PaaS platform stability test method, device and equipment

Abstract

The invention discloses a PaaS platform stability test method, device and equipment. The method comprises the following steps: creating a plurality of virtual machine instances; configuring a corresponding application program for each virtual machine instance in the plurality of virtual machine instances, and monitoring a performance index of each virtual machine instance when the application program is operated; executing high-frequency operation on the plurality of virtual machine instances in parallel, and monitoring operation data of each virtual machine instance in the plurality of virtual machine instances when the high-frequency operation is executed; and if the performance index and / or the operation data of each virtual machine instance are / is abnormal, generating an exception recovery strategy, and realizing the stability test of the PaaS platform based on the exception recovery strategy. The method is not only suitable for the stability test of the PaaS platform in a high-concurrency and high-load scene, but also capable of automatically and dynamically adjusting the parameters in the stability test process of the PaaS platform, so that the test efficiency and accuracy are improved.

Description

Technical Field

[0001] The present application relates to the field of testing technology, and in particular to a method, device, and equipment for testing the stability of a PaaS platform. Background Art

[0002] With the development of cloud computing technology, PaaS (Platform as a Service) has become one of the important infrastructures for developing, deploying and managing applications. The PaaS platform provides developers with virtualized computing, storage and network resources, manages virtual machine instances through APIs or control panels, and helps developers quickly develop, test and deploy applications. The stability test of the PaaS platform is very important, which can ensure the stable operation of the PaaS platform.

[0003] Currently, stability testing of PaaS platforms is usually performed by manually writing and executing scripts.

[0004] However, the current methods are not applicable to high-concurrency and high-load scenarios. Summary of the invention

[0005] The main purpose of this application is to provide a PaaS platform stability testing method, device, and equipment to solve the problem that existing methods are not applicable to high-concurrency and high-load scenarios.

[0006] In order to achieve the above objectives, in a first aspect, the present application provides a PaaS platform stability testing method, comprising:

[0007] Create multiple virtual machine instances;

[0008] configuring a corresponding application for each of the plurality of virtual machine instances and monitoring a performance indicator of each virtual machine instance while running the application;

[0009] performing high-frequency operations on the plurality of virtual machine instances in parallel and monitoring operation data of each of the plurality of virtual machine instances while performing the high-frequency operations;

[0010] If the performance indicators and / or operation data of each virtual machine instance are abnormal, an abnormal recovery strategy is generated, and the PaaS platform stability test is implemented based on the abnormal recovery strategy.

[0011] In a possible implementation, multiple virtual machine instances are created, including:

[0012] Obtain preset resource allocation rules;

[0013] Corresponding resources are configured for each virtual machine instance in the multiple virtual machine instances through preset resource allocation rules.

[0014] In a possible implementation, configuring a corresponding application for each of the multiple virtual machine instances and monitoring a performance indicator of each virtual machine instance when the application is running includes:

[0015] monitoring resource usage of each of the plurality of virtual machine instances;

[0016] Divide the resource usage of each virtual machine instance according to the load to obtain different types of virtual machine instances;

[0017] Corresponding applications are configured for different types of virtual machine instances according to priority, and performance indicators of each of the multiple virtual machine instances are collected when the applications are running.

[0018] In a possible implementation, resource usage of each virtual machine instance is divided according to the load to obtain different types of virtual machine instances, including:

[0019] The virtual machine instance whose resource usage is greater than or equal to the first preset threshold is regarded as a high-load instance;

[0020] The virtual machine instance whose resource usage is less than the first preset threshold is regarded as a medium load instance;

[0021] A virtual machine instance whose resource usage is less than a first preset threshold and whose CPU utilization, memory and network bandwidth are in a low usage state is regarded as a low-load instance.

[0022] In a possible implementation, if a performance indicator of each virtual machine instance is abnormal, a first abnormality recovery strategy is generated, including:

[0023] Compare the performance indicator of each virtual machine instance with the historical performance indicator of each virtual machine instance to obtain a comparison result;

[0024] If the comparison result exceeds a second preset threshold, a first abnormality recovery strategy is generated, wherein the first abnormality recovery strategy reallocates resources for each virtual machine instance.

[0025] In a possible implementation, performing high-frequency operations on multiple virtual machine instances in parallel and monitoring operation data of each of the multiple virtual machine instances when performing the high-frequency operations include:

[0026] Start multiple virtual machine instances and perform power on, power off, and switch operations on multiple virtual machine instances in parallel;

[0027] Each time a startup, shutdown, or switch operation is performed, the execution progress and success rate of the startup, shutdown, or switch operation are monitored.

[0028] In a possible implementation, if the operation data of each virtual machine instance is abnormal, an abnormality recovery strategy is generated, including:

[0029] If the execution progress of the power-on, power-off and switching operations exceeds the third preset threshold, or the success rate of the power-on, power-off and switching operations is lower than the fourth preset threshold, a second abnormal recovery strategy is generated, wherein the second abnormal recovery strategy is to restart the power-on, power-off and switching operations.

[0030] In a possible implementation, the method further includes:

[0031] Obtaining preset evaluation resources and preset resource utilization;

[0032] If the preset evaluation resource is equal to the fifth preset threshold, reducing the number of virtual machine instances for the next time or extending the operation interval;

[0033] If the preset resource utilization is lower than the sixth preset threshold, the number of virtual machine instances is increased next time.

