic repair, and load balancing, thereby ensuring high availability.

[0047] When using the Kubernetes orchestration tool to allocate resources, it is a single-threshold resource configuration, which mainly adjusts resource allocation through the resource requests and resource limits of containers defined in Kubernetes. For example, resources are configured based on the resource requests and resource limits defined for each container, the resource quotas allocated after application component division, and the defined priorities. However, this resource configuration method does not take into account the dynamic changes in container demand for resources. When resource conflicts occur, low-priority container sets (pods) will be expelled to ensure the normal supply of resources for high-priority pods, but resource conflicts for pods with the same priority cannot be avoided. Therefore, by analyzing the dynamic changes in resource demands of pods with the same priority, resource allocation is optimized and adjusted to improve resource utilization.

[0048] A Kubernetes cluster consists of multiple nodes. A node is a machine that performs specific work. It can be a virtual machine or a physical machine. Pods are distributed on nodes according to scheduling configurations. Each node can have multiple Pods. Each pod can contain one or more containers. The container resource usage of all containers in all pods of the node is collected. All containers in pods with the same priority have the same priority.

[0049] Therefore, before deploying the container image, it is necessary to define a resource allocation strategy to guide system operation when conflicts arise in resource configuration.

[0050] The Agent on the node regularly samples the resource usage of containers at the same priority through Docker-Daemon, obtains the usage data of each load in the container, samples the container N times, forms a sequence according to the collection order, and obtains the resource usage sequence of each load parameter of each container at the same priority. Each load parameter includes: CPU usage, memory usage, and network bandwidth usage.

[0051] Preferably, in this embodiment, the container is sampled 500 times. As other implementation modes, the implementer can set it according to the actual situation.

[0052] At this point, the resource usage sequence of each load parameter of each container at the same priority is obtained.

[0053] Step 2: divide the resource usage sequence into multiple subsequences; analyze the central trend of element changes in each subsequence of the resource usage sequence and the fluctuation of elements in the subsequence to obtain a local feature vector of each subsequence; and determine the dynamic feature value of each subsequence in the resource usage sequence for each load parameter of each container at the same priority based on the similarity of the changes of the local feature vectors between different subsequences in the resource usage sequence.

[0054] In order to clarify the dynamic change characteristics of the load of each container with the same priority over time, the dynamic characteristic value is determined by analyzing the local change trend and local fluctuation degree of each load parameter to reflect the dynamic difference of container resources over time. Specifically:

[0055] Divide the resource usage sequence of each load parameter of each container under the same priority into multiple subsequences;

[0056] Preferably, in this embodiment, the resource usage sequence is divided into a plurality of subsequences using a Bernaola Galvan segmentation algorithm, wherein the Bernaola Galvan segmentation algorithm is a well-known technology and will not be described in detail herein.

[0057] Calculating trend statistics and coefficient of variation for each subsequence within the resource usage sequence;

[0058] Preferably, in this embodiment, the Mann-Kendall trend check algorithm is used to obtain the trend statistics of each subsequence, wherein the Mann-Kendall trend check algorithm and the calculation of the coefficient of variation are both well-known technologies and will not be described in detail here.

[0059] The trend statistic and the coefficient of variation are combined to form a local feature vector of each subsequence;

[0060] It should be noted that the trend statistic reflects the central trend of the subsequence, and the coefficient of variation reflects the degree of fluctuation of the elements within the subsequence.

[0061] Calculate the mean of the point set of the local feature vectors of each subsequence in the resource usage sequence and all other subsequences as the dynamic feature value of each subsequence in the resource usage sequence for each load parameter of each container at the same priority;

[0062] Preferably, in this embodiment, the calculation formula for the dynamic characteristic value of each subsequence in the resource usage sequence of each load parameter of each container at the same priority is: in, The dynamic characteristic value of the kth subsequence in the resource usage sequence of the rth load parameter of the mth container at the same priority level, The local feature vector of the kth subsequence in the resource usage sequence of the rth load parameter of the mth container at the same priority level, The local feature vector of the g-th subsequence in the resource usage sequence of the r-th load parameter of the m-th container at the same priority level, dpr() is the dot product of the calculated vector, G m,r The number of all subsequences in the resource usage sequence for the rth load parameter of the mth container at the same priority.

