Container platform fault disposal scheme preferential method and device and electronic equipment

Abstract

The invention discloses a container platform fault disposal scheme preferential method and device and electronic equipment. The method comprises the following steps: obtaining current index values of a plurality of fault disposal schemes on a plurality of evaluation indexes respectively; the fault processing scheme is used for processing a fault generated when a container platform realizes business service; the container platform is built based on a plurality of running nodes, and the running nodes realize corresponding business services through running containers; according to the current index value, a first disposal scheme and a second disposal scheme are obtained, and the index value of the first disposal scheme on each evaluation index is optimal in all the fault disposal schemes; the index value of the second disposal scheme on each evaluation index is the worst in all the fault disposal schemes; and according to the first disposal scheme and the second disposal scheme, screening out a target disposal scheme meeting a preferred condition from the plurality of fault disposal schemes.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method, device and electronic device for selecting the best solution for handling container platform failures. Background Art

[0002] Currently, operation and maintenance personnel usually select fault handling solutions for container platforms based on their experience, which may result in inaccuracies in the selected fault handling solutions. Summary of the invention

[0003] In view of the above problems, the present application provides a method, device and electronic device for selecting the best fault handling solution for a container platform to improve the accuracy of selecting the best fault handling solution. The specific solution is as follows:

[0004] In a first aspect, the present application provides a method for selecting a best container platform fault handling solution, the method comprising:

[0005] Obtaining current index values ​​of multiple fault handling solutions on multiple evaluation indicators respectively; the fault handling solutions are used to handle faults caused by the container platform implementing business services; the container platform is built based on multiple running nodes, and the running nodes implement corresponding business services through the running containers;

[0006] According to the current indicator value, a first treatment plan and a second treatment plan are obtained, wherein the indicator value of the first treatment plan on each evaluation indicator is the best among all the fault treatment plans; and the indicator value of the second treatment plan on each evaluation indicator is the worst among all the fault treatment plans;

[0007] According to the first handling solution and the second handling solution, a target handling solution that meets the optimization condition is screened out from the multiple fault handling solutions.

[0008] In a possible implementation, the evaluation index includes any number of the following: execution time of the fault handling solution, resource utilization of the fault handling solution, impact range parameter of the fault handling solution on the first service, fault recurrence rate of the fault handling solution, operation complexity of the fault handling solution, collaborative processing parameter of the fault handling solution in a multi-operating node scenario, continuity impact parameter of the fault handling solution on the second service, and execution strategy adjustment parameter of the fault handling solution;

[0009] The first service is a business service implemented by the container platform that meets the first condition; the second service is a business service implemented by the container platform that meets the second condition.

[0010] In a possible implementation, before obtaining the first treatment plan and the second treatment plan according to the current indicator value, the method further includes:

[0011] The current indicator value is weighted according to the indicator weight corresponding to each evaluation indicator.

[0012] In a possible implementation, the method further includes:

[0013] Obtaining multiple indicator values ​​of each of the evaluation indicators for all the fault handling solutions within a target duration;

[0014] According to the multiple indicator values, an entropy value corresponding to each evaluation indicator is obtained; the entropy value represents the fluctuation state of the indicator value;

[0015] According to the entropy value, the indicator weight corresponding to each evaluation indicator is obtained.

[0016] In a possible implementation, the selecting a target handling solution that meets a preferred condition from the multiple fault handling solutions according to the first handling solution and the second handling solution includes:

[0017] According to the current index value of the fault handling solution on each of the evaluation indicators, respectively obtain a first evaluation distance and a second evaluation distance corresponding to each of the fault handling solutions; the first evaluation distance is the distance between the fault handling solution and the first handling solution; the second evaluation distance is the distance between the fault handling solution and the second handling solution;

[0018] According to the first evaluation distance and the second evaluation distance, a target handling solution that meets the optimization condition is screened out from the multiple fault handling solutions.

[0019] In a possible implementation, selecting a target handling solution that meets a preferred condition from the multiple fault handling solutions according to the first evaluation distance and the second evaluation distance includes:

[0020] Obtaining a first relative closeness between each of the fault handling solutions and the first handling solution according to the first evaluation distance and the second evaluation distance;

[0021] From the multiple fault handling solutions, a target handling solution whose first relative closeness satisfies a first optimal condition is selected.

[0022] In a possible implementation, selecting a target handling solution that meets a preferred condition from the multiple fault handling solutions according to the first evaluation distance and the second evaluation distance includes:

[0023] Obtaining a second relative closeness between each of the fault handling solutions and a second handling solution according to the first evaluation distance and the second evaluation distance;

[0024] From the multiple fault handling solutions, a target handling solution whose second relative closeness satisfies a second optimal condition is screened out.

[0025] In a possible implementation, obtaining a first treatment plan and a second treatment plan according to the current indicator value includes:

[0026] Filter out the best index value and the worst index value corresponding to each evaluation index from the current index values ​​of all the fault handling solutions on each evaluation index;

[0027] Constructing a first treatment plan based on the optimal indicator value corresponding to each of the evaluation indicators;

[0028] Based on the worst indicator value corresponding to each of the evaluation indicators, a second disposal plan is constructed.

[0029] A second aspect of the present application provides a device for selecting the best solution for handling container platform failures, the device comprising:

[0030] An indicator obtaining unit, used to obtain current indicator values ​​of multiple fault handling solutions on multiple evaluation indicators respectively; the fault handling solutions are used to handle faults caused by the container platform implementing business services; the container platform is built based on multiple running nodes, and the running nodes implement corresponding business services through the running containers;

[0031] A solution obtaining unit, configured to obtain a first treatment solution and a second treatment solution according to the current indicator value, wherein the indicator value of the first treatment solution on each evaluation indicator is the best among all the fault treatment solutions; and the indicator value of the second treatment solution on each evaluation indicator is the worst among all the fault treatment solutions;

[0032] A solution screening unit is used to screen out a target solution that meets the optimization condition from the multiple fault handling solutions according to the first handling solution and the second handling solution.

[0033] A third aspect of the present application provides a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements the optimal method for handling container platform faults according to the first aspect or any implementation of the first aspect.

[0034] A fourth aspect of the present application provides an electronic device, comprising at least one processor and a memory connected to the processor, wherein:

[0035] The memory is used to store computer programs;

[0036] The processor is used to execute the computer program so that the electronic device can implement the optimal method of the container platform fault handling solution of the above-mentioned first aspect or any implementation method of the first aspect.

[0037] In a fifth aspect, the present application provides a computer storage medium, which carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can select the optimal method for handling container platform faults according to the first aspect or any implementation of the first aspect.

[0038] By means of the above technical solution, in a method, device and electronic device for selecting a preferred container platform fault handling solution provided by the present application, the current index values ​​of multiple fault handling solutions of the container platform on multiple evaluation indicators are obtained to obtain a first handling solution and a second handling solution, the index value corresponding to each evaluation indicator in the first handling solution is the best among all fault handling solutions, and the index value corresponding to each evaluation indicator in the second handling solution is the worst among all fault handling solutions, based on which, according to the handling solution composed of the best index value and the handling solution composed of the worst index value, a target handling solution that meets the preferred conditions is screened out from multiple fault handling solutions. It can be seen that in this embodiment, by constructing an index system composed of multiple evaluation indicators, the best handling solution and the worst handling solution can be constructed by the index values ​​of each fault handling solution on these evaluation indicators, and the target handling solution can be screened out from multiple fault handling solutions accordingly, which can improve the accuracy of selecting the best fault handling solution.

