Pressure testing method, device, equipment and medium

Abstract

The embodiment of the invention provides a pressure test method and device, equipment and a medium, and is used for solving the problem that a pressure test cannot be performed on a cluster operation environment in the prior art. The method comprises the following steps: respectively selecting a container category set associated with each load type from a plurality of container categories according to each load type associated with a target pressure scene; based on preset load configuration data of each load type, in combination with a non-container category set having a container calling relationship with the corresponding container category set, obtaining a number ratio of test containers associated with the container category set and the non-container category set, and based on the number ratio, determining the number of the test containers associated with the container category set and the non-container category set, between the test containers associated with the non-container category set and the container category set. Performing a corresponding pressure test to obtain target test parameters of various test indexes; and comparing the target test parameters corresponding to the various test indexes with corresponding test thresholds to obtain a pressure test result in the target pressure scene.

Description

Background Art

[0002] Moving to the cloud means deploying the data and application programs of individuals, enterprises, organizations, etc. on cloud servers. In real life, various business modules and microservices are gradually moving to the cloud. Therefore, it is crucial to ensure the stability of the microservices and business modules after moving to the cloud.

[0003] During the process of migrating business modules to the cloud, a series of K8S functions will be involved, such as: container orchestration, resource control, Virtual IP Cluster (VIPC), cloud data disk management, version update, etc. The implementation of this series of K8S functions makes the operating environment of the K8S system relatively complex, and it is easy to introduce many uncertain and uncontrollable factors, resulting in problems during the cloud migration process. Therefore, it is necessary to conduct a complete performance stress test on the K8S system before migrating business modules to the cloud to discover and solve potential problems in advance.

[0004] However, under the existing technology, performance stress test tools such as stress and GST only support stress testing of a single host or container. In actual applications, there are complex interactions between microservices in the K8S system, and the existing testing methods cannot perform stress testing on the interactions between microservices. Summary of the Invention

[0005] Embodiments of the present application provide a stress test method, device, equipment and medium, which are used to solve the problem that the existing technology cannot perform stress testing on the cluster operating environment.

[0006] In a first aspect, embodiments of the present application provide a stress test method, including:

[0007] According to each load type associated with the target pressure scenario, from multiple container categories, respectively select the set of container categories associated with each load type; where each container category is associated with at least one test container, and each test container is used to simulate a test environment for a pressure test task that conforms to the corresponding load type;

[0008] For each load type, respectively perform the following operations: based on the load configuration data preset for a corresponding load type, in combination with the set of non-container categories that have a container call relationship with the set of container categories, obtain the quantity ratio of the test containers associated with the set of container categories and the set of non-container categories, and based on the quantity ratio, perform corresponding stress testing between the test containers associated with the set of container categories and the set of non-container categories, and obtain the test sub-parameters of each type of test index;

[0009] For each type of test metric, perform the following operations respectively: Based on the test sub-parameters corresponding to each load type for a type of test metric, obtain the target test parameter of the type of test metric;

[0010] Compare the target test parameters corresponding to each type of test metric with the corresponding test thresholds respectively to obtain the stress test result under the target stress scenario.

[0011] In a second aspect, an embodiment of the present application provides a stress test device, including:

[0012] A selection unit, configured to select, according to each load type associated with the target stress scenario, a set of container categories associated with each load type respectively from multiple container categories; wherein, each container category is associated with at least one test container, and each test container is used to simulate a test environment for a stress test task conforming to the corresponding load type;

[0013] A first processing unit, configured to perform the following operations respectively for each load type: Based on the load configuration data preset for one load type, combine with a set of non-container categories having a container call relationship with the set of container categories, obtain the quantity ratio of the test containers associated with the set of container categories and the set of non-container categories respectively, and based on the quantity ratio, perform corresponding stress tests between the test containers associated with the set of container categories and the set of non-container categories to obtain the test sub-parameters of each type of test metric;

[0014] A second processing unit, configured to perform the following operations respectively for each type of test metric: Based on the test sub-parameters corresponding to each load type for a type of test metric, obtain the target test parameter of the type of test metric;

[0015] A determination unit, configured to compare the target test parameters corresponding to each type of test metric with the corresponding test thresholds respectively to obtain the stress test result under the target stress scenario.

[0016] In a possible implementation manner, the determination unit is further configured to: determine a target number of computing resource pools included in the test system where the test containers are located, and a set of set numbers of service nodes included in each computing resource pool; wherein, each test container is deployed on any service node in the set of service nodes;

[0017] When obtaining the quantity ratio of the test containers associated with the set of container categories and the set of non-container categories respectively, the first processing unit is specifically configured to:

[0018] For each computing resource pool, perform the following operations: Based on the load configuration data preset according to the load type, and in combination with the non-container category set having a container call relationship with the container category set, determine the quantity ratio of the test containers associated with the container category set and the non-container category set respectively in the set number of service node sets included in a computing resource pool.

[0019] In a possible implementation manner, the target test parameters corresponding to the various test metrics are detected in real time; then the determining unit is further configured to:

[0020] During the stress test, in response to the operation of stopping the running triggered for the target computing resource pool among the target quantity of computing resource pools, abort the running of at least one test container included in the target computing resource pool;

[0021] The second processing unit is further configured to respectively obtain the target test parameters of the various test metrics before and after the target computing resource pool stops running;

[0022] The determining unit is further configured to determine the disaster tolerance capability of the test system according to the change conditions of the target test parameters of the various test metrics before and after the target computing resource pool stops running.

[0023] In a possible implementation manner, the target test parameters corresponding to the various test metrics are detected in real time; then the determining unit is further configured to:

[0024] During the stress test, in response to the operation of stopping the running triggered for the target service node among the set number of service node sets, abort the running of at least one test container included in the target service node;

[0025] The second processing unit is further configured to respectively obtain the target test parameters of the various test metrics before and after the target service node stops running;

[0026] The determining unit is further configured to determine the fault masking capability of the test system according to the change conditions of the target test parameters of the various test metrics before and after the target service node stops running.