[0063] It should be noted that the larger the dynamic eigenvalue, the smaller the difference in the fluctuation of the container's use of load parameters in the local time period, that is, the local dynamic changes over a longer period of time are relatively stable. Among them, the dot product reflects the similarity of vectors in multidimensional space. If the dynamic eigenvalue is large, it indicates that the resource usage of the corresponding subsequence is similar to other subsequences, which may be a normal load change.

[0064] Further, the step flow chart of the method for obtaining the dynamic characteristic value of each subsequence in the resource usage sequence of each load parameter of each container at the same priority provided by the embodiment of the present application is as follows: Figure 2 shown.

[0065] At this point, the dynamic characteristic values ​​of each subsequence in the resource usage sequence of each load parameter of each container at the same priority are obtained.

[0066] Step 3: Determine the first relative difference and the second relative difference between each load parameter of each container and the remaining load parameters at the same priority level according to the distance relationship between all the dynamic feature values ​​between different load parameters of each container and the difference in the number of elements in all the subsequences; determine the relative dynamic difference of each load parameter of each container at the same priority level based on the first relative difference and the second relative difference.

[0067] Furthermore, the time range differences of resource usage of each load parameter of each container under the same priority level over time, as well as the dynamic differences of different load parameters in the resource allocation process under time changes, are analyzed to determine the relative dynamic differences, specifically:

[0068] The average of the number of elements in all subsequences in the resource usage sequence of each load parameter of each container at the same priority level is used as the relative characteristic coefficient of each load parameter of each container;

[0069] The dynamic feature values ​​of all subsequences in the resource usage sequence of each load parameter of each container at the same priority level are combined into a dynamic feature sequence;

[0070] The distance between each load parameter of each container and the other load parameters of the dynamic feature sequence under the same priority is recorded as the first relative difference;

[0071] Preferably, in this embodiment, the DTW distance of the dynamic feature sequence between each load parameter of each container at the same priority and the remaining load parameters is recorded as the first relative difference, wherein the calculation of the DTW distance is a well-known technology and will not be repeated here.

[0072] Calculate the difference value of the relative characteristic coefficient between each load parameter of each container under the same priority and the other load parameters, and record the calculation result of an exponential function with a natural constant as the base and the difference value as the exponent as the second relative difference;

[0073] Preferably, in this embodiment, the absolute value of the difference between the relative characteristic coefficient of each load parameter of each container under the same priority and the remaining load parameters is calculated, and the calculation result of the exponential function with a natural constant as the base and the absolute value as the exponent is recorded as the second relative difference.

[0074] Calculating a ratio of the first relative difference to the second relative difference between each load parameter of each container and the remaining load parameters at the same priority level;

[0075] Taking the average of the ratios between each load parameter of each container and all other load parameters under the same priority as the relative dynamic difference of each load parameter of each container;

[0076] Preferably, in this embodiment, the calculation formula for the relative dynamic difference of each load parameter of each container at the same priority is: Among them, C m,r is the relative dynamic difference of the rth load parameter of the mth container under the same priority, w m,r is the dynamic characteristic sequence of the rth load parameter of the mth container under the same priority, w m,x is the dynamic characteristic sequence of the xth load parameter of the mth container under the same priority, a m,r is the relative characteristic coefficient of the rth load parameter of the mth container under the same priority, a m,x is the relative characteristic coefficient of the xth load parameter of the mth container under the same priority, dtw() represents the calculation of DTW distance, exp() is the exponential function with natural constant as the base, R m is the number of all load parameters of the mth container at the same priority.

[0077] It should be noted that the larger the first relative difference, the greater the difference in resource usage between different load parameters; the larger the second relative difference, the smaller the difference in usage duration of resources with different load parameters; the larger the resulting relative dynamic difference, the more significant the impact of dynamic changes in load parameters on container resource configuration.

[0078] Further, the flowchart of the steps of the method for obtaining the relative dynamic difference of each load parameter of each container under the same priority provided in the embodiment of the present application is as follows: Figure 3 shown.