[0039] It should be noted that the method, device and electronic device for selecting the best container platform fault handling solution provided in this application are intended to provide a rational evaluation and theoretical basis for the fault handling solution. The evaluation results are for reference only and are determined by the operation and maintenance personnel based on the actual situation. The goal of this application is to assist decision-making rather than to provide standardized answers to avoid dogmatic use. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0041] Figure 1 A flowchart for implementing a method for selecting the best container platform fault handling solution provided in an embodiment of the present application;

[0042] Figure 2 This is an architecture diagram of the container platform in the embodiment of the present application;

[0043] Figure 3 This is an example diagram of a plan evaluation index system corresponding to a fault handling solution of a container platform in an embodiment of the present application;

[0044] Figure 4 A partial flow chart of a method for selecting an optimal solution for handling container platform failures provided in an embodiment of the present application;

[0045] Figure 5 Another partial flow chart of a method for selecting an optimal solution for handling a container platform failure provided in an embodiment of the present application;

[0046] Figure 6 A schematic diagram of the structure of a device for selecting the best container platform fault handling solution provided in an embodiment of the present application;

[0047] Figure 7 Another structural schematic diagram of a device for selecting the best container platform fault handling solution provided in an embodiment of the present application;

[0048] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application;

[0049] Fig. 9 This is a specific flow chart of the TOPSIS algorithm in the embodiment of this application;

[0050] Fig.10 This is a specific flow chart of the method for selecting the best container platform fault handling plan in the embodiment of the present application;

[0051] Fig.11 It is a schematic diagram of the correspondence between the optimization process of the container platform fault handling solution and the TOPSIS algorithm process in the embodiment of the present application. DETAILED DESCRIPTION

[0052] The following describes the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation method section of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0053] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0054] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0055] Reference Figure 1 , is a flowchart of a method for selecting a best container platform fault handling solution provided in an embodiment of the present application, which can be applied to electronic devices capable of data processing, such as computers or servers. The technical solution in this embodiment is mainly used to improve the accuracy of selecting the best fault handling solution.

[0056] Specifically, the method in this embodiment may include the following steps:

[0057] Step 101: obtaining current index values ​​of multiple fault handling solutions on multiple evaluation indexes respectively.

[0058] Among them, the fault handling solution is used to handle faults caused by the container platform to implement business services. The container platform here is built based on multiple running nodes, and the running nodes implement corresponding business services through the running containers. For example, Figure 2 As shown in , the container platform is built based on nodes 1, 2, and 3. Containers A and B are built in node 1, and containers C and D are built in node 3. Containers are used to run applications and their dependent execution environments. Nodes are actual physical devices or virtual machines that run containers. In the container platform, nodes can be divided into master nodes and worker nodes. The master node is responsible for controlling and managing the entire cluster (such as scheduling containers, coordinating nodes, and monitoring status); the worker node runs the actual container. The container shares the node's operating system kernel with the host, but each container runs in its own isolated environment. For example: a web application or database instance can be run in a Docker container.

[0059] A container platform is a collection of infrastructure and tools used to manage and coordinate containerized applications. Its core functions include container scheduling, orchestration, load balancing, monitoring, and automatic expansion. For example, a container platform is responsible for managing the lifecycle of multiple containers.

[0060] In the specific implementation, the evaluation indicators include: indicators corresponding to the execution parameters of the fault handling plan, indicators corresponding to the business services implemented by the container platform during and after the execution of the fault handling plan, etc.

[0061] In one implementation, the evaluation indicators include any number of the following: execution time of the fault handling plan, resource utilization of the fault handling plan, impact range parameters of the fault handling plan on the first service, fault recurrence rate of the fault handling plan, operational complexity of the fault handling plan, collaborative processing parameters of the fault handling plan in a multi-running node scenario, continuity impact parameters of the fault handling plan on the second service, execution strategy adjustment parameters of the fault handling plan, and so on.

[0062] Among them, the first service is a business service implemented by the container platform that meets the first condition, such as a normal service on the container platform; the second service is a business service implemented by the container platform that meets the second condition, such as a key service on the container platform.

[0063] For example, in this embodiment, the following key indicator system can be constructed according to the operating background of the container platform and the actual production environment:

[0064] (1) Plan execution time, that is, the execution time of the fault handling plan:

[0065] The execution time of the solution is the time required from the start of the fault handling solution to the complete resolution of the fault, and is an important indicator for measuring the response efficiency of the fault handling solution. The shorter the time, the faster the business services that fail on the container platform will be restored, and the smaller the impact of business service interruptions on users. This indicator not only reflects the direct execution effect of the fault handling solution, but also involves the collaborative efficiency of multiple factors such as system architecture and resource scheduling. In order to achieve the optimal solution execution time, the container platform needs to maintain efficient collaboration in detection, decision-making, scheduling and recovery, ensure that the recovery operations of each node are carried out synchronously, and avoid business interruptions and data inconsistencies. This requires the container platform to have strong scheduling capabilities and network optimization strategies to achieve the lowest latency and highest system stability.

[0066] (2) Resource utilization of fault handling solutions:

[0067] This evaluation index aims to evaluate the efficient use of system resources (including computing, storage, and network bandwidth) during the execution of the fault handling plan. The fault handling plan must minimize the overall system resource usage while ensuring the efficiency of fault repair, so as to improve resource availability and optimize the overall operating cost.

[0068] (3) Service impact range, i.e., the impact range parameters of the fault handling solution on the first service:

[0069] This evaluation indicator is used to assess the depth and breadth of the impact of the fault handling plan on normal services during its execution, including specific impact range parameters in terms of service performance degradation, availability interruption, and user experience. An ideal fault handling plan efficiently completes fault repair while minimizing the impact on user services, ensuring the continuity and stability of business services. To achieve this goal, the fault handling plan should be able to dynamically adjust the operating status of the system during the fault repair process through resource scheduling and load balancing to minimize the impact on user perception. In addition, the assessment of the service impact range also includes an analysis of the degree of impact on key service components, thereby ensuring priority protection of important business links and minimizing the risk of interruption of core business functions.

[0070] (4) Fault recurrence rate of fault handling solution:

[0071] This evaluation index is used to evaluate the probability of recurrence of faults after the execution of the fault handling solution, and to measure the long-term effectiveness and robustness of the solution. A low fault recurrence rate means that the solution not only solves the current fault, but also has the potential to prevent similar faults from happening again, thereby improving the overall stability and reliability of the system. Through in-depth analysis of historical fault data, the robustness and effectiveness of the fault handling solution can be verified, which helps to evaluate the adaptability of the solution in a dynamic environment.