[0027] In a possible implementation manner, when the selecting unit respectively selects the container category sets associated with the respective load types from multiple container categories according to the load types associated with the target pressure scenario, it is specifically configured to:

[0028] When the load type is the processor CPU load type, determine that the container category set associated with the CPU load type includes: logical layer test containers;

[0029] When the load type is a memory load type, determining that the set of container categories associated with the memory load type includes: a logic layer test container;

[0030] When the load type is a network load type, determining that the set of container categories associated with the network load type includes: a logic layer test container and a Common Gateway Interface (CGI) layer test container;

[0031] When the load type is a disk load type, determining that the set of container categories associated with the disk load type includes: a storage layer test container.

[0032] In a possible implementation, when the first processing unit obtains the quantity ratio of the test containers respectively associated with the set of container categories and the non-container category set based on the load configuration data preset for a corresponding load type and in combination with the non-container category set having a container call relationship with the set of container categories, it is specifically configured to:

[0033] When the load type is a CPU load type, determining that the non-container category set having a container call relationship with the logic layer test container includes: a CGI layer test container, and determining the quantity ratio between the logic layer test container and the CGI layer test container;

[0034] When the load type is a memory load type, determining that the non-container category set having a container call relationship with the logic layer test container includes: a CGI layer test container, and determining the quantity ratio between the logic layer test container and the CGI layer test container;

[0035] When the load type is a network load type, determining that the non-container category set is an empty set according to the container call relationship, and determining the quantity ratio between the logic layer test container and the CGI layer test container;

[0036] When the load type is a network disk load type, determining that the non-container category set having a container call relationship with the storage layer test container includes: a CGI layer test container and a logic layer test container, and determining the quantity ratio among the CGI layer test container, the logic layer test container, and the storage layer test container.

[0037] In a third aspect, an embodiment of the present application provides an electronic device, including: a memory and a processor;

[0038] The memory is used to store computer instructions;

[0039] The processor is used to obtain the computer instructions stored in the memory and implement the steps of the stress test method provided by the embodiments of the present application according to the computer instructions.

[0040] Fourthly, an embodiment of the present application provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the steps of the stress test method provided by the embodiments of the present application.

[0041] Fifthly, an embodiment of the present application provides a computer program product including computer instructions stored in a computer-readable storage medium. When a processor of an electronic device reads the computer instructions from the computer-readable storage medium, the processor executes the computer instructions, causing the electronic device to execute the steps of the stress test method provided by the embodiments of the present application.

[0042] The beneficial effects of the present application are as follows:

[0043] An embodiment of the present application provides a stress test method, device, equipment, and storage medium. In the embodiment of the present application, when performing a stress test under a target stress scenario, first determine the container categories associated with each load type, further determine the quantity ratio between the container categories and the test containers included in the non-container categories, and then perform a stress test among the test containers with the determined quantity ratio. In the present application, adjustable stress tests can be performed from the dimension of multiple load types, thereby simulating the business performance under various load scenarios. In addition, the test containers are used to simulate a test environment for stress test tasks conforming to the corresponding load types. By performing stress tests among different types of test containers, the load calls between real services can be simulated. Additionally, the present application can also test the disaster tolerance ability and fault shielding ability of the test system by stopping the operation of the target computing resource pool or the target business node, so as to verify the stability and reliability of the test system.

[0044] Other features and advantages of the present application will be described in the subsequent specification, and, in part, will be obvious from the specification, or will be understood by implementing the present application. The objectives and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written specification, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] To more clearly illustrate the technical solutions in the embodiments of the present application, the following briefly introduces the drawings required for the description of the embodiments. Obviously, the following drawings are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0046] Figure 1 It is a schematic diagram of an application scenario provided by an embodiment of the present application;

[0047] Figure 2Flowchart of a pressure testing method provided by an embodiment of the present application;

[0048] Figure 3 Schematic diagram of container categories associated with a load type provided by an embodiment of the present application;

[0049] Figure 4 Schematic diagram of calls between different types of test containers provided by an embodiment of the present application;

[0050] Figure 5 Schematic diagram of non-container categories associated with a load type provided by an embodiment of the present application;

[0051] Figure 6 Schematic diagram of a deployment plan provided by an embodiment of the present application;

[0052] Figure 7 Schematic diagram of a multi-campus deployment plan provided by an embodiment of the present application;

[0053] Figure 8 Schematic diagram of the deployment of test containers in a multi-campus provided by an embodiment of the present application;

[0054] Figure 9 Another schematic diagram of the deployment of test containers in a multi-campus provided by an embodiment of the present application;

[0055] Figure 10 Schematic diagram of the ratio of different test containers in a pressure scenario provided by an embodiment of the present application;

[0056] Figure 11 Schematic diagram of performing a pressure test provided by an embodiment of the present application;

[0057] Figure 12 Schematic diagram of a pressure testing device provided by an embodiment of the present application;

[0058] Figure 13 Schematic diagram of an electronic device provided by an embodiment of the present application. Detailed implementation manners

[0059] In order to make the objectives, technical solutions and beneficial effects of the present application clearer and more understandable, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0060] To facilitate better understanding of the technical solutions of the present application by those skilled in the art, some concepts involved in the present application will be introduced below.

[0061] K8S: The full name of K8S is Kubernetes, which is an abbreviation formed by replacing 8 characters "ubernete" with 8. It is an open-source tool used to manage containerized applications on multiple hosts in a cloud platform. The goal of Kubernetes is to make it simple and efficient to deploy containerized applications. Kubernetes provides a mechanism for application deployment, planning, updating, and maintenance.

[0062] A Pod is the smallest unit in K8S. The IP address of a Pod is random, and deleting a Pod will change the IP. Each Pod has a root container. A Pod can consist of one or more containers. Containers within a Pod share the network namespace of the root container, and the network address within a Pod is provided by the root container.