[0079] At this point, the relative dynamic difference of each load parameter of each container at the same priority level is obtained.

[0080] Step 4: Determine the resource adjustment coefficient of each container at the same priority level based on the similarity of all the relative dynamic differences between each container and other containers at the same priority level; adjust the resource configuration of containers at the same priority level based on the resource adjustment coefficient; and deploy and monitor the container through a container orchestration tool based on the container image and the resource allocation strategy of the resource configuration adjustment.

[0081] Furthermore, based on the relative dynamic difference, the dynamic changes of load parameters between different containers of the same priority are analyzed, and the resource adjustment coefficient is determined to reflect the stability of resource usage of the container under load, so that resource allocation can be given priority, specifically:

[0082] Calculate the similarity of the relative dynamic differences of all kinds of load parameters between each container and the other containers at the same priority; take the average of the similarity between each container and all the other containers at the same priority as the resource adjustment coefficient of each container at the same priority;

[0083] Preferably, in this embodiment, the cosine similarity of the relative dynamic differences of all types of load parameters between each container and the other containers at the same priority level is calculated, wherein the calculation of the cosine similarity is a well-known technique and will not be described in detail herein.

[0084] It should be noted that the larger the resource adjustment coefficient, the more consistent the dynamic change characteristics of different container resources with the same priority over time, reflecting that the current container resource configuration is less affected by the dynamic change over time, that is, the change characteristics of the load parameters within a longer time range are more stable, so the resource configuration of the container can be prioritized.

[0085] When conflicts occur in resource configurations of the same priority, the resource adjustment coefficients of all containers of the same priority are sorted in descending order, and resource configurations of the containers of the same priority are adjusted in sequence according to the sorting results.

[0086] Based on the built container image and the formulated resource allocation strategy, the Kubernetes orchestration tool is used for deployment and monitoring, including:

[0087] Create corresponding resource objects in Kubernetes based on the defined container images and resource allocation policies. Configure the network connection of the container. Kubernetes uses network plug-ins to enable communication between containers and between containers and external networks. Expose the service port inside the container to the inside or outside of the cluster by setting up Service resources so that other components or users can access it. For application components that require persistent storage of data, configure storage volumes and use cloud storage as the backend of the storage volume. Ensure that the container can access and use persistently stored data by specifying the storage volume mount point in the Pod definition. Use the Kubernetes command line tool to apply the created resource objects to the Kubernetes cluster. Kubernetes automatically pulls the container image, starts the container, and configures it according to the policy based on the definition of the resource object.

[0088] Kubernetes provides built-in monitoring indicators to monitor the operating status of containers and clusters in real time, such as CPU usage, memory usage, network bandwidth, etc. Dynamic adjustments are made based on the monitored container resource usage. For example, if the CPU usage of a container continues to exceed the set threshold, Kubernetes can automatically trigger the horizontal scaling mechanism. Through the horizontal pod autoscaler (HPA), the number of container instances is automatically adjusted according to the CPU usage to share the load and reduce the CPU usage of a single container.

[0089] Monitor container-related events, including container start, stop, and failure events. When an event occurs, Kubernetes can dynamically adjust according to pre-defined policies. For example, when a container fails, Kubernetes can automatically restart the container or switch to a backup container based on the recovery strategy.

[0090] It should be noted that the process of deploying containers through container orchestration is a well-known technology and will not be described in detail here.

[0091] An embodiment of the present application also provides a container-based policy orchestration response system, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of any one of the above-mentioned container-based policy orchestration response methods when executing the computer program.

[0092] Based on the same inventive concept as the above method, an embodiment of the present application further provides a computer storage medium, wherein the computer storage medium stores a computer program, and when the program is executed by a processor, any one of the above container-based policy orchestration response methods is implemented.

[0093] It should be understood that although Figure 1 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0094] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0095] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the present application. It should be pointed out that, for those of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application without departing from the content of the technical solution of the present application, shall fall within the protection scope of the technical solution of the present application.