[0072] (5) Operational complexity, that is, the operational complexity of the fault handling solution:

[0073] This evaluation indicator is used to assess the number and complexity of manual operation steps involved in the execution of the fault handling solution. The fewer the operation steps and the simpler the process, the easier it is to automate the deployment of the fault handling solution, thereby effectively reducing the risk of human error and improving the overall reliability and handling efficiency of the system. By reducing manual intervention and introducing standardized and automated operation and maintenance measures, the platform can better adapt to rapid response needs and ensure efficient execution.

[0074] (6) Multi-node collaborative processing capability, that is, the collaborative processing parameters of the fault handling solution in the scenario of multiple running nodes:

[0075] When a system failure extends to multiple nodes, this evaluation indicator assesses the coordination and consistency of the fault handling solution in a multi-node environment. Specifically, the fault handling solution needs to have effective load redistribution capabilities and an efficient processing mechanism for synchronous recovery of multiple nodes to ensure the consistency of the overall service and system stability. This includes resource coordination between nodes, management of parallel recovery operations, and avoiding resource competition and service interruptions that may arise during the recovery of different nodes. The container platform should demonstrate a high degree of parallel processing capabilities and network communication optimization to minimize recovery time and maintain service continuity in a multi-node environment.

[0076] (7) Business continuity impact, i.e., the impact of the fault handling solution on the continuity of the second service:

[0077] This evaluation indicator is used to assess the impact of each fault handling solution on the continuity of key business services during its execution. An ideal fault handling solution should minimize the interruption time of key business through systematic intervention and automated repair, ensuring that business processes can maintain the highest possible continuity and stability during a fault, thereby effectively reducing the negative impact on the end-user experience.

[0078] (8) Dynamic adaptability, i.e., adjustment parameters of the execution strategy of the fault handling solution:

[0079] This evaluation index assesses the adaptability of the fault handling solution in dealing with dynamic changes in system status, that is, whether the fault handling solution can dynamically adjust its execution strategy based on real-time system resource utilization, load status and other information to achieve the best repair effect. Through the adaptive adjustment mechanism, the fault handling solution needs to demonstrate the ability to maintain efficient and stable operation in a constantly changing environment, thereby effectively reducing fault recovery delays caused by resource bottlenecks or load imbalances, and ensuring the robustness and recovery efficiency of the system.

[0080] like Figure 3 As shown in , the plan evaluation index system corresponding to the fault handling plan of the container platform contains multiple core evaluation indicators, which are used to comprehensively evaluate the effectiveness and adaptability of the fault handling plan. These evaluation indicators include plan execution time, resource utilization, service impact range, fault recovery rate, operation complexity, multi-node collaborative processing capability, business continuity impact, and dynamic adaptability. These indicators are evaluated by combining production environment data to ensure that the process is scientific and reasonable, and finally select the optimal fault handling plan. Through this evaluation index system, the feasibility and efficiency of the plan can be scientifically evaluated from multiple dimensions, providing a reliable basis for fault management of the container platform.

[0081] Based on this, in this embodiment, for each fault handling plan, according to the evaluation indicators in the evaluation indicator system, data collection can be performed on the operating status of the container platform before, during and after the execution of the fault handling plan, including dynamic data collection and static data collection. Then, based on the collected dynamic data and static data, the current indicator values ​​of the fault handling plan on evaluation indicators such as execution time, resource utilization, fault recurrence rate, operation complexity, and execution strategy adjustment parameters are calculated.

[0082] In addition, in this embodiment, for each fault handling plan, a corresponding indicator setting operation can be obtained based on the collected dynamic data and static data. For example, experts set indicator values ​​for evaluation indicators based on dynamic data and static data, and in response to the indicator setting operation, current indicator values ​​on evaluation indicators such as impact range parameters for the first service, collaborative processing parameters in a multi-operating node scenario, continuity impact parameters for the second service, and execution strategy adjustment parameters are obtained.

[0083] For example, in this embodiment, the running status of the container platform can be collected in real time, including dynamic data such as CPU usage, memory usage, and network status. Use monitoring tools (such as Prometheus) to comprehensively monitor each node and the container in the node to ensure that all relevant data can be captured when a fault occurs. In the operating environment of the container platform, these data help identify the health status of the system and resource bottlenecks, ensuring that various types of abnormal information can be accurately captured at the beginning of the fault, and providing a detailed basis for subsequent fault handling. For example, in this embodiment, static data related to the long-term stability of the container platform can be collected, such as historical fault frequency, resource utilization records, and system reliability. Historical data is collected regularly by using log collection tools (such as ELK Stack) to evaluate the long-term performance of the container platform. These historical data are crucial for building prediction models and performing trend analysis, which can help the platform better understand resource usage patterns, identify potential system bottlenecks, and provide a basis for future system optimization and capacity planning. In addition, in the operation of the container platform, static indicators can also effectively assist in the formulation of container scheduling strategies, ensuring that the number of container instances, node distribution, etc. are adapted to the system load, thereby improving the overall stability and reliability of the platform.

[0084] Furthermore, after the dynamic data and static data are collected in this embodiment, data preprocessing can be performed first, and then the current index value of each evaluation index can be calculated based on these data. For example, in this embodiment, various types of collected data can be processed uniformly, and the preprocessing operations include any one or more of the following:

[0085] (1) Missing data processing: Use interpolation or average filling methods to process missing values ​​in various types of collected data to ensure data integrity.

[0086] (2) Normalization: The collected data are processed by maximum and minimum value normalization or Z-score standardization, so that different types of indicators can be compared under the same standard. Normalization can eliminate the dimensional differences between different indicators and ensure that the subsequent evaluation process is more reasonable and scientific.

[0087] (3) Outlier detection: Use statistical methods to detect and process abnormal data in the collected data to ensure the quality and validity of the data.

[0088] Step 102: Obtain a first treatment plan and a second treatment plan according to the current indicator value.

[0089] Among them, the index value of the first treatment plan on each evaluation index is the best among all fault treatment plans; the index value of the second treatment plan on each evaluation index is the worst among all fault treatment plans.

[0090] In one implementation, the first solution is the solution with the best indicator values ​​on each evaluation indicator, and the second solution is the solution with the worst indicator values ​​on each evaluation indicator. Therefore, the first solution represents the best fault solution under ideal conditions, and can also be called a positive ideal solution; the second solution represents the worst fault solution under ideal conditions, and can also be called a negative ideal solution.

[0091] Specifically, in this embodiment, the best indicator value and the worst indicator value corresponding to each evaluation indicator can be firstly screened out from the current indicator value of each evaluation indicator of all fault handling solutions; then, based on the best indicator value corresponding to each evaluation indicator, a first handling solution can be constructed, and based on the worst indicator value corresponding to each evaluation indicator, a second handling solution can be constructed.

[0092] For example, taking 8 evaluation indicators and 5 fault handling solutions as an example, each fault handling solution has a current indicator value on the 8 evaluation indicators, and each evaluation indicator has a current indicator value on the 5 fault handling solutions. Based on this, the best indicator value and the worst indicator value are selected from the 5 current indicator values ​​corresponding to each evaluation indicator on the 5 fault handling solutions. The 8 best indicator values ​​on the selected 8 evaluation indicators are used to construct the first handling solution, and the 8 worst indicator values ​​on the selected 8 evaluation indicators are used to construct the second handling solution.