[0034] In a second aspect, an embodiment of the present invention provides a PaaS platform stability testing device, including:

[0035] A creation module for creating multiple virtual machine instances;

[0036] A first monitoring module, configured to configure a corresponding application for each virtual machine instance among the multiple virtual machine instances and to monitor a performance indicator of each virtual machine instance when running the application;

[0037] A second monitoring module is used to perform high-frequency operations on the multiple virtual machine instances in parallel and monitor operation data of each of the multiple virtual machine instances when performing the high-frequency operations;

[0038] The test module is used to generate an abnormal recovery strategy if the performance indicators and / or operation data of each virtual machine instance are abnormal, and implement PaaS platform stability testing based on the abnormal recovery strategy.

[0039] In a third aspect, an embodiment of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the steps of any of the above PaaS platform stability testing methods are implemented.

[0040] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, the steps of any of the above PaaS platform stability testing methods are implemented.

[0041] In a fifth aspect, an embodiment of the present invention provides a computer program product, including a computer program, which, when executed by a processor, implements any of the above PaaS platform stability testing methods.

[0042] The embodiment of the present invention provides a PaaS platform stability test method, device, and equipment, including: first creating multiple virtual machine instances, then configuring corresponding applications for each of the multiple virtual machine instances and monitoring the performance indicators of each virtual machine instance when running the application, and performing high-frequency operations on multiple virtual machine instances in parallel and monitoring the operation data of each of the multiple virtual machine instances when performing high-frequency operations, if the performance indicators and / or operation data of each virtual machine instance are abnormal, generating an abnormal recovery strategy, and implementing PaaS platform stability test based on the abnormal recovery strategy. The present invention is not only suitable for PaaS platform stability testing in high-concurrency and high-load scenarios, but also can automatically and dynamically adjust the parameters in the PaaS platform stability test process, thereby improving the test efficiency and accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0044] Figure 1 It is a flow chart of a PaaS platform stability testing method provided by an embodiment of the present invention;

[0045] Figure 2 It is a structural schematic diagram of a PaaS platform stability testing device provided by an embodiment of the present invention;

[0046] Figure 3 It is a schematic diagram of a computer device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in sequences other than those illustrated or described herein.

[0049] It should be understood that in various embodiments of the present invention, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0050] It should be understood that in the present invention, "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0051] It should be understood that in the present invention, "plurality" refers to two or more than two. "And / or" is merely a description of the association relationship of associated objects, indicating that three relationships may exist. For example, and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "Contains A, B and C", "Contains A, B, C" means that A, B, and C are all included, "Contains A, B or C" means that one of A, B, and C is included, and "Contains A, B and / or C" means that any one, any two, or any three of A, B, and C are included.

[0052] It should be understood that in the present invention, "B corresponding to A", "B corresponding to A", "A corresponds to B" or "B corresponds to A" means that B is associated with A and B can be determined based on A. Determining B based on A does not mean determining B based only on A, but B can also be determined based on A and / or other information. A and B match when the similarity between A and B is greater than or equal to a preset threshold.

[0053] Depending on the context, "if" as used herein may be interpreted as "when" or "when" or "in response to determining" or "in response to detecting."

[0054] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0055] In order to make the purpose, technical solutions and advantages of the present invention more clear, specific embodiments will be described below in conjunction with the accompanying drawings.

[0056] In one embodiment, Figure 1 As shown, a PaaS platform stability testing method is provided, including the following steps:

[0057] Step S101: Create multiple virtual machine instances.

[0058] For creating multiple virtual machine instances, it is necessary to first obtain a preset resource allocation rule, and then configure corresponding resources for each of the multiple virtual machine instances according to the preset resource allocation rule.

[0059] Specifically, for batch creation of virtual machine instances, at the beginning of the test, the system creates multiple virtual machine instances in batches through the PaaS platform API. The system parallelizes the operation through automated scripts to ensure that multiple instances can be started efficiently in a short period of time.

[0060] Each time a virtual machine instance is created, the configuration of the virtual machine (such as CPU, memory, network bandwidth, etc.) is determined by the preset resource allocation rules, and then the corresponding resources, such as load, are configured for each of the multiple virtual machine instances through the preset resource allocation rules.

[0061] This application will also adjust the number of virtual machine instances created each time according to the current load of the system. Specifically, the preset evaluation resources and the preset resource utilization rate are first obtained. If the preset evaluation resources are equal to the fifth preset threshold, the number of virtual machine instances for the next time is reduced or the operation interval is extended; if the preset resource utilization rate is lower than the sixth preset threshold, the number of virtual machine instances for the next time is increased. The fifth preset threshold is set according to the specific situation.

[0062] Assume that the system has 50 virtual machine instances that need to be started, and the current CPU utilization is 70%. According to the system evaluation, a maximum of 20 virtual machine instances are currently allowed to be started at the same time. The system will start these virtual machine instances and wait for the operation to complete. When the CPU load reaches 90%, the number of instances to be started next time will be reduced or the startup time will be delayed to ensure that resources are not overloaded.

[0063] This application can analyze the load changes of the system in real time through dynamic resources, and prevent resource exhaustion or operation failure caused by operating too many virtual machine instances at one time. Compared with static batch operations, this scheduling algorithm can adaptively respond to system load changes and ensure stable testing.

[0064] Step S102: configuring a corresponding application for each of the multiple virtual machine instances and monitoring the performance indicators of each virtual machine instance when running the application.