[0063] A cluster is a computer system that is connected by a group of loosely integrated computer software and / or hardware to collaborate highly closely to complete computing tasks. Individual computers in a cluster computer system are usually called nodes, and the nodes are usually connected via a local area network, but there are other possible connection methods. Cluster computer systems are usually used to improve the computing speed and / or reliability of a single computer.

[0064] Cloud technology refers to a hosting technology that unifies a series of resources such as hardware, software, and networks within a wide area network or a local area network to achieve data computing, storage, processing, and sharing.

[0065] Cloud technology is the general term for network technology, information technology, integration technology, management platform technology, application technology, etc. based on the cloud computing business model. It can form a resource pool, be used on demand, and is flexible and convenient. Cloud computing technology will become an important support. The back-end services of technical network systems require a large amount of computing and storage resources, such as video websites, picture-based websites, and more portal websites. With the high development and application of the Internet industry, in the future, each item may have its own identification mark and needs to be transmitted to the back-end system for logical processing. Data at different levels will be processed separately, and various industry data requires a powerful system back-end support, which can only be achieved through cloud computing.

[0066] QPS: Queries Per Second, is a performance metric used to describe the number of queries or requests that a service can process per second.

[0067] Core: Core Dump, refers to the situation where a program crashes due to certain errors, which usually generates a coredump (core dump).

[0068] OOM: Out Of Memory, which refers to the phenomenon that a program is forcibly killed by the operating system because the memory occupancy exceeds the operating system limit. Common causes include program memory leaks or insufficient memory in the running environment.

[0069] Probe timeout: A probe is an inspection mechanism used to perform health probes on the running status of a service. If the service does not give a response within a predetermined time range, it is considered a probe timeout. This usually means that the service is unavailable due to failures, full loads, or other factors.

[0070] Stress scenario: It refers to a simulated stress test scenario obtained after determining the type of resource stress and the proportion of link modules used. Such as CPU sensitivity stress scenario, disk sensitivity stress scenario, etc.

[0071] Deployment plan: It refers to a planning plan for deploying stress test modules in a K8S cluster or within a cluster park. There are different numbers of K8S clusters and involved parks among different deployment plans, and the resource quotas allocated to each Pod are also different.

[0072] As used hereinafter, the term "exemplary" means "serving as an example, embodiment, or illustration". Any embodiment illustrated as "exemplary" does not have to be construed as superior or better than other embodiments.

[0073] The terms "first" and "second" in the text are only used for descriptive purposes and cannot be construed as explicitly or implicitly indicating relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0074] The following briefly introduces the design concept of the embodiments of the present application:

[0075] During the process of migrating business modules to the cloud, a series of K8S functions will be involved, such as: container orchestration, resource control, Virtual IP Cluster (VIPC), cloud data disk management, version update, etc. The implementation of this series of K8S functions makes the running environment of the K8S system relatively complex and prone to introducing many uncertain and uncontrollable factors, thus causing problems during the cloud migration process. Therefore, it is necessary to conduct a complete performance stress test on the K8S system before migrating business modules to the cloud to discover and solve potential problems in advance.

[0076] Under the existing technology, performance stress testing tools such as stress and GST only support stress testing on a single host or container. In practical applications, there are complex interactions among microservices in the K8S system, and the existing testing methods cannot perform stress testing on the interactions among microservices.

[0077] In view of the above problems, the embodiments of the present application provide a stress testing method, device, equipment and medium. By using different types of test containers, a test environment simulating stress testing tasks conforming to different load types is simulated. Under different stress scenarios, through stress testing of different load types, and then stress testing is carried out among different types of test containers, simulating the complex calls among real services to determine the stress test results of the test system.

[0078] The following describes the preferred embodiments of the present application in conjunction with the accompanying drawings of the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. And without conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0079] Reference Figure 1 , Figure 1 is a schematic diagram of the application scenario of the embodiments of the present application. This application scenario includes a client 100 and an execution device 200. Among them, in some scenarios, the execution device can be a server, and the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0080] In some scenarios, the client 100 transmits a stress testing task to the execution device 200. After receiving the stress testing task, the execution device 200 can execute the stress testing method. In some scenarios, after executing the stress testing method, the execution device 200 can send the stress test result to the client 100.

[0081] Figure 1 The above is only an example, and actually the number of execution devices is not limited and is not specifically limited in the embodiments of the present application.

[0082] Based on the above application scenario, the stress testing method provided by the exemplary embodiment of the present application will be described below in conjunction with the above-described application scenario according to the drawings. It should be noted that the above application scenario is only shown for the convenience of understanding the spirit and principle of the present application, and the embodiments of the present application are not limited in this regard.

[0083] Reference Figure 2 as shown Figure 2 An exemplary flowchart of a pressure test method in an embodiment of the present application is provided. The method includes the following steps:

[0084] S201, according to each load type associated with the target pressure scenario, select the set of container categories associated with each load type respectively from multiple container categories.

[0085] In some scenarios, the load types include the processor CPU, memory, disk, latency, and network. Different load types are associated with different container categories.

[0086] Among them, each container category is associated with at least one test container, and each test container is used to simulate a test environment for a pressure test task that conforms to the corresponding load type. In some scenarios, the test container can also be implemented through a module.

[0087] As an example, in the embodiment of the present application, there are a total of 3 different container categories, such as category A, category B, and category C.

[0088] Among them, the category A test container is used to simulate the CGI layer, mainly for subcontracting, constructing network load requests, etc. The category B test container is used to simulate the logic layer, mainly for simulating aspects such as CPU, memory, and latency. The category C test container is used to simulate the storage layer, uses local kv storage, and mainly simulates disk load.