[0093] It should be noted that the best indicator value and the worst indicator value corresponding to the evaluation indicator can be determined based on the indicator type to which the evaluation indicator belongs and the numerical value of the indicator value.

[0094] For example, when the evaluation indicator is a benefit-type indicator such as fault recurrence rate, collaborative processing parameters in a multi-running node scenario, or execution strategy adjustment parameters, the best indicator value corresponding to the evaluation indicator is the current indicator value with the largest numerical value; the worst indicator value corresponding to the evaluation indicator is the current indicator value with the smallest numerical value.

[0095] When the evaluation indicator is a cost-type indicator such as execution time, resource utilization, impact range parameter on the first service, operation complexity or continuity impact parameter on the second service, the best indicator value corresponding to the evaluation indicator is the current indicator value with the smallest numerical value; the worst indicator value corresponding to the evaluation indicator is the current indicator value with the largest numerical value.

[0096] Step 103: According to the first solution and the second solution, a target solution that meets the optimization condition is selected from a plurality of fault solution.

[0097] In one implementation, in this embodiment, a solution that is closer to the first solution and less close to the second solution can be screened out from multiple fault handling solutions as a target handling solution.

[0098] By means of the above technical solution, in a method for selecting a preferred container platform fault handling solution provided by an embodiment of the present application, the current index values ​​of multiple fault handling solutions of the container platform on multiple evaluation indicators are obtained to obtain a first handling solution and a second handling solution, the index value corresponding to each evaluation indicator in the first handling solution is the best among all fault handling solutions, and the index value corresponding to each evaluation indicator in the second handling solution is the worst among all fault handling solutions, based on which, according to the handling solution composed of the best index value and the handling solution composed of the worst index value, a target handling solution that meets the preferred conditions is screened out from multiple fault handling solutions. It can be seen that in this embodiment, by constructing an index system composed of multiple evaluation indicators, the best handling solution and the worst handling solution can be constructed by the index values ​​of each fault handling solution on these evaluation indicators, and the target handling solution can be screened out from multiple fault handling solutions accordingly, without the need for manual screening by operation and maintenance personnel, which can improve the accuracy of selecting the best fault handling solution.

[0099] In a possible implementation, in this embodiment, before obtaining the first treatment plan and the second treatment plan according to the current indicator value in step 102, the current indicator value may be weighted according to the indicator weight corresponding to each evaluation indicator.

[0100] For example, in this embodiment, the indicator weight corresponding to each evaluation indicator may be multiplied by the current indicator value of the corresponding evaluation indicator to obtain the weighted current indicator value.

[0101] In one implementation, the indicator weight corresponding to the evaluation indicator can be obtained based on the received weight setting operation. For example, the user can perform a weight setting operation on each evaluation indicator, based on which, in this embodiment, in response to the received weight setting operation, the indicator weight corresponding to the corresponding evaluation indicator is generated.

[0102] In another implementation, the indicator weight corresponding to the evaluation indicator can be obtained by the following method: Figure 4 As shown in:

[0103] Step 401: Obtain multiple indicator values ​​of all fault handling solutions on each evaluation indicator within a target duration.

[0104] In this embodiment, dynamic data and static data of the fault handling solution in the target duration can be collected, and multiple indicator values ​​on each evaluation indicator can be calculated based on the data.

[0105] Step 402: Obtain an entropy value corresponding to each evaluation indicator according to multiple indicator values.

[0106] The entropy value represents the fluctuation state of the index value. The larger the entropy value, the greater the fluctuation of the index value on the evaluation index; the smaller the entropy value, the smaller the fluctuation of the index value on the evaluation index.

[0107] Step 403: Obtain the indicator weight corresponding to each evaluation indicator according to the entropy value.

[0108] Specifically, in this embodiment, the indicator weight corresponding to the evaluation indicator can be set to a corresponding value according to the numerical range of the entropy value. For example, when the entropy value is greater than the first threshold, the indicator weight corresponding to the evaluation indicator is set to a smaller value; when the entropy value is less than the second threshold, the indicator weight corresponding to the evaluation indicator is set to a larger value, and the first threshold is greater than the second threshold.

[0109] For example, in this embodiment, the indicator weight corresponding to the evaluation indicator can be obtained by any of the following methods:

[0110] (1) Entropy weight method: The entropy weight method is used to calculate the weight of each evaluation index to reflect the importance of different evaluation indicators to the fault handling solution. The larger the entropy value, the less information the indicator has and the lower the corresponding indicator weight. Conversely, the higher the indicator weight.

[0111] (2) Custom weight method: Users can set custom indicator weights based on experience to cope with different business scenarios and increase flexibility. For example, in a specific business scenario, users can set the system response time to be more important and assign a larger indicator weight.

[0112] In one implementation, when selecting a target solution that meets the preferred condition from multiple fault solution solutions in step 102, it can be implemented in the following manner: Figure 5 As shown in:

[0113] Step 501: According to the current index value of each evaluation index of the fault handling scheme, respectively obtain the first evaluation distance and the second evaluation distance corresponding to each fault handling scheme.

[0114] Among them, the first evaluation distance is the distance between the fault handling plan and the first handling plan; the second evaluation distance is the distance between the fault handling plan and the second handling plan.

[0115] In one implementation, in this embodiment, the Euclidean distance calculation method can be used to calculate the Euclidean distance between each fault handling solution and the first handling solution and the second handling solution, that is, the evaluation distance, wherein the Euclidean distance between the fault handling solution and the first handling solution is the first evaluation distance, and the Euclidean distance between the fault handling solution and the second handling solution is the second evaluation distance.

[0116] Step 502: Filter out a target handling solution that meets the optimization condition from a plurality of fault handling solutions according to the first evaluation distance and the second evaluation distance.

[0117] Specifically, in this embodiment, according to the magnitude of the first evaluation distance and the second evaluation distance, a solution that is closer to the first solution and less close to the second solution can be screened out from multiple fault handling solutions as a target solution.

[0118] In one implementation method, in this embodiment, the first relative closeness between each fault handling plan and the first handling plan can be first obtained based on the first evaluation distance and the second evaluation distance. The first relative closeness represents the degree of closeness between the fault handling plan and the first handling plan. The smaller the first relative closeness, the closer the fault handling plan is to the first handling plan. Based on this, from multiple fault handling plans, a target handling plan whose first relative closeness meets the first preferred condition is screened out.

[0119] The first optimal condition may be that, among multiple fault handling solutions, the first relative closeness corresponding to the target handling solution is the smallest.

[0120] For example, in this embodiment, the first relative closeness between the i-th fault handling solution and the first handling solution can be calculated according to formula (1):

[0121]

[0122] in, is the second evaluation distance between the i-th fault handling solution and the second handling solution, is the second evaluation distance between the i-th fault handling solution and the first handling solution, C i is the first relative closeness between the i-th fault handling solution and the first handling solution.

[0123] Based on this, the smaller the first relative closeness is, the better the i-th fault handling solution is.