[0065] In order to configure a corresponding application for each of multiple virtual machine instances and monitor the performance indicators of each virtual machine instance when running the application, it is necessary to first monitor the resource usage of each of the multiple virtual machine instances, and then divide the resource usage of each virtual machine instance according to the load to obtain different types of virtual machine instances, and then configure corresponding applications for different types of virtual machine instances according to priority and collect the performance indicators of each of the multiple virtual machine instances when running the application.

[0066] Among them, the resource usage of each virtual machine instance is divided according to the load to obtain different types of virtual machine instances, including: virtual machine instances with resource usage greater than or equal to a first preset threshold are regarded as high-load instances; virtual machine instances with resource usage less than the first preset threshold are regarded as medium-load instances; virtual machine instances with resource usage less than the first preset threshold and CPU utilization, memory and network bandwidth in low usage state are regarded as low-load instances.

[0067] Specifically, after each virtual machine instance is started, the system automatically runs the corresponding application. The application includes resource-intensive applications, such as video players or WebRTC streaming media applications. These applications will bring certain computing and network loads to the virtual machine instance.

[0068] Monitor the performance indicators of each virtual machine instance during the operation of the application. That is, after the application is started, the system continuously monitors its performance indicators through automated scripts, such as the usage of CPU, memory, and network bandwidth in resource consumption, and records the resource consumption curve of each virtual machine instance to analyze the performance of the system under stress load.

[0069] By analyzing the performance of the system under pressure load, resources are allocated and dynamically adjusted. Specifically, the system dynamically adjusts resource allocation according to the resource usage of each virtual machine instance. For example, if an application in a virtual machine instance occupies too much CPU resources, the system will prioritize lowering the resource priority of other instances or limiting resource consumption to ensure that key tasks can be executed smoothly.

[0070] For example, after a virtual machine instance is started, the system starts a resource monitoring module to track the CPU, memory, and network bandwidth usage of each virtual machine instance in real time.

[0071] The system monitors resource consumption through preset resource thresholds (such as a CPU utilization upper limit of 90% and a memory utilization upper limit of 80%) to ensure that the application's resource usage remains within a reasonable range.

[0072] The system divides the resource usage of each VM instance into three cases according to the load, and dynamically configures resources based on different types of VM instances:

[0073] High-load instances: The resource usage of an instance approaches or exceeds the set threshold, such as CPU utilization reaching 90% or memory usage approaching 80%. For high-load instances: When the resource usage of an instance approaches or exceeds the set threshold, the algorithm will prioritize allocating more resources to the instance. For example, if the CPU utilization of an instance reaches 90%, the system will dynamically increase the CPU quota for the instance or release resources for other low-priority tasks.

[0074] Medium-load instances: Instances with high resource usage but not close to the threshold, such as CPU utilization between 60% and 80%. For medium-load instances: Resource allocation for medium-load instances remains stable, but the algorithm regularly monitors its resource consumption. If the load of the instance increases, the system adjusts its resource allocation strategy and increases the quota appropriately to prevent its load from continuing to rise and causing resource bottlenecks.

[0075] Low-load instances: The instance uses less resources, the CPU utilization is below 50%, and the memory and network bandwidth are also in a low-usage state. For low-load instances: For low-load instances, the algorithm will reduce its resource allocation, especially when resources are tight (such as when the overall CPU utilization is high), the system will reduce the resource priority of low-load instances to ensure that more resources are used for high-priority tasks.

[0076] In addition, this application also assigns different resource priorities to each virtual machine instance based on the importance of the task. Critical tasks (such as real-time communication or high-definition video streaming) are set to high priority, and when resources are tight, the resource requirements of these tasks will be prioritized. Non-critical tasks are set to low priority, and their resource usage priority will be lowered when resource pressure is high. The priority mechanism ensures that critical tasks will not be affected in a resource-constrained environment, while avoiding unnecessary waste of resources.

[0077] After dynamic resource allocation, the load of the adjusted virtual machine instance will be continuously monitored. If the load of the virtual machine instance is alleviated after the resource adjustment (for example, the CPU utilization rate drops from 90% to 80%), the system will maintain the current resource allocation strategy; if the load is not improved, the system will further adjust the resource allocation strategy until the instance load returns to normal levels.

[0078] If the resource allocation of a low-load instance is too low, causing performance degradation, the system will reallocate resources to maintain the stability of its operation after the resource load returns to normal.

[0079] This application combines a resource priority mechanism, which enables the system to allocate more resources to important tasks (such as real-time communication applications) and dynamically reduce resource allocation to low-priority tasks. This mechanism ensures the stability of the application under resource-constrained conditions while avoiding resource waste.

[0080] Step S103: executing high-frequency operations on multiple virtual machine instances in parallel and monitoring operation data of each of the multiple virtual machine instances when executing the high-frequency operations.

[0081] In order to perform high-frequency operations on multiple virtual machine instances in parallel and monitor the operation data of each of the multiple virtual machine instances when performing high-frequency operations, it is necessary to first start multiple virtual machine instances, and perform power-on, power-off, and switching operations on the multiple virtual machine instances in parallel. Each time a power-on, power-off, and switching operation is performed, the execution progress and success rate of the power-on, power-off, and switching operations are monitored.