[0089] In some embodiments, different container categories are associated with at least one test container. As an example, the category A test container includes multiple test containers such as A1, A2, A3, etc. Different category A test containers are used to simulate different types of Common Gateway Interface (CGI) layer services. The category B test container includes multiple test containers such as B1, B2, B3, etc. Different category B test containers are used to simulate different types of logic layer services. The category C test container includes multiple test containers such as C1, C2, C3, etc. Different category C test containers are used to simulate different types of storage layer services.

[0090] In a possible implementation, according to each load type associated with the target pressure scenario, from multiple container categories, the container category sets respectively associated with each load type are selected. Specifically, it can be implemented in the following way: When the load type is the processor CPU load type, it is determined that the container category set associated with the CPU load type includes: logical layer test containers; when the load type is the memory load type, it is determined that the container category set associated with the memory load type includes: logical layer test containers; when the load type is the network load type, it is determined that the container category set associated with the network load type includes: logical layer test containers and CGI layer test containers; when the load type is the disk load type, it is determined that the container category set associated with the disk load type includes: storage layer test containers, such as Figure 3 shown.

[0091] S202. For each load type, the following operations are respectively performed: Based on the load configuration data preset for a corresponding load type, in combination with the non-container category set that has a container call relationship with the container category set, the quantity ratio of the test containers respectively associated with the container category set and the non-container category set is obtained, and based on the quantity ratio, corresponding pressure tests are performed between the test containers associated with the container category set and the non-container category set to obtain the test sub-parameters of each type of test metric.

[0092] In some scenarios, there is a call relationship between each type of test container. Continuing with the above example, the call order among three types of test containers, namely type A, type B, and type C, is A→B→C. Among them, each type of test container can call any test container in the downstream test container type. As an example, A1 can call any one of the B-type test containers such as B1, B2, and B3. This call relationship can simulate the call relationship between complex services in the live network environment.

[0093] Such as Figure 4 shown, the type A test container is used to represent CGI layer services, the type B test container is used to represent logical layer services, and the type C test container is used to represent storage layer services, and their call relationship is A→B→C. As an example, the A1 test container can call the B1 test container, the A2 test container can call the B2 test container or the B3 test container, the A3 test container can call the B2 test container and the B3 test container, and the A4 test container can call the B4 test container. The B1 test container can call the C1 test container, the B2 test container can call the C1 test container and the C2 test container, the B3 test container can call the C3 test container and the C4 test container, and the B4 test container can call the C4 test container. Of course, there can be other call relationships between different test containers, and the present application does not make specific limitations on this.

[0094] In some embodiments, based on the load configuration data preset for a corresponding load type, in combination with the non-container category set that has a container call relationship with the container category set, the quantity ratio of the test containers associated with the container category set and the non-container category set is obtained. Specifically, it can be implemented in the following manner:

[0095] When the load type is the CPU load type, it is determined that the non-container category set having a container call relationship with the logic layer test container includes: the CGI layer test container, and the quantity ratio between the logic layer test container and the CGI layer test container is determined;

[0096] When the load type is the memory load type, it is determined that the non-container category set having a container call relationship with the logic layer test container includes: the CGI layer test container, and the quantity ratio between the logic layer test container and the CGI layer test container is determined;

[0097] When the load type is the network load type, it is determined that the non-container category set is an empty set according to the container call relationship, and the quantity ratio between the logic layer test container and the CGI layer test container is determined;

[0098] When the load type is the network disk load type, it is determined that the non-container category set having a container call relationship with the storage layer test container includes: the CGI layer test container and the logic layer test container, and the quantity ratio among the CGI layer test container, the logic layer test container, and the storage layer test container is determined.

[0099] As an example, the relationship between the container category set and the non-container category set associated with each load type is as Figure 5 shown.

[0100] As an example, if the load type is CPU and the load configuration data of the CPU is 90%, it can be determined that the container category set associated with the CPU includes the logic layer test container. According to the call relationship, it is determined that the non-container category set includes the CGI layer test container. Then, it is further determined that the quantity ratio between the CGI layer test container and the logic layer test container is 1:19.

[0101] In some embodiments, after determining the quantity ratio between the test containers associated with the container category set and the non-container category set for each load type, based on the quantity ratio, corresponding stress tests can be performed between the test containers associated with the container category set and the non-container category set to obtain the test sub-parameters of each type of test metric.

[0102] In some embodiments, the load types include five load types: CPU, memory, disk, latency, and network. After determining each load type in the target stress scenario, stress tests can be performed for each load type. For example, when it is determined that the load types in the target stress scenario include CPU and network, a first quantity ratio between the CGI layer test containers and the logic layer test containers is determined according to the CPU load, and a second quantity ratio between the CGI layer test containers and the logic layer test containers is determined according to the network load. Then, a stress test for the CPU load type is performed between the CGI layer test containers and the logic layer test containers with the first quantity ratio, and a stress test for the network load type is performed between the CGI layer test containers and the logic layer test containers with the second quantity ratio, so as to obtain test sub-parameters corresponding to each test metric under each load type.

[0103] S203. For each type of test metric, perform the following operations respectively: Based on the test sub-parameters corresponding to each load type for a type of test metric, obtain the target test parameter for a type of test metric.

[0104] Continuing with the above example, under the network load type, there are test sub-parameters corresponding to each test metric, and under the CPU load type, there are also test sub-parameters corresponding to each test metric. Further, for each type of test metric, the target test parameter for this type of test metric can be obtained according to the test sub-parameters of this type of test metric under the network load and the CPU load respectively.

[0105] In a possible implementation, for each type of test metric, the test sub-parameters corresponding to each load type for this type of test metric can be weighted to obtain the target test parameter for this type of test metric.

[0106] S204. Compare the target test parameters corresponding to each type of test metric with the corresponding test thresholds respectively to obtain the stress test result in the target stress scenario.

[0107] In some embodiments, each type of test metric corresponds to a corresponding test threshold. By comparing with the test threshold, the stress test result in the target stress scenario can be determined.