[0124] In another implementation method, in this embodiment, the second relative closeness between each fault handling plan and the second handling plan can be first obtained based on the first evaluation distance and the second evaluation distance. The second relative closeness represents the degree of closeness between the fault handling plan and the second handling plan. The larger the second relative closeness, the less close the fault handling plan is to the second handling plan. Based on this, from multiple fault handling plans, a target handling plan whose second relative closeness meets the second preferred condition is screened out.

[0125] The second optimal condition may be that, among the multiple fault handling solutions, the second relative closeness corresponding to the target handling solution is the largest.

[0126] For example, in this embodiment, the second relative closeness between the i-th fault handling solution and the second handling solution can be calculated according to formula (2):

[0127]

[0128] Based on this, the greater the second relative closeness, the better the i-th fault handling solution.

[0129] A method for selecting a best method for handling a container platform failure provided in an embodiment of the present application is introduced above. A device for executing the method for selecting a best method for handling a container platform failure is introduced below.

[0130] See also Figure 6 , is a schematic diagram of the structure of a device for selecting the best container platform fault handling solution provided in an embodiment of the present application. The device can be configured in an electronic device capable of data processing, such as a computer or a server. The technical solution in this embodiment is mainly used to improve the accuracy of selecting the best fault handling solution.

[0131] Specifically, the device in this embodiment may include the following units:

[0132] The indicator obtaining unit 601 is used to obtain the current indicator values ​​of multiple fault handling solutions on multiple evaluation indicators respectively; the fault handling solutions are used to handle the faults caused by the container platform implementing the business service; the container platform is built based on multiple running nodes, and the running nodes implement the corresponding business services through the running containers;

[0133] A solution obtaining unit 602 is used to obtain a first treatment solution and a second treatment solution according to the current indicator value, wherein the indicator value of the first treatment solution on each evaluation indicator is the best among all the fault treatment solutions; and the indicator value of the second treatment solution on each evaluation indicator is the worst among all the fault treatment solutions;

[0134] The solution screening unit 603 is used to screen out a target solution that meets the optimization condition from the multiple fault handling solutions according to the first handling solution and the second handling solution.

[0135] By means of the above technical solution, in a device for selecting the best container platform fault handling solution provided by an embodiment of the present application, the first handling solution and the second handling solution are obtained by obtaining the current index values ​​of multiple fault handling solutions of the container platform on multiple evaluation indicators, respectively. The index value corresponding to each evaluation indicator in the first handling solution is the highest among all fault handling solutions. The index value corresponding to each evaluation index in the second treatment scheme is the best among all fault treatment schemes. Difference, based on this, root According to the treatment plan composed of the best index value and the treatment plan composed of the worst index value, a target treatment plan that meets the optimization condition is screened out from multiple fault treatment plans. It can be seen that in this embodiment, by constructing an index system composed of multiple evaluation indicators, the best treatment plan and the worst treatment plan can be constructed by the index value of each fault treatment plan on these evaluation indicators, and the target treatment plan can be screened out from multiple fault treatment plans accordingly, without the need for manual screening by operation and maintenance personnel, which can improve the accuracy of selecting the best fault treatment plan.

[0136] In one implementation, the evaluation indicators include any number of the following: the execution time of the fault handling plan, the resource utilization of the fault handling plan, the impact range parameter of the fault handling plan on the first service, the fault recurrence rate of the fault handling plan, the operational complexity of the fault handling plan, the collaborative processing parameters of the fault handling plan in a multi-running node scenario, the continuity impact parameter of the fault handling plan on the second service, and the execution strategy adjustment parameter of the fault handling plan; wherein, the first service is a business service that meets the first condition and is implemented by the container platform; the second service is a business service that meets the second condition and is implemented by the container platform.

[0137] In one implementation, before obtaining the first treatment plan and the second treatment plan according to the current indicator value, the solution obtaining unit 602 is further configured to: perform weighted processing on the current indicator value according to the indicator weight corresponding to each evaluation indicator.

[0138] In one implementation, the device in this embodiment may further include the following units: Figure 7 As shown in:

[0139] The weight obtaining unit 604 is used to obtain multiple indicator values ​​of all the fault handling solutions on each of the evaluation indicators within the target duration; according to the multiple indicator values, obtain the entropy value corresponding to each of the evaluation indicators; the entropy value characterizes the fluctuation state of the indicator value; according to the entropy value, obtain the indicator weight corresponding to each of the evaluation indicators.

[0140] In one implementation, the solution screening unit 603 is specifically used to: obtain the first evaluation distance and the second evaluation distance corresponding to each fault handling solution according to the current indicator value of the fault handling solution on each of the evaluation indicators; the first evaluation distance is the distance between the fault handling solution and the first handling solution; the second evaluation distance is the distance between the fault handling solution and the second handling solution; based on the first evaluation distance and the second evaluation distance, screen out the target handling solution that meets the preferred conditions from the multiple fault handling solutions.

[0141] In one implementation, when the solution screening unit 603 screens out a target solution that meets the preferred condition from the multiple fault handling solutions based on the first evaluation distance and the second evaluation distance, it is specifically used to: obtain a first relative closeness between each of the fault handling solutions and the first solution based on the first evaluation distance and the second evaluation distance; and screen out a target solution from the multiple fault handling solutions whose first relative closeness meets the first preferred condition.

[0142] In one implementation, when the solution screening unit 603 screens out a target solution that meets the preferred condition from the multiple fault handling solutions based on the first evaluation distance and the second evaluation distance, it is specifically used to: obtain a second relative closeness between each of the fault handling solutions and the second solution based on the first evaluation distance and the second evaluation distance; and screen out a target solution whose second relative closeness meets the second preferred condition from the multiple fault handling solutions.

[0143] In one implementation, the solution acquisition unit 602 is specifically used to: filter out the best indicator value and the worst indicator value corresponding to each of the evaluation indicators from the current indicator values ​​of all the fault handling solutions on each of the evaluation indicators; construct a first handling solution based on the best indicator value corresponding to each of the evaluation indicators; and construct a second handling solution based on the worst indicator value corresponding to each of the evaluation indicators.

[0144] It should be noted that the specific implementation method of each unit in this embodiment can refer to the corresponding content in the previous text and will not be described in detail here.

[0145] An electronic device is also provided in an embodiment of the present application. The electronic device comprises:

[0146] A memory, used to store a computer program and data generated by the execution of the computer program;

[0147] A processor, configured to execute the computer program to implement:

[0148] Obtaining current index values ​​of multiple fault handling solutions on multiple evaluation indicators respectively; the fault handling solutions are used to handle faults caused by the container platform implementing business services; the container platform is built based on multiple running nodes, and the running nodes implement corresponding business services through the running containers;

[0149] According to the current indicator value, a first treatment plan and a second treatment plan are obtained, wherein the indicator value of the first treatment plan on each evaluation indicator is the best among all the fault treatment plans; and the indicator value of the second treatment plan on each evaluation indicator is the worst among all the fault treatment plans;

[0150] According to the first handling solution and the second handling solution, a target handling solution that meets the optimization condition is screened out from the multiple fault handling solutions.