[0082] Specifically, perform startup, shutdown, and switching operations on virtual machine instances:

[0083] After the virtual machine instance is started, the system will perform batch startup, shutdown and instance switching operations to simulate frequent user operation scenarios. These operations are performed through automated scripts called by APIs, and the execution time and success rate of each operation are monitored.

[0084] By dynamically evaluating system resources, the execution order and quantity of batch operations are reasonably arranged. Before each operation is executed, it will decide whether to allow a new round of operations to be started based on indicators such as current CPU utilization and memory consumption, and reasonably allocate the scale of each batch of operations.

[0085] During the execution of the operation, data such as CPU utilization, memory consumption, and network bandwidth usage are collected in real time, and a batch operation queue is initialized to add operations such as virtual machine instance creation, startup, shutdown, and switching to the queue to be executed.

[0086] You can set the threshold for each resource (such as CPU utilization not exceeding 90%, memory utilization not exceeding 80%, network bandwidth utilization not exceeding 85%, etc.), set the number of initial batch operations (such as starting no more than 20 virtual machine instances in a batch each time), and allow the algorithm to dynamically adjust the batch size.

[0087] Before executing each batch of operations, the system's current resource status, including CPU, memory, and network load, will be evaluated. If any resource is close to the preset threshold (such as CPU utilization exceeding 80%), the algorithm will reduce the number of operations in the next batch or extend the interval between operations; if the resource utilization is lower than the set threshold (such as CPU utilization below 50%), the number of operations in the next batch will be increased to ensure maximum test efficiency.

[0088] While ensuring that the resource load is controllable, the system starts executing the current batch of operations (such as batch starting virtual machine instances). Each time an operation is executed, the start time and resource consumption of the operation are recorded for subsequent monitoring.

[0089] After the operation is executed, the current resource usage is evaluated again, and the number of operations and the operation interval of the next batch are adjusted according to the change in resource load after the operation. If the resource load increases significantly (such as CPU utilization rising from 70% to 90%), the number of instances of the next batch of operations is reduced to prevent resource exhaustion; if the resource load decreases or stabilizes, the system increases the batch size to ensure that the batch operation is completed as soon as possible.

[0090] In high-concurrency scenarios, the system monitors the current resource load (CPU, memory, network) in real time and dynamically adjusts the number of instances to be started in the next batch according to the current system status. If the system resources are close to the load limit, the system will automatically reduce the number of instances to be started in the next batch or delay the operation.

[0091] In addition to real-time collection of resource usage (such as CPU, memory, network bandwidth, etc.) of each virtual machine instance, this application also monitors network performance indicators, especially network latency, jitter, packet loss rate, etc. of real-time communication applications such as WebRTC. These network performance indicators will be visualized in the real-time monitoring system to help evaluate the performance of applications under network pressure.

[0092] Step S104: If the performance indicators and / or operation data of each virtual machine instance are abnormal, an abnormal recovery strategy is generated, and the PaaS platform stability test is implemented based on the abnormal recovery strategy.

[0093] If the performance indicator of each virtual machine instance is abnormal, a first abnormality recovery strategy is generated, including: comparing the performance indicator of each virtual machine instance with the historical performance indicator of each virtual machine instance to obtain a comparison result; if the comparison result exceeds a second preset threshold, a first abnormality recovery strategy is generated, wherein the first abnormality recovery strategy reallocates resources for each virtual machine instance.

[0094] Specifically, if the resource usage of a virtual machine instance fluctuates abnormally (such as the CPU utilization suddenly soars to 100% and continues to be highly loaded), the system will record the abnormal situation of the virtual machine instance, issue an early warning, and try to reduce the load of the instance according to the preset exception handling rules (for example, forcibly reducing its resource consumption or adjusting the task execution priority).

[0095] If an abnormality occurs in the operating data of each virtual machine instance, an abnormal recovery strategy is generated, including: if the execution progress of the power-on, power-off and switching operations exceeds the third preset threshold, or the success rate of the power-on, power-off and switching operations is lower than the fourth preset threshold, a second abnormal recovery strategy is generated, wherein the second abnormal recovery strategy is to restart the power-on, power-off and switching operations.

[0096] Specifically, if an operation fails (such as a virtual machine startup failure), the system will automatically retry the operation and record a log for subsequent analysis. If multiple retries still fail, the system will suspend subsequent batch operations, restore the initial state, and wait for the next round of operation requests.

[0097] When the batch operation is completed, the system evaluates the overall resource consumption and releases excess resources if necessary.

[0098] After the test is completed, a detailed log report will be automatically generated, including the execution status of each test operation, resource usage, exception capture records, etc., and performance analysis will be performed based on this data to identify possible bottlenecks or optimization space in the system.

[0099] Specifically, after each operation (such as virtual machine startup, shutdown, switching, etc.) is completed, the operation log is automatically recorded, including the start time, execution duration, resource usage and success or failure status of the operation.

[0100] After each test, a test report will be automatically generated, recording in detail each operation and resource usage during the test, and highlighting abnormal operations for subsequent analysis. For example, the data analysis module can conduct a comprehensive analysis of the collected logs and performance data, identify possible bottlenecks or performance optimization space in the system, and give corresponding optimization suggestions.

[0101] Exemplarily, the system automatically records logs after each operation is completed, including: operation type (such as virtual machine startup, shutdown, switching, etc.), start time and execution duration, operation status (success or failure), resource usage (such as CPU, memory, network bandwidth, etc.), exception information (such as the reason for the operation failure, instance ID, timestamp, etc.), and automatic generation of logs to ensure that all test operations are recorded in detail for subsequent analysis.