[0108] In a possible implementation, when performing a stress test for the corresponding load type, it can be implemented in the following manner:

[0109] CPU load: Calculate the SHA value (hash value) of the password hash function. Considering calculating the SHA value of 10KB of data once as a load task.

[0110] Memory load: A memory block of a given size, and random data is written every 4KB to ensure that the memory is truly allocated. The memory allocation will be the first load in the request processing function, and the memory block will not be freed until the request processing function finishes execution. Therefore, the memory occupancy will last throughout the request processing flow, which can cover the "CPU load", "latency load", and the process of sending disk load requests to Class C test containers.

[0111] Disk load: Write several 1KB key-value pairs to the local key-value (kv) storage.

[0112] Latency load: The time given in the sleep function.

[0113] Network request / response load: Fill a given length of string in the request / response packet.

[0114] In some embodiments, different types of test containers may have different deployment schemes. There are different numbers of K8S clusters and the number of involved campuses among different deployment schemes, and the resource quotas allocated to each POD are also different. In this application, a campus can be understood as a computing resource pool or a computer room, and a cluster can be understood as a set of business nodes.

[0115] In some embodiments, it is possible to determine the target number of computing resource pools included in the test system where the test container is located, and the set number of business node sets included in each computing resource pool. Among them, each test container is deployed on any business node in the business node set. Then, obtaining the quantity ratio of the test containers associated with the container category set and the non-container category set respectively can be specifically achieved through the following method:

[0116] For each computing resource pool, perform the following operations: Based on the load configuration data preset according to the load type, combined with the non-container category set that has a container call relationship with the container category set, determine the quantity ratio of the test containers associated with the container category set and the non-container category set respectively in the set number of business node sets included in a computing resource pool.

[0117] As an example, as Figure 6 shown, there are 4 deployment schemes provided by this application. Each deployment scheme has different focuses. Different deployment schemes can be combined with different stress scenarios to fully implement the stress test on the K8S system.

[0118] For example, adopt the six-cluster three-campus 8CPU deployment scheme. This scheme focuses on testing the load capacity, scheduling capacity, and disaster tolerance capacity of multiple campuses and multiple clusters. In this scheme, the test containers adopt a disaster tolerance scheme for 3 campuses, and the 3 pods of each test container are located in the clusters of 3 different campuses.

[0119] As Figure 7 shown, there are three parks in total, including Park 1, Park 2 and Park 3. Among them, Park 1 includes Cluster 1 and Cluster 2, Park 2 includes Cluster 3 and Cluster 4, and Park 3 includes Cluster 5 and Cluster 6. In some scenarios, three A1 test containers are respectively located in Park 1, Park 2 and Park 3. The A1 test container in Park 1 can be located in any one of Cluster 1 and Cluster 2, and this application does not make specific limitations in this regard.

[0120] Exemplarily, assume that the quantity ratio of the A-type test containers to the B-type test containers is 1:1. If the A-type test containers are A1 - A3, then the B-type test containers are B1 - B3. If there are three parks, then each of the three parks includes A1 - A3 test containers and B1 - B3 test containers. In one possible implementation, the A1 - A3 test containers are all in one cluster, and the B1 - B3 test containers are in another test container, as Figure 8 shown. In another possible implementation, each of the A1 - A3 test containers can be in any one of Cluster 1 and Cluster 2 in Park 1, and each of the B1 - B3 test containers can be in any one of Cluster 1 and Cluster 2 in the park, as Figure 9 shown. Among them, in Park 2 and Park 3, the quantity of the A-type test containers and the B-type test containers is the same as that in Park 1, but the clusters where the respective test containers are located can be the same or different. This application does not make specific limitations in this regard.

[0121] In some embodiments, the target test parameters corresponding to various test metrics are detected in real time. During the stress test, in response to the operation of stopping the running of the target computing resource pool triggered for the target computing resource pool in the target quantity of computing resource pools, the running of at least one test container included in the target computing resource pool is aborted. Further, the target test parameters of various test metrics before and after the target computing resource pool stops running can be obtained respectively. Furthermore, the disaster tolerance ability of the test system can be determined according to the change situation of the target test parameters of various test metrics before and after the target computing resource pool stops running.

[0122] As an example, Park 1 in Parks 1 - 3 can be stopped from running to simulate a park failure, so as to determine the park disaster tolerance ability of the test system.

[0123] In some embodiments, the target test parameters corresponding to various test metrics are detected in real time. During the stress test, in response to an operation to stop the running of a target service node triggered for a set number of business nodes, the running of at least one test container included in the target service node is aborted. Further, the target test parameters of various test metrics before and after the target service node stops running can be obtained respectively. Furthermore, the fault shielding ability of the test system can be determined based on the changes in the target test parameters of various test metrics before and after the target service node stops running.

[0124] In some embodiments, it is also possible to simulate faults in K8S control plane components to test the impact scope of the K8S system on deployed services and newly added services. In some scenarios, it is possible to simulate single-module or multi-module changes to business containers, and then test the impact of business changes on existing traffic. In addition, it is possible to test the impact scope on deployed services and newly added services by changing K8S components in a single cluster / multi-cluster.

[0125] In the embodiments of the present application, the stress test results of the test system are determined through multi-dimensional test metrics. The test metrics include performance metrics (requests per second QPS), quality metrics (call failure rate and call duration), stability metrics (module health status), and resource consumption metrics (resource occupancy statistics). The statistical methods corresponding to the above test metrics are as follows:

[0126] Performance metric (QPS): Count the number of requests on the supply side within one second and observe the correlation between its changes and operations. By observing the RPC call column on the monitoring page, a QPS curve at the minute granularity can be obtained.

[0127] Quality metric (call failure rate): During the stress test, the tool reports the number of call errors at the second granularity and observes the correlation between its changes and operations. At the end of the stress test, the final stress test results reported by the tool include QPS, call duration statistics, error codes, and the number of errors. Observe the call failure rate at the minute granularity on the monitoring page. There should be no call failures when the hardware resources are not overloaded.