[0151] refer to Figure 8 As shown, it shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic device in the embodiments of the present application may include but is not limited to fixed terminals such as mobile phones, notebook computers, PDAs (personal digital assistants), PADs (tablet computers), desktop computers, etc. Figure 8 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0152] like Figure 8 As shown, the electronic device may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 801, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 802 or the program loaded from the storage device 808 to the random access memory (RAM) 803 to implement the preferred method of the container platform fault handling solution implemented in the above embodiments. When the electronic device is powered on, various programs and data required for the operation of the electronic device are also stored in the RAM 803. The processing device 801, ROM 802 and RAM 803 are connected to each other via a bus 804. The input / output (I / O) interface 805 is also connected to the bus 804.

[0153] Typically, the following devices may be connected to the I / O interface 805: input devices 806 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 807 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 808 including, for example, a memory card, a hard disk, etc.; and communication devices 809. The communication device 809 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 8 An electronic device having various devices is shown, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0154] An embodiment of the present application also provides a computer program product including computer-readable instructions. When the computer-readable instructions are executed on an electronic device, the electronic device implements a method for selecting the best of any container platform fault handling solutions provided in the embodiments of the present application.

[0155] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When the one or more computer programs are executed by an electronic device, the electronic device can implement a method for selecting the best of any container platform fault handling solutions provided in an embodiment of the present application.

[0156] The following is an example of the technical solution of this application:

[0157] First, in this embodiment, the TOPSIS algorithm can be used to evaluate various fault handling solutions, such as Fig. 9 As shown in the figure, the specific process of the TOPSIS algorithm includes the following main steps: first, a decision matrix is ​​constructed, and then the decision matrix is ​​standardized. Next, a weighted standardized decision matrix is ​​constructed, and the positive ideal solution and the negative ideal solution are identified. Finally, the distance from each solution to the positive ideal solution and the negative ideal solution is calculated to complete the sorting and optimization of the solutions. This process provides a systematic method for the scientific analysis and solution of multi-criteria decision-making problems. The process is as follows:

[0158] Step 901: Construct a decision matrix:

[0159] Through the data collection process, relevant data of each fault handling solution is obtained to calculate the current index value of each evaluation index.

[0160] Assume that there are multiple fault handling solutions, and each fault handling solution will be scored on several evaluation indicators. Assume that there are m fault handling solutions and n evaluation indicators, and construct an m×n matrix, denoted as X. Each matrix element X ij It represents the current index value of the i-th fault handling solution on the j-th evaluation index.

[0161] Step 902: Standardize the decision matrix:

[0162] In order to eliminate the influence of the dimensions of each evaluation index, the decision matrix is ​​standardized so that each element is between [0,1]. For example, vector normalization. In the application scenario of container platform fault handling, the current index value of each fault handling solution on the evaluation index can be standardized. For example, the current index value of the fault handling solution on the evaluation index can be standardized according to formula (3), and the original index value can be mapped to the same scale.

[0163]

[0164] Among them, r ij is the normalized current index value of the i-th fault handling solution on the j-th evaluation index; x ij is the current index value of the i-th fault handling solution on the j-th evaluation index before normalization.

[0165] Step 903: Construct a weighted standardized decision matrix:

[0166] The weight of each evaluation index is introduced to reflect the influence of different evaluation indexes on the final decision. The weight of the jth evaluation index is w j (obtained by entropy weight method or custom setting), the elements of the weighted normalization matrix are shown in formula (4):

[0167] v ij = w j × r ij (4)

[0168] Among them, v ij It is the normalized and weighted current index value of the i-th fault handling solution on the j-th evaluation index.

[0169] Step 904: Determine the positive ideal solution (i.e., the first treatment plan) and the negative ideal solution (i.e., the second treatment plan):

[0170] Positive ideal solution A + and negative ideal solution A - Represent the best and worst cases of each evaluation indicator. For each evaluation indicator, the ideal solution is the maximum value (if it is a benefit indicator) or the minimum value (if it is a cost indicator) of the evaluation indicator.

[0171] The positive ideal solution (optimal target) is shown in formula (5):

[0172]

[0173] in, is the optimal index value of the second evaluation index, as shown in formula (7):

[0174]

[0175] in, is the optimal index value of the jth evaluation index.

[0176] The negative ideal solution (worst target) is shown in formula (8):

[0177]

[0178] in, is the worst indicator value of the second evaluation indicator, as shown in formula (9):

[0179]

[0180] Step 905: Calculate the distances of each solution to the positive ideal solution and the negative ideal solution (i.e., the first evaluation distance and the second evaluation distance mentioned above), calculate the relative closeness, and sort and select the best solution:

[0181] Based on the decision matrix corresponding to the weighted standardized evaluation index, the Euclidean distance between each fault handling solution and the positive ideal solution and the negative ideal solution is calculated, as shown in formula (10) and formula (11):

[0182]

[0183] The relative closeness of each solution is calculated, as shown in formula (2). The larger the relative closeness, the better the solution. Based on this, the various fault handling solutions are ranked according to the relative closeness. The larger the value, the better the fault handling solution. Then, the various fault handling solutions are ranked to select the best fault handling solution.

[0184] Based on the above implementation, in this embodiment, various fault handling solutions can be ranked according to the results calculated by the TOPSIS algorithm, and the optimal fault handling solution can be selected for application to ensure efficient and stable operation of the system.

[0185] (1) Automated execution: The evaluation results will be directly input into the automated execution module of the system, and the optimal solution will be immediately deployed after selection. This can shorten the response time and avoid further system problems caused by manual operation delays.

[0186] (2) Feedback and adjustment: After the plan is executed, the system will provide feedback and evaluation on the execution effect. If the effect is not ideal, the system will re-adjust the weight and select the plan, forming a closed-loop optimization process.

[0187] like Fig.10As shown in , the specific process of selecting the optimal method for the container platform fault handling plan (i.e., fault handling solution) includes the following steps:

[0188] Step 1001: Establish an evaluation index system for container platform fault handling solutions to lay the foundation for subsequent evaluation.

[0189] Step 1002: collecting data;

[0190] Step 1003: Perform data preprocessing to ensure the accuracy and consistency of the evaluation data.

[0191] Step 1004: assigning different importance to each indicator through a weight determination method;

[0192] Step 1005: Evaluate and optimize each fault handling solution.

[0193] Step 1006: Sort the fault handling solutions according to the evaluation results, and select the optimal fault handling solution to support efficient fault handling decisions of the container platform.

[0194] like Fig.11 As shown in , the optimization selection of container platform fault handling solutions includes two key processes: the "solution selection optimization process" and the "TOPSIS algorithm process", which are closely linked.

[0195] (1) Index system and data provision:

[0196] In the process of scheme selection and optimization, the first step is to establish the evaluation index system of the disposal scheme of the container platform. This is the starting point of the whole process and determines the standard and direction of the evaluation. This index system provides the core index dimensions and data support required for the TOPSIS algorithm to build the decision matrix. Data collection and index determination directly affect the input data of the TOPSIS method, that is, the data source used to build the decision matrix.