[0102] After each test, the system automatically summarizes the operation logs to generate a test report, which records in detail the execution status and resource consumption of each operation. Abnormal operations will be highlighted in the report, such as certain virtual machine startup failures or operation timeouts, which will be listed separately and provide relevant detailed logs to help administrators quickly locate problems.

[0103] When generating reports, the system will also visualize key performance indicators in the test (such as CPU utilization, memory consumption, network latency, etc.) to help administrators better understand the operation of the system.

[0104] The data analysis module automatically analyzes all operation logs and performance data to find patterns and regularities. The analysis includes:

[0105] Operation success rate analysis: Check the success rate of all operations and identify the types or instances of operations that fail more frequently.

[0106] Resource utilization analysis: Identify which virtual machine instances consume excessive resources (such as CPU, memory, etc.) during the test, and analyze whether there is unreasonable resource allocation.

[0107] Operation delay analysis: Check whether there are delays in the execution time of various operations, especially in high-concurrency operations, to check whether there are delays in starting or switching operations.

[0108] Abnormal pattern analysis: By analyzing the records of abnormal operations, we can identify the abnormal types that occur frequently in the system and track the patterns of their occurrence.

[0109] By analyzing log data, the system can quickly identify key bottlenecks that affect system performance. Common bottlenecks include:

[0110] Resource contention: When multiple instances are started or operated at the same time, CPU or memory resource contention causes operation failure or delay.

[0111] Load imbalance: The resource utilization of some instances is too high, resulting in a decrease in the overall system performance.

[0112] Network bottleneck: Especially in real-time communication applications such as WebRTC, network delays, jitters and other issues may cause application performance to degrade.

[0113] The system analyzes historical data to identify the root causes of performance bottlenecks and provide specific optimization suggestions.

[0114] After that, the system automatically generates optimization suggestions based on the results of log analysis. The optimization suggestions are based on a comprehensive evaluation of the current performance of the system and combine historical test data to provide specific adjustment plans. Common optimization suggestions include:

[0115] Resource allocation optimization: In case of resource contention, the system may recommend delaying certain high-load virtual machine operations until resources are released to avoid starting multiple high-resource consumption instances at the same time.

[0116] Load balancing optimization: If the load on some instances is too high, the system recommends transferring some tasks to other instances with lower resource utilization through the load balancing mechanism.

[0117] Network optimization: In response to network bottlenecks, the system may recommend reducing the resolution of streaming applications, lowering bandwidth usage, or extending the frame transmission interval to optimize network performance.

[0118] Through these optimization suggestions, administrators can make adjustments quickly to improve the efficiency and stability of the overall system operation.

[0119] After each test is completed, the system not only generates logs and reports, but also adds new log data to the historical database. In this way, the system's log analysis algorithm can continue to learn and improve, enhancing its ability to predict potential problems in future tests.

[0120] The system's optimization suggestions will be adjusted and improved based on the actual results of each test, forming a self-improvement closed-loop mechanism to help the system gradually optimize its performance and resource management capabilities.

[0121] Through intelligent data analysis, the system can automatically discover performance issues exposed during the test and propose optimization solutions based on historical data and current performance. This innovation can greatly reduce the errors caused by human analysis and improve the accuracy of the test.

[0122] The embodiment of the present invention provides a PaaS platform stability testing method, including: first creating multiple virtual machine instances, then configuring corresponding applications for each of the multiple virtual machine instances and monitoring the performance indicators of each virtual machine instance when running the application, and performing high-frequency operations on multiple virtual machine instances in parallel and monitoring the operation data of each of the multiple virtual machine instances when performing high-frequency operations, if the performance indicators and / or operation data of each virtual machine instance are abnormal, generating an abnormal recovery strategy, and implementing the PaaS platform stability test based on the abnormal recovery strategy. The present invention is not only suitable for the PaaS platform stability test in high-concurrency and high-load scenarios, but also can automatically and dynamically adjust the parameters in the PaaS platform stability test process, thereby improving the test efficiency and accuracy.

[0123] It should be understood that the order of execution of the steps in the above embodiment does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention.

[0124] The following is an embodiment of the device of the present invention. For details not described in detail therein, reference may be made to the corresponding method embodiment described above.

[0125] Figure 2A schematic diagram of the structure of a PaaS platform stability testing device provided by an embodiment of the present invention is shown. For the sake of convenience, only the parts related to the embodiment of the present invention are shown. A PaaS platform stability testing device includes a creation module 201, a first monitoring module 202, a second monitoring module 203 and a testing module 204, which are as follows:

[0126] A creation module 201, used to create multiple virtual machine instances;

[0127] A first monitoring module 202, configured to configure a corresponding application for each virtual machine instance among the multiple virtual machine instances and to monitor a performance indicator of each virtual machine instance when running the application;

[0128] A second monitoring module 203, configured to perform high-frequency operations on multiple virtual machine instances in parallel and monitor operation data of each of the multiple virtual machine instances when performing the high-frequency operations;

[0129] The test module 204 is used to generate an abnormality recovery strategy if an abnormality occurs in the performance indicators and / or operation data of each virtual machine instance, and implement a PaaS platform stability test based on the abnormality recovery strategy.

[0130] In a possible implementation, the creation module 201 is further used to obtain a preset resource allocation rule;

[0131] Corresponding resources are configured for each virtual machine instance in the multiple virtual machine instances through preset resource allocation rules.