Claims

1. A container-based policy orchestration response method, characterized in that: The method comprises the following steps: Build a container image through the container engine; obtain resource usage data of each load parameter of each container at the same priority on the nodes in the cluster, and form a resource usage sequence for each load parameter, wherein the load parameters include: CPU usage, memory usage, and network bandwidth usage; The resource usage sequence is divided into a plurality of subsequences; a central trend of element changes in each subsequence of the resource usage sequence and fluctuations of elements in the subsequence are analyzed to obtain a local feature vector of each subsequence; and a dynamic feature value of each subsequence in the resource usage sequence for each load parameter of each container at the same priority is determined according to similarities in changes of the local feature vectors between different subsequences in the resource usage sequence; According to the distance relationship of all the dynamic characteristic values ​​between different load parameters of each container and the difference in the number of elements in all the subsequences, respectively determine the first relative difference and the second relative difference between each load parameter of each container and the other load parameters at the same priority; based on the first relative difference and the second relative difference, determine the relative dynamic difference of each load parameter of each container at the same priority; Determine a resource adjustment coefficient for each container at the same priority level according to similarities of all the relative dynamic differences between each container at the same priority level and the other containers; and adjust resource configuration for the containers at the same priority level based on the resource adjustment coefficient; Based on the container image and the resource allocation strategy adjusted by the resource configuration, the container is deployed and monitored through the container orchestration tool.

2. The container-based policy orchestration response method according to claim 1, characterized in that: The obtaining of the local feature vector of each subsequence includes: Calculating trend statistics and coefficient of variation for each subsequence within the resource usage sequence; The trend statistic and the coefficient of variation are combined to form a local feature vector of each subsequence.

3. The container-based policy orchestration response method according to claim 1, characterized in that: The dynamic feature value of each subsequence in the resource usage sequence of each load parameter of each container at the same priority is the average of the dot products of the local feature vectors of each subsequence in the resource usage sequence and all other subsequences.

4. The container-based policy orchestration response method according to claim 1, characterized in that: The determining of a first relative difference between each load parameter of each container and the remaining load parameters at the same priority level includes: The dynamic feature values ​​of all subsequences in the resource usage sequence are combined into a dynamic feature sequence; The distance of the dynamic feature sequence between each load parameter of each container under the same priority and the remaining load parameters is taken as the first relative difference between each load parameter of each container under the same priority and the remaining load parameters.

5. The container-based policy orchestration response method according to claim 1, characterized in that: The method for obtaining the second relative difference is: The average of the number of elements in all subsequences in the resource usage sequence of each load parameter of each container at the same priority level is used as the relative characteristic coefficient of each load parameter of each container; Calculate the difference value of the relative characteristic coefficient between each load parameter of each container under the same priority and the remaining load parameters, and use the calculation result of an exponential function with a natural constant as the base and the difference value as the exponent as the second relative difference between each load parameter of each container under the same priority and the remaining load parameters.

6. The container-based policy orchestration response method according to claim 1, characterized in that: The determining of the relative dynamic difference of each load parameter of each container at the same priority level includes: calculating a ratio of the first relative difference to the second relative difference; The average of the ratios between each load parameter of each container at the same priority and all other load parameters is taken as the relative dynamic difference of each load parameter of each container at the same priority.

7. The container-based policy orchestration response method according to claim 1, characterized in that: The determining of the resource adjustment coefficient of each container at the same priority level includes: Calculating the similarity of the relative dynamic differences of all kinds of load parameters between each container and other containers at the same priority level; The average of the similarities between each container and all other containers at the same priority level is used as the resource adjustment coefficient of each container at the same priority level.

8. The container-based policy orchestration response method according to claim 1, characterized in that: The adjusting resource configuration for containers of the same priority level includes: When conflicts occur in resource configurations of the same priority, the resource adjustment coefficients of all containers of the same priority are sorted in descending order, and resource configurations of the containers of the same priority are adjusted in sequence according to the sorting results.

9. A container-based policy orchestration response system, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor implements the steps of the container-based policy orchestration response method as described in any one of claims 1 to 8 when executing the computer program.

10. A computer storage medium storing a computer program, characterized in that: When the program is executed by a processor, a container-based policy orchestration response method as described in any one of claims 1 to 8 is implemented.

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