[0197] (2) Data preprocessing and standardization:

[0198] In the data preprocessing stage, the scheme selection optimization process cleans, corrects and standardizes the collected data. The results of these processes are directly passed to the standardized decision matrix construction stage of the TOPSIS algorithm. Data preprocessing ensures the consistency and comparability of the data, thereby ensuring the quality of the data and the rationality of the calculation during the execution of the TOPSIS algorithm.

[0199] (3) Weight determination and weighting matrix construction:

[0200] In the step of "determining indicator weights" in the process of optimizing the scheme selection, the indicator weights of each evaluation indicator are calculated using methods such as the entropy weight method (this method can objectively measure the amount of information of each indicator to reduce the influence of human subjective factors). These indicator weights will be directly used in the construction process of the "weighted standardized decision matrix" of the TOPSIS algorithm. By introducing indicator weights into standardized indicators, TOPSIS can reasonably consider the importance of each indicator, thereby evaluating each scheme more scientifically.

[0201] (4) Evaluation and ideal solution analysis:

[0202] In the process of optimizing the scheme selection, the index weight is used to evaluate the pros and cons of the scheme, which corresponds to the steps of "determining the positive ideal solution and the negative ideal solution" and "calculating the distance between the scheme and the positive ideal solution and the negative ideal solution" in the TOPSIS algorithm. The TOPSIS algorithm measures the degree of proximity of each scheme to the optimal and worst cases by calculating the Euclidean distance between each scheme and the positive ideal solution and the negative ideal solution, thereby providing a quantitative basis for the overall scheme evaluation and selection.

[0203] (5) Selection of the best and the best solution:

[0204] The "evaluation result ranking and solution selection" of the solution selection optimization process is directly related to the "calculation of the distance between each solution and the positive ideal solution and the negative ideal solution" of the TOPSIS algorithm. The calculation results of the TOPSIS algorithm will give the degree of closeness of each solution to the positive ideal solution or the negative ideal solution. The solution selection optimization process will perform the final ranking and optimization of the solutions based on these calculation results, thereby achieving a comprehensive comparison of all candidate solutions and ultimately selecting the optimal solution.

[0205] like Fig.11 As shown in the figure, the correspondence between the process of selecting the optimal solution for the container platform fault handling solution and the TOPSIS algorithm process is shown. The optimal solution selection process starts with the establishment of an evaluation index system, and goes through the steps of data collection, data preprocessing, indicator weight determination (such as entropy weight method), solution evaluation and optimization. The TOPSIS algorithm, as a key technical support in this process, completes the sorting and optimization of solutions by constructing a decision matrix, standardization processing, weighted calculation, and distance analysis between positive ideal solutions and negative ideal solutions. The two complement each other and together form a complete optimization and evaluation system for container platform fault handling solutions.

[0206] In order to more clearly illustrate the actual application effect of the method for selecting the best container platform fault handling plan, this example will be based on the aforementioned method system and will use specific examples to demonstrate the specific process of the method in detail. The example will cover the entire process from the establishment of the evaluation index system, data collection and preprocessing, to weight determination, solution evaluation and optimization, and further intuitively present how the method for selecting the best container platform fault handling plan can efficiently support solution optimization and decision-making. Through example analysis, we can not only verify the scientificity and practicality of the method, but also provide reference and guidance for applications in similar scenarios.

[0207] 1. Example background:

[0208] In the daily operation of a certain enterprise container platform, due to the large number of microservices and high resource load, it often faces sudden failures, such as long service response time or node downtime. In order to improve the platform's fault response capabilities,

[0209] The technical team has designed five sets of fault handling solutions or contingency plans, hoping to select the best solution through a merit-based approach to improve the reliability and efficiency of the platform.

[0210] Plan Overview:

[0211] Plan A: After a failure occurs, prioritize expanding service instances and adding load balancing nodes to distribute requests and ensure service availability.

[0212] Plan B: Dynamically adjust resource allocation strategies to prioritize CPU and memory resources for core services to quickly restore critical business.

[0213] Plan C: Restart the faulty node and redistribute traffic to healthy nodes to quickly isolate the problem node and reduce the scope of the fault.

[0214] Plan D: Start the redundant backup of the failed service, switch traffic to the backup service instance, and analyze and repair the original service failure.

[0215] Plan E: Initiate a global downgrade strategy, temporarily shut down low-priority services, concentrate resources on handling high-priority services, and inform users through a notification mechanism.

[0216] 2. Establish an evaluation index system:

[0217] According to the actual needs of the container platform, the evaluation indicators include the following dimensions:

[0218] (1) Plan execution time (unit: minutes): evaluates the time required for the fault handling plan to be started and completed. The shorter the time, the better, reflecting the plan's rapid response capability.

[0219] (2) Resource utilization (unit: %): This evaluates the efficiency of the use of platform resources (such as CPU, memory, network, etc.) during the execution of the plan. The higher the utilization rate, the better, which reflects the degree of resource optimization.

[0220] (3) Service impact range (unit: number of affected services): The number of services affected by failures or handling processes during the implementation of the evaluation plan. The smaller the impact range, the better, which reflects the stability of the plan.

[0221] (4) Fault recovery rate (unit: %): This evaluates the degree to which the system recovers to normal operating status after the fault is resolved. The higher the recovery rate, the better, which reflects the effectiveness of the solution.

[0222] (5) Operational complexity (unit: score, 1-10 points): evaluates the complexity of the solution implementation process. The lower the score, the simpler the operation, reflecting the ease of use and deployment of the solution.

[0223] (6) Multi-node collaborative processing capability (unit: score, 1-10 points): evaluates the processing capability of the solution in a multi-node environment. The score is scored by experts. The higher the score, the better. It reflects the adaptability of the solution in complex distributed scenarios.

[0224] (7) Business continuity impact (unit: score, 1-10 points): Evaluates the impact of the solution on business continuity during implementation. The lower the score, the smaller the impact on the business, reflecting the solution's friendliness to business support.

[0225] (8) Dynamic adaptability (unit: score, 1-10 points): evaluates the solution's ability to flexibly respond to different failure scenarios. The score is scored by experts. The higher the score, the better, reflecting the adaptability of the solution.

[0226] The current index values ​​of the evaluation indicators are shown in Table 1:

[0227] Table 1 Evaluation indicators

[0228]

[0229] The decision matrix X is constructed as shown in formula (12):

[0230]

[0231] After going through the processing flow in the previous article, select the best fault handling plan.

[0232] It can be seen that the technical solution of the present application has the following beneficial effects:

[0233] (1) Enhanced adaptive capabilities:

[0234] This application combines the TOPSIS algorithm with the entropy weight method to achieve the system's ability to select adaptive fault handling solutions in a complex operating environment. By dynamically adjusting the indicator weights of each evaluation indicator through the entropy weight method, and using the TOPSIS algorithm to optimize the fault handling solution, this application can adaptively select the best fault handling solution based on the current system state based on the consideration of the system's multi-dimensional performance indicators (such as resource utilization, response speed, load conditions, etc.). This method significantly improves the system's handling of faults, allowing the container platform to remain stable and efficient in a constantly changing operating environment. At the same time, the adaptive mechanism enables the platform to adjust the handling solution in a targeted manner when facing different types of faults, reduce system interruption time, and improve the continuity and reliability of the overall business.