[0132] In a possible implementation, the first monitoring module 202 is further configured to monitor resource usage of each virtual machine instance among the multiple virtual machine instances;

[0133] Divide the resource usage of each virtual machine instance according to the load to obtain different types of virtual machine instances;

[0134] Corresponding applications are configured for different types of virtual machine instances according to priority, and performance indicators of each of the multiple virtual machine instances are collected when the applications are running.

[0135] In a possible implementation, the first monitoring module 202 is further configured to take a virtual machine instance whose resource usage is greater than or equal to a first preset threshold as a high-load instance;

[0136] The virtual machine instance whose resource usage is less than the first preset threshold is regarded as a medium load instance;

[0137] A virtual machine instance whose resource usage is less than a first preset threshold and whose CPU utilization, memory and network bandwidth are in a low usage state is regarded as a low-load instance.

[0138] In a possible implementation, the testing module 204 is further used to compare the performance indicator of each virtual machine instance with the historical performance indicator of each virtual machine instance to obtain a comparison result;

[0139] If the comparison result exceeds a second preset threshold, a first abnormality recovery strategy is generated, wherein the first abnormality recovery strategy reallocates resources for each virtual machine instance.

[0140] In a possible implementation, the second monitoring module 203 is further used to start multiple virtual machine instances, and perform startup, shutdown, and switching operations on the multiple virtual machine instances in parallel;

[0141] Each time a startup, shutdown, or switch operation is performed, the execution progress and success rate of the startup, shutdown, or switch operation are monitored.

[0142] In one possible implementation, the test module 204 is also used to generate a second abnormal recovery strategy if the execution progress of the power-on, power-off and switching operations exceeds a third preset threshold, or the success rate of the power-on, power-off and switching operations is lower than a fourth preset threshold, wherein the second abnormal recovery strategy is to restart the power-on, power-off and switching operations.

[0143] In a possible implementation, the device further includes: an adjustment module, the adjustment module being configured to obtain a preset evaluation resource and a preset resource utilization rate;

[0144] If the preset evaluation resource is equal to the fifth preset threshold, reducing the number of virtual machine instances for the next time or extending the operation interval;

[0145] If the preset resource utilization is lower than the sixth preset threshold, the number of virtual machine instances is increased next time.

[0146] The embodiment of the present invention provides a PaaS platform stability testing device, which is specifically used to: first create multiple virtual machine instances, then configure corresponding applications for each of the multiple virtual machine instances and monitor the performance indicators of each virtual machine instance when running the application, and perform high-frequency operations on multiple virtual machine instances in parallel and monitor the operation data of each of the multiple virtual machine instances when performing high-frequency operations. If the performance indicators and / or operation data of each virtual machine instance are abnormal, an abnormal recovery strategy is generated, and the PaaS platform stability test is implemented based on the abnormal recovery strategy. The present invention is not only suitable for the PaaS platform stability test in high-concurrency and high-load scenarios, but also can automatically and dynamically adjust the parameters in the PaaS platform stability test process, thereby improving the test efficiency and accuracy.

[0147] Figure 3 Schematic diagram of a computer device provided by an embodiment of the present invention. Figure 3 As shown, the computer device 3 of this embodiment includes: a processor 301, a memory 302, and a computer program 303 stored in the memory 302 and executable on the processor 301. When the processor 301 executes the computer program 303, the steps in the above-mentioned various PaaS platform stability test method embodiments are implemented, such as Figure 1 Alternatively, when the processor 301 executes the computer program 303, the functions of each module / unit in the above-mentioned embodiments of the PaaS platform stability testing device are realized, for example Figure 2 Functionality of modules / units 201-204 is shown.

[0148] The present invention also provides a readable storage medium, in which a computer program is stored. When the computer program is executed by a processor, it is used to implement a PaaS platform stability testing method provided in the above various embodiments, including:

[0149] Create multiple virtual machine instances;

[0150] configuring a corresponding application for each of the plurality of virtual machine instances and monitoring a performance indicator of each virtual machine instance while running the application;

[0151] performing high-frequency operations on the plurality of virtual machine instances in parallel and monitoring operation data of each of the plurality of virtual machine instances while performing the high-frequency operations;

[0152] If the performance indicators and / or operation data of each virtual machine instance are abnormal, an abnormal recovery strategy is generated, and the PaaS platform stability test is implemented based on the abnormal recovery strategy.

[0153] In a possible implementation, multiple virtual machine instances are created, including:

[0154] Obtain preset resource allocation rules;

[0155] Corresponding resources are configured for each virtual machine instance in the multiple virtual machine instances through preset resource allocation rules.

[0156] In a possible implementation, configuring a corresponding application for each of the multiple virtual machine instances and monitoring a performance indicator of each virtual machine instance when the application is running includes:

[0157] monitoring resource usage of each of the plurality of virtual machine instances;

[0158] Divide the resource usage of each virtual machine instance according to the load to obtain different types of virtual machine instances;

[0159] Corresponding applications are configured for different types of virtual machine instances according to priority, and performance indicators of each of the multiple virtual machine instances are collected when the applications are running.