[0128] Quality metric (call duration): During the stress test, the tool reports QPS at the second granularity and observes the correlation between its changes and operations. When the concurrency is constant, the corresponding call duration can be deduced from QPS. At the end of the stress test, the final stress test results reported by the tool include call duration statistics, and the specific statistical items are as follows: Average duration: The average duration regardless of whether the request is successful or not. p50: The median, which is ensured to be accurate through sorting. p99: The duration at the 99% percentile, which is ensured to be accurate through sorting. Observe the call duration at the minute granularity on the monitoring page.

[0129] Stability indicators (module health status): During the stress test, whether there are any abnormal phenomena in each module, each pod, etc. Statistical items include: Core: The business process crashes. Observed through the monitoring page. OOM: The business container has an OutOfMemory, using more memory than the limit, resulting in it being restarted by K8S. Observe the restart times and events of the container. Pod eviction: When node resources such as memory and disk reach the threshold, causing the Pod to be evicted from the current node, observe the Pod restart time. Probe script timeout: Observe whether there will be a situation during the stress test where the business container is killed due to probe detection failure. Observe the restart times and events of the container.

[0130] Resource consumption indicators (resource occupancy statistics): CPU and memory usage rates: Obtained by polling the K8S APIServer through the kubectl command. Disk and network usage rates: Obtained by observing the monitoring pages of nodes and pods.

[0131] In some embodiments, through the above series of observation indicators, the performance of the entire K8S system during the stress test can be observed from all aspects and multiple angles, which helps to timely discover problems existing in the system during the stress test.

[0132] In the embodiments of the present application, different types of test containers play different roles, and there will be different deployment ratios and pressure ratios of test containers. In the present application, by adjusting the different pressures received by the 3 types of test containers and the quantity ratio between the test containers, different scenarios in the real enterprise WeChat background are simulated. As Figure 10 shown, Figure 10 is the pressure scenario used in the present application. Under different pressure scenarios, the quantity ratios between the respective test containers are different.

[0133] Before actual stress application, a certain amount of normal load will be held in the stress test link. The normal load occupies approximately 40% of the resources, which is used to simulate the normal operation of the business module and provides a reference value for comparing with the business performance after stress application.

[0134] In some embodiments, a stress test can be initiated through a stress application tool. Through the command-line interface of the stress test, initiate the stress test and view the test results in real time. In some embodiments, after the stress test tool initiates the stress test, Class A, Class B, and Class C containers can perform corresponding tests according to the stress test initiated by the stress application tool, and then determine the test results, as Figure 11 shown.

[0135] Based on the same technical concept, as shown in Figure 12 shown, Figure 12Exemplarily shown is a pressure testing device 1200 provided by an embodiment of the present application. The device 1200 includes:

[0136] A selection unit 1201, configured to respectively select a set of container categories associated with each load type from multiple container categories according to each load type associated with a target pressure scenario; wherein, each container category is associated with at least one test container, and each test container is used to simulate a test environment for a pressure testing task conforming to the corresponding load type.

[0137] A first processing unit 1202, configured to respectively perform the following operations for each load type: Based on the load configuration data preset for one load type, in combination with a set of non-container categories having a container call relationship with the set of container categories, obtain the quantity ratio of the test containers associated with the set of container categories and the set of non-container categories, and based on the quantity ratio, perform corresponding pressure testing between the test containers associated with the set of container categories and the set of non-container categories, and obtain test sub-parameters of each type of test metric.

[0138] A second processing unit 1203, configured to respectively perform the following operations for each type of test metric: Based on the test sub-parameters corresponding to each load type of one type of test metric, obtain the target test parameter of one type of test metric.

[0139] A determination unit 1204, configured to respectively compare the target test parameters corresponding to each type of test metric with the corresponding test thresholds, and obtain the pressure testing result in the target pressure scenario.

[0140] In a possible implementation manner, the determination unit 1204 is further configured to: determine a target number of computing resource pools included in the test system where the test containers are located, and a set number of business node sets included in each computing resource pool; wherein, each test container is deployed on any business node in the business node set.

[0141] When obtaining the quantity ratio of the test containers associated with the set of container categories and the set of non-container categories, the first processing unit 1202 is specifically configured to:

[0142] For each computing resource pool, perform the following operations: Based on the load configuration data preset for the load type, in combination with the set of non-container categories having a container call relationship with the set of container categories, determine the quantity ratio of the test containers associated with the set of container categories and the set of non-container categories in the set number of business node sets included in one computing resource pool.

[0143] In a possible implementation manner, the target test parameters corresponding to each type of test metric are detected in real time; then the determination unit 1204 is further configured to:

[0144] During the stress test, in response to a run stop operation triggered for a target computing resource pool among a target number of computing resource pools, the operation of at least one test container included in the target computing resource pool is aborted;

[0145] The second processing unit 1203 is further configured to respectively obtain target test parameters of various test metrics before and after the target computing resource pool stops running;

[0146] The determination unit 1204 is further configured to determine the disaster tolerance ability of the test system according to the change conditions of the target test parameters of various test metrics before and after the target computing resource pool stops running.

[0147] In a possible implementation manner, the target test parameters corresponding to various test metrics are detected in real time; then the determination unit 1204 is further configured to:

[0148] During the stress test, in response to an operation of stopping the running of a target service node triggered for a target number of service nodes in a service node set, the operation of at least one test container included in the target service node is aborted;

[0149] The second processing unit 1203 is further configured to respectively obtain target test parameters of various test metrics before and after the target service node stops running;

[0150] The determination unit 1204 is further configured to determine the fault masking ability of the test system according to the change conditions of the target test parameters of various test metrics before and after the target service node stops running.