[0235] (2) Improve the scientific nature of decision-making:

[0236] This application adopts a systematic multi-index decision-making method to comprehensively evaluate and rank each alternative fault handling solution, so as to ensure that the final selected fault handling solution has the best overall performance under the current complex conditions. By introducing a weight allocation mechanism based on the entropy weight method and combining the multi-dimensional optimization strategy of the TOPSIS algorithm, it can effectively integrate multiple system performance indicators (such as resource utilization, response time, recovery cost, etc.) to quantify the effectiveness of each candidate solution and conduct scientific ranking evaluation. This method overcomes the limitations of single priority or empirical rules in traditional decision-making, enhances the scientificity and rationality of decision-making, and ensures that the selected fault handling solution has the best response capabilities under different fault types and system states, thereby greatly improving the fault handling efficiency and system reliability of the container platform.

[0237] (3) Reduce manual intervention:

[0238] This application significantly reduces the reliance on operation and maintenance personnel through a highly automated fault detection and handling process, thereby achieving autonomous and intelligent management of system operation. The system's built-in automation mechanism can not only actively monitor the occurrence of faults, but also quickly start preset repair measures without human intervention. Through intelligent scheduling algorithms and event-driven self-healing mechanisms, the system can analyze the root causes of faults in real time and select appropriate solutions for implementation. The design that reduces human intervention significantly improves the response speed of the system, thereby reducing processing delays or potential misoperations caused by human intervention. In this way, this application effectively improves fault recovery efficiency, enhances the stability and reliability of the system, and ensures that the platform can maintain continuous and efficient operation in the face of various emergencies.

[0239] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0240] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a training device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0241] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0242] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a training device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, training device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. A method for selecting the best solution for container platform failure handling, characterized in that: The method comprises: Obtaining current index values ​​of multiple fault handling solutions on multiple evaluation indicators respectively; the fault handling solutions are used to handle faults caused by the container platform implementing business services; the container platform is built based on multiple running nodes, and the running nodes implement corresponding business services through the running containers; According to the current indicator value, a first treatment plan and a second treatment plan are obtained, wherein the indicator value of the first treatment plan on each evaluation indicator is the best among all the fault treatment plans; and the indicator value of the second treatment plan on each evaluation indicator is the worst among all the fault treatment plans; According to the first handling solution and the second handling solution, a target handling solution that meets the optimization condition is screened out from the multiple fault handling solutions.

2. The method according to claim 1, characterized in that The evaluation index includes any number of the following: execution time of the fault handling solution, resource utilization rate of the fault handling solution, impact range parameter of the fault handling solution on the first service, fault recurrence rate of the fault handling solution, operation complexity of the fault handling solution, collaborative processing parameter of the fault handling solution in a multi-operating node scenario, continuity impact parameter of the fault handling solution on the second service, and execution strategy adjustment parameter of the fault handling solution; The first service is a business service implemented by the container platform that meets the first condition; the second service is a business service implemented by the container platform that meets the second condition.

3. The method according to claim 1 or 2, characterized in that: Before obtaining the first treatment plan and the second treatment plan according to the current indicator value, the method further includes: The current indicator value is weighted according to the indicator weight corresponding to each evaluation indicator.

4. The method according to claim 3, characterized in that The method further comprises: Obtaining multiple indicator values ​​of each of the evaluation indicators for all the fault handling solutions within a target duration; According to the multiple indicator values, an entropy value corresponding to each evaluation indicator is obtained; the entropy value represents the fluctuation state of the indicator value; According to the entropy value, the indicator weight corresponding to each evaluation indicator is obtained.

5. The method according to claim 1 or 2, characterized in that: The step of selecting a target handling solution that meets a preferred condition from the plurality of fault handling solutions according to the first handling solution and the second handling solution includes: According to the current index value of the fault handling solution on each of the evaluation indicators, respectively obtain a first evaluation distance and a second evaluation distance corresponding to each of the fault handling solutions; the first evaluation distance is the distance between the fault handling solution and the first handling solution; the second evaluation distance is the distance between the fault handling solution and the second handling solution; According to the first evaluation distance and the second evaluation distance, a target handling solution that meets the optimization condition is screened out from the multiple fault handling solutions.

6. The method according to claim 5, characterized in that The step of selecting a target handling solution that meets a preferred condition from the plurality of fault handling solutions according to the first evaluation distance and the second evaluation distance includes: Obtaining a first relative closeness between each of the fault handling solutions and the first handling solution according to the first evaluation distance and the second evaluation distance; From the multiple fault handling solutions, a target handling solution whose first relative closeness satisfies a first optimal condition is selected.

7. The method according to claim 5, characterized in that The step of selecting a target handling solution that meets a preferred condition from the plurality of fault handling solutions according to the first evaluation distance and the second evaluation distance includes: Obtaining a second relative closeness between each of the fault handling solutions and a second handling solution according to the first evaluation distance and the second evaluation distance; From the multiple fault handling solutions, a target handling solution whose second relative closeness satisfies a second optimal condition is screened out.

8. The method according to claim 1 or 2, characterized in that: The obtaining of a first treatment plan and a second treatment plan according to the current indicator value comprises: Filter out the best index value and the worst index value corresponding to each evaluation index from the current index values ​​of all the fault handling solutions on each evaluation index; Constructing a first treatment plan based on the optimal indicator value corresponding to each of the evaluation indicators; Based on the worst indicator value corresponding to each of the evaluation indicators, a second disposal plan is constructed.

9. A device for selecting the best solution for container platform failure handling, characterized in that: The device comprises: An indicator obtaining unit, used to obtain current indicator values ​​of multiple fault handling solutions on multiple evaluation indicators respectively; the fault handling solutions are used to handle faults caused by the container platform implementing business services; the container platform is built based on multiple running nodes, and the running nodes implement corresponding business services through the running containers; A solution obtaining unit, configured to obtain a first treatment solution and a second treatment solution according to the current indicator value, wherein the indicator value of the first treatment solution on each evaluation indicator is the best among all the fault treatment solutions; and the indicator value of the second treatment solution on each evaluation indicator is the worst among all the fault treatment solutions; A solution screening unit is used to screen out a target solution that meets the optimization condition from the multiple fault handling solutions according to the first handling solution and the second handling solution.

10. An electronic device, characterized in that: include: A memory, used to store a computer program and data generated by the execution of the computer program; A processor, configured to execute the computer program to implement: Obtaining current index values ​​of multiple fault handling solutions on multiple evaluation indicators respectively; the fault handling solutions are used to handle faults caused by the container platform implementing business services; the container platform is built based on multiple running nodes, and the running nodes implement corresponding business services through the running containers; According to the current indicator value, a first treatment plan and a second treatment plan are obtained, wherein the indicator value of the first treatment plan on each evaluation indicator is the best among all the fault treatment plans; and the indicator value of the second treatment plan on each evaluation indicator is the worst among all the fault treatment plans; According to the first handling solution and the second handling solution, a target handling solution that meets the optimization condition is screened out from the multiple fault handling solutions.

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