[0160] In a possible implementation, resource usage of each virtual machine instance is divided according to the load to obtain different types of virtual machine instances, including:

[0161] The virtual machine instance whose resource usage is greater than or equal to the first preset threshold is regarded as a high-load instance;

[0162] The virtual machine instance whose resource usage is less than the first preset threshold is regarded as a medium load instance;

[0163] A virtual machine instance whose resource usage is less than a first preset threshold and whose CPU utilization, memory and network bandwidth are in a low usage state is regarded as a low-load instance.

[0164] In a possible implementation, if a performance indicator of each virtual machine instance is abnormal, a first abnormality recovery strategy is generated, including:

[0165] Compare the performance indicator of each virtual machine instance with the historical performance indicator of each virtual machine instance to obtain a comparison result;

[0166] If the comparison result exceeds a second preset threshold, a first abnormality recovery strategy is generated, wherein the first abnormality recovery strategy reallocates resources for each virtual machine instance.

[0167] In a possible implementation, performing high-frequency operations on multiple virtual machine instances in parallel and monitoring operation data of each of the multiple virtual machine instances when performing the high-frequency operations include:

[0168] Start multiple virtual machine instances and perform power on, power off, and switch operations on multiple virtual machine instances in parallel;

[0169] Each time a startup, shutdown, or switch operation is performed, the execution progress and success rate of the startup, shutdown, or switch operation are monitored.

[0170] In a possible implementation, if the operation data of each virtual machine instance is abnormal, an abnormality recovery strategy is generated, including:

[0171] If the execution progress of the power-on, power-off and switching operations exceeds the third preset threshold, or the success rate of the power-on, power-off and switching operations is lower than the fourth preset threshold, a second abnormal recovery strategy is generated, wherein the second abnormal recovery strategy is to restart the power-on, power-off and switching operations.

[0172] In a possible implementation, the method further includes:

[0173] Obtaining preset evaluation resources and preset resource utilization;

[0174] If the preset evaluation resource is equal to the fifth preset threshold, reducing the number of virtual machine instances for the next time or extending the operation interval;

[0175] If the preset resource utilization is lower than the sixth preset threshold, the number of virtual machine instances is increased next time.

[0176] Among them, the readable storage medium can be a computer storage medium or a communication medium. The communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The computer storage medium can be any available medium that can be accessed by a general or special-purpose computer. For example, a readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can be located in an application-specific integrated circuit (ASIC). In addition, the ASIC can be located in a user device. Of course, the processor and the readable storage medium can also exist in a communication device as discrete components. The readable storage medium can be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, and an optical data storage device, etc.

[0177] The present invention also provides a program product, which includes an execution instruction, and the execution instruction is stored in a readable storage medium. At least one processor of a device can read the execution instruction from the readable storage medium, and at least one processor executes the execution instruction so that the device implements a PaaS platform stability testing method provided by the above various embodiments, including:

[0178] Create multiple virtual machine instances;

[0179] configuring a corresponding application for each of the plurality of virtual machine instances and monitoring a performance indicator of each virtual machine instance while running the application;

[0180] performing high-frequency operations on the plurality of virtual machine instances in parallel and monitoring operation data of each of the plurality of virtual machine instances while performing the high-frequency operations;

[0181] If the performance indicators and / or operation data of each virtual machine instance are abnormal, an abnormal recovery strategy is generated, and the PaaS platform stability test is implemented based on the abnormal recovery strategy.

[0182] In a possible implementation, multiple virtual machine instances are created, including:

[0183] Obtain preset resource allocation rules;

[0184] Corresponding resources are configured for each virtual machine instance in the multiple virtual machine instances through preset resource allocation rules.

[0185] In a possible implementation, configuring a corresponding application for each of the multiple virtual machine instances and monitoring a performance indicator of each virtual machine instance when the application is running includes:

[0186] monitoring resource usage of each of the plurality of virtual machine instances;

[0187] Divide the resource usage of each virtual machine instance according to the load to obtain different types of virtual machine instances;

[0188] Corresponding applications are configured for different types of virtual machine instances according to priority, and performance indicators of each of the multiple virtual machine instances are collected when the applications are running.

[0189] In a possible implementation, resource usage of each virtual machine instance is divided according to the load to obtain different types of virtual machine instances, including:

[0190] The virtual machine instance whose resource usage is greater than or equal to the first preset threshold is regarded as a high-load instance;

[0191] The virtual machine instance whose resource usage is less than the first preset threshold is regarded as a medium load instance;

[0192] A virtual machine instance whose resource usage is less than a first preset threshold and whose CPU utilization, memory and network bandwidth are in a low usage state is regarded as a low-load instance.

[0193] In a possible implementation, if a performance indicator of each virtual machine instance is abnormal, a first abnormality recovery strategy is generated, including:

[0194] Compare the performance indicator of each virtual machine instance with the historical performance indicator of each virtual machine instance to obtain a comparison result;

[0195] If the comparison result exceeds a second preset threshold, a first abnormality recovery strategy is generated, wherein the first abnormality recovery strategy reallocates resources for each virtual machine instance.

[0196] In a possible implementation, performing high-frequency operations on multiple virtual machine instances in parallel and monitoring operation data of each of the multiple virtual machine instances when performing the high-frequency operations include:

[0197] Start multiple virtual machine instances and perform power on, power off, and switch operations on multiple virtual machine instances in parallel;

[0198] Each time a startup, shutdown, or switch operation is performed, the execution progress and success rate of the startup, shutdown, or switch operation are monitored.