[0151] In a possible implementation manner, when the selection unit 1201 respectively selects a set of container categories associated with each load type from multiple container categories according to each load type associated with a target pressure scenario, it is specifically configured to:

[0152] When the load type is a processor CPU load type, it is determined that the set of container categories associated with the CPU load type includes: logical layer test containers;

[0153] When the load type is a memory load type, it is determined that the set of container categories associated with the memory load type includes: logical layer test containers;

[0154] When the load type is a network load type, it is determined that the set of container categories associated with the network load type includes: logical layer test containers and common gateway interface CGI layer test containers;

[0155] When the load type is a disk load type, it is determined that the set of container categories associated with the disk load type includes: storage layer test containers.

[0156] In a possible implementation, when the first processing unit 1202 obtains the quantity ratio of the test containers associated with the container category set and the non-container category set respectively, based on the load configuration data preset for a corresponding load type and in combination with the non-container category set having a container call relationship with the container category set, it is specifically configured to:

[0157] When the load type is the CPU load type, determine that the non-container category set having a container call relationship with the logic layer test container includes: the CGI layer test container, and determine the quantity ratio between the logic layer test container and the CGI layer test container;

[0158] When the load type is the memory load type, determine that the non-container category set having a container call relationship with the logic layer test container includes: the CGI layer test container, and determine the quantity ratio between the logic layer test container and the CGI layer test container;

[0159] When the load type is the network load type, determine that the non-container category set is an empty set according to the container call relationship, and determine the quantity ratio between the logic layer test container and the CGI layer test container;

[0160] When the load type is the network disk load type, determine that the non-container category set having a container call relationship with the storage layer test container includes: the CGI layer test container and the logic layer test container, and determine the quantity ratio among the CGI layer test container, the logic layer test container and the storage layer test container.

[0161] For the convenience of description, the above parts are divided into respective units (or modules) according to functions and described separately. Of course, when implementing the present application, the functions of the respective units (or modules) can be implemented in the same or multiple software or hardware.

[0162] Those skilled in the art to which the present application pertains can understand that various aspects of the present application can be implemented as a system, a method, or a program product. Therefore, various aspects of the present application can be specifically implemented in the following forms, namely: a complete hardware implementation manner, a complete software implementation manner (including firmware, microcode, etc.), or an implementation manner combining hardware and software aspects, which can be collectively referred to as "circuitry", "module", or "system" here.

[0163] After introducing the stress test method and device of the exemplary implementation manner of the present application, next, an electronic device for stress testing according to another exemplary implementation manner of the present application is introduced.

[0164] Based on the same inventive concept as the above method embodiment of the present application, an electronic device is further provided in the embodiment of the present application, and this electronic device can be a server. In this embodiment, the structure of the electronic device can be as Figure 13 shown, including a memory 1301 and one or more processors 1302.

[0165] A memory 1301 for storing a computer program executed by a processor 1302. The memory 1301 may mainly include a program storage area and a data storage area. Among them, the program storage area may store an operating system and programs required to run an instant messaging function, etc.; the data storage area may store various instant messaging information and operation instruction sets, etc.

[0166] The memory 1301 may be a volatile memory, such as a random-access memory (RAM); the memory 1301 may also be a non-volatile memory, such as a read-only memory, a flash memory, a hard disk drive (HDD), or a solid-state drive (SSD); or the memory 1301 is any other medium that can be used to carry or store a desired computer program in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory 1301 may be a combination of the above memories.

[0167] The processor 1302 may include one or more central processing units (CPUs) or be a digital processing unit, etc. The processor 1302 is used to implement the above stress test method when calling the computer program stored in the memory 1301.

[0168] In the embodiments of the present application, the specific connection medium between the above-mentioned memory 1301 and the processor 1302 is not limited. In the embodiments of the present application Figure 13 it is described that the memory 1301 and the processor 1302 are connected through a bus 1303. The bus 1303 is described in thick lines in Figure 13 The connection manners between other components are only for illustrative purposes and are not to be considered limiting. The bus 1303 may be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of description, Figure 13 only one thick line is used to describe it in

[0169] A computer storage medium is stored in the memory 1301. Computer-executable instructions are stored in the computer storage medium. The computer-executable instructions are used to implement the stress test method of the embodiments of the present application. The processor 1302 is used to execute the above stress test method.

[0170] In some possible embodiments, various aspects of the stress testing method provided in this application can also be implemented in the form of a program product, which includes a computer program. When the program product runs on an electronic device, the computer program is used to cause the electronic device to execute the steps in the authentication method according to various exemplary embodiments of this application described above in this specification.

[0171] The program product can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0172] The program product of the embodiments of this application can adopt a portable compact disc read-only memory (CD-ROM) and include a computer program, and can run on a computing device. However, the program product of this application is not limited to this. In this document, the readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with a command execution system, apparatus, or device.

[0173] The readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, which carries a readable computer program. Such a propagated data signal can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with a command execution system, apparatus, or device.

[0174] The computer program included on the readable medium can be transmitted by any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination of the above.

[0175] It should be noted that although several units or subunits of the device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of this application, the features and functions of the two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0176] In addition, although the operations of the method of the present application are described in a specific order in the drawings, this does not require or imply that these operations must be performed in that specific order, or that all of the shown operations must be performed to achieve the desired result. Additionally or alternatively, some steps may be omitted, multiple steps may be combined into one step for execution, and / or one step may be decomposed into multiple steps for execution.

[0177] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code.

[0178] The present application is described with reference to the flowcharts and / or block diagrams of methods, apparatuses (systems), and computer program products according to the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and the combination of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0179] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means that implement the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0181] Obviously, those skilled in the art can make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalent technologies, this application is also intended to cover these changes and modifications.

Claims

1. A pressure test method, characterized in that, Including: According to each load type associated with the target pressure scenario, respectively select the set of container categories associated with each load type from multiple container categories; wherein, each container category is associated with at least one test container, and each test container is used to simulate a test environment for a pressure test task conforming to the corresponding load type. For each load type, respectively perform the following operations: Based on the load configuration data preset for a load type, in combination with the set of non-container categories having a container call relationship with the set of container categories, obtain the quantity ratio of the test containers associated with the set of container categories and the set of non-container categories respectively, and based on the quantity ratio, perform corresponding pressure tests between the test containers associated with the set of container categories and the set of non-container categories to obtain the test sub-parameters of each type of test metric. For each type of test metric, respectively perform the following operations: Based on the test sub-parameters corresponding to each load type for a type of test metric, obtain the target test parameter of the type of test metric. Compare the target test parameters corresponding to each type of test metric with the corresponding test thresholds respectively to obtain the pressure test result in the target pressure scenario.

2. The method according to claim 1, characterized in that, The method further includes: Determine the target number of computing resource pools included in the test system where the test containers are located, and the set of a set number of service nodes included in each computing resource pool; wherein, each test container is deployed on any one of the service nodes in the set of service nodes. The obtaining of the quantity ratio of the test containers associated with the set of container categories and the set of non-container categories respectively includes: For each computing resource pool, perform the following operations: Based on the load configuration data preset for the load type, in combination with the set of non-container categories having a container call relationship with the set of container categories, determine the quantity ratio of the test containers associated with the set of container categories and the set of non-container categories respectively in the set of a set number of service nodes included in a computing resource pool.

3. The method according to claim 2, wherein The target test parameters corresponding to each type of test metric are detected in real time; Then the method further includes: During the pressure test, in response to a running stop operation triggered for a target computing resource pool among the target number of computing resource pools, abort the running of at least one test container included in the target computing resource pool. Respectively obtain the target test parameters of each type of test metric before and after the target computing resource pool stops running. According to the change situation of the target test parameters of each type of test metric before and after the target computing resource pool stops running, determine the disaster tolerance ability of the test system.

4. The method according to claim 2, wherein The target test parameters corresponding to each type of test metric are detected in real time; Then the method further includes: During the pressure test, in response to a running stop operation triggered for a target service node among the set number of service nodes, abort the running of at least one test container included in the target service node. Respectively obtain the target test parameters of each type of test metric before and after the target service node stops running. Determine the fault masking ability of the test system according to the change of the target test parameters of each type of test index before and after the target service node stops running.

5. The method according to any one of claims 1-4, characterized in that, The selecting, from multiple container categories, of the set of container categories associated with each load type according to each load type associated with the target pressure scenario includes: When the load type is the processor CPU load type, determine that the set of container categories associated with the CPU load type includes: logical layer test containers; When the load type is the memory load type, determine that the set of container categories associated with the memory load type includes: logical layer test containers; When the load type is the network load type, determine that the set of container categories associated with the network load type includes: logical layer test containers and common gateway interface CGI layer test containers; When the load type is the disk load type, determine that the set of container categories associated with the disk load type includes: storage layer test containers.

6. The method according to claim 5, wherein The obtaining of the quantity ratio of the test containers associated with the set of container categories and the non-container categories respectively, based on the load configuration data preset for a corresponding load type and in combination with the non-container categories having a container call relationship with the set of container categories, includes: When the load type is the CPU load type, determine that the non-container categories having a container call relationship with the logical layer test containers include: CGI layer test containers, and determine the quantity ratio between the logical layer test containers and the CGI layer test containers; When the load type is the memory load type, determine that the non-container categories having a container call relationship with the logical layer test containers include: CGI layer test containers, and determine the quantity ratio between the logical layer test containers and the CGI layer test containers; When the load type is the network load type, determine that the non-container categories are an empty set according to the container call relationship, and determine the quantity ratio between the logical layer test containers and the CGI layer test containers; When the load type is the network disk load type, determine that the non-container categories having a container call relationship with the storage layer test containers include: CGI layer test containers and logical layer test containers, and determine the quantity ratio among the CGI layer test containers, the logical layer test containers and the storage layer test containers.

7. A pressure testing device, characterized in that, Including: A selection unit, configured to select, from multiple container categories, the set of container categories associated with each load type according to each load type associated with the target pressure scenario; wherein each container category is associated with at least one test container, and each test container is used to simulate a test environment for a pressure test task conforming to the corresponding load type; A first processing unit, configured to perform the following operations for each load type respectively: based on the load configuration data preset for a corresponding load type, in combination with the non-container categories having a container call relationship with the set of container categories, obtain the quantity ratio of the test containers associated with the set of container categories and the non-container categories respectively, and based on the quantity ratio, perform a corresponding pressure test between the test containers associated with the set of container categories and the non-container categories, to obtain the test sub-parameters of each type of test index; A second processing unit, configured to perform the following operations respectively for each type of test metric: obtaining target test parameters of the type of test metric based on test sub-parameters corresponding to each load type for the type of test metric; A determination unit, configured to compare the target test parameters corresponding to each type of test metric with corresponding test thresholds respectively, to obtain a stress test result in the target stress scenario.

8. The device according to claim 7, characterized in that, The determination unit is further configured to: determine a target number of computing resource pools included in a test system where a test container is located, and a set number of business node sets included in each computing resource pool; wherein each test container is deployed on any one of the business nodes in the business node set; When obtaining the quantity ratio of test containers associated with the container category set and the non-container category set respectively, the first processing unit is specifically configured to: for each computing resource pool, perform the following operations: based on load configuration data preset for the load type, and in combination with a non-container category set having a container call relationship with the container category set, determine the quantity ratio of test containers associated with the container category set and the non-container category set respectively in the set number of business node sets included in one computing resource pool.

9. An electronic device, characterized in that, Comprising: a memory and a processor; The memory is configured to store computer instructions; The processor is configured to obtain the computer instructions stored in the memory, and execute the method according to any one of claims 1-6 in accordance with the computer instructions.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions, and when the computer instructions are executed by the processor, the method according to any one of claims 1-6 is implemented.

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