[0199] In a possible implementation, if the operation data of each virtual machine instance is abnormal, an abnormality recovery strategy is generated, including:

[0200] If the execution progress of the power-on, power-off and switching operations exceeds the third preset threshold, or the success rate of the power-on, power-off and switching operations is lower than the fourth preset threshold, a second abnormal recovery strategy is generated, wherein the second abnormal recovery strategy is to restart the power-on, power-off and switching operations.

[0201] In a possible implementation, the method further includes:

[0202] Obtaining preset evaluation resources and preset resource utilization;

[0203] If the preset evaluation resource is equal to the fifth preset threshold, reducing the number of virtual machine instances for the next time or extending the operation interval;

[0204] If the preset resource utilization is lower than the sixth preset threshold, the number of virtual machine instances is increased next time.

[0205] In the embodiments of the above-mentioned devices, it should be understood that the processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc. The steps of the method disclosed in the present invention may be directly embodied as being executed by a hardware processor, or may be executed by a combination of hardware and software modules in the processor.

[0206] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A PaaS platform stability testing method, characterized in that: include: Create multiple virtual machine instances; Configuring a corresponding application for each of the multiple virtual machine instances and monitoring a performance indicator of each virtual machine instance when running the application; performing high-frequency operations on the plurality of virtual machine instances in parallel and monitoring operation data of each of the plurality of virtual machine instances while performing the high-frequency operations; If an abnormality occurs in the performance indicators and / or operation data of each virtual machine instance, an abnormality recovery strategy is generated, and the PaaS platform stability test is implemented based on the abnormality recovery strategy.

2. A PaaS platform stability testing method according to claim 1, characterized in that: The step of creating multiple virtual machine instances includes: Obtain preset resource allocation rules; Corresponding resources are configured for each of the multiple virtual machine instances according to the preset resource allocation rule.

3. A PaaS platform stability testing method according to claim 1, characterized in that: The configuring a corresponding application for each of the multiple virtual machine instances and monitoring the performance indicators of each virtual machine instance when running the application includes: Monitoring resource usage of each of the plurality of virtual machine instances; Dividing resource usage of each virtual machine instance according to the load to obtain different types of virtual machine instances; Corresponding applications are configured for the different types of virtual machine instances according to priority, and performance indicators of each of the multiple virtual machine instances are collected when the applications are running.

4. A PaaS platform stability testing method according to claim 3, characterized in that: The resource usage of each virtual machine instance is divided according to the load to obtain different types of virtual machine instances, including: The virtual machine instance whose resource usage is greater than or equal to the first preset threshold is regarded as a high-load instance; The virtual machine instance whose resource usage is less than the first preset threshold is regarded as a medium load instance; The virtual machine instance whose resource usage is less than the first preset threshold and whose CPU utilization, memory and network bandwidth are in a low usage state is regarded as a low-load instance.

5. A PaaS platform stability testing method according to claim 4, characterized in that: If the performance indicator of each virtual machine instance is abnormal, generating a first abnormality recovery strategy includes: Comparing the performance index of each virtual machine instance with the historical performance index of each virtual machine instance to obtain a comparison result; If the comparison result exceeds a second preset threshold, a first abnormality recovery strategy is generated, wherein the first abnormality recovery strategy reallocates resources for each virtual machine instance.

6. A PaaS platform stability testing method according to claim 1, characterized in that: The performing high-frequency operations on the multiple virtual machine instances in parallel and monitoring the operation data of each of the multiple virtual machine instances when performing the high-frequency operations includes: Starting the multiple virtual machine instances, and performing power-on, power-off, and switching operations on the multiple virtual machine instances in parallel; Each time the power-on, power-off and switching operations are performed, the execution progress and success rate of the power-on, power-off and switching operations are monitored.

7. A PaaS platform stability testing method according to claim 6, characterized in that: If the operation data of each virtual machine instance is abnormal, an abnormal recovery strategy is generated, including: If the execution progress of the power-on, power-off and switching operations exceeds a third preset threshold, or the success rate of the power-on, power-off and switching operations is lower than a fourth preset threshold, a second abnormal recovery strategy is generated, wherein the second abnormal recovery strategy is to restart the power-on, power-off and switching operations.

8. A PaaS platform stability testing method according to claim 6, characterized in that: The method further comprises: Obtaining preset evaluation resources and preset resource utilization; If the preset evaluation resource is equal to the fifth preset threshold, reducing the number of virtual machine instances for the next time or extending the operation interval; If the preset resource utilization is lower than a sixth preset threshold, the number of virtual machine instances is increased next time.

9. A PaaS platform stability testing device, characterized in that: include: A creation module for creating multiple virtual machine instances; A first monitoring module, configured to configure a corresponding application for each of the multiple virtual machine instances and monitor a performance indicator of each virtual machine instance when running the application; A second monitoring module, configured to perform high-frequency operations on the multiple virtual machine instances in parallel and monitor operation data of each of the multiple virtual machine instances when performing the high-frequency operations; The test module is used to generate an abnormality recovery strategy if an abnormality occurs in the performance indicators and / or operation data of each virtual machine instance, and implement a PaaS platform stability test based on the abnormality recovery strategy.

10. A computer device, characterized in that: comprising a memory, and one or more processors communicatively connected to the memory; The memory stores instructions that can be executed by the one or more processors, and the instructions are executed by the one or more processors to enable the one or more processors to implement the PaaS platform stability testing method as described in any one of claims 1 to 8.

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