System efficiency guarantee method and device of distributed system, medium and electronic equipment

Abstract

The invention discloses a system efficiency guarantee method and device of a distributed system, a medium and electronic equipment. The method comprises the following steps: obtaining performance data of each target container in a distributed system based on a preset moment through a target server; analyzing the performance data of each target container by adopting a target analysis model to obtain an analysis result, the analysis result comprising at least one of the following: a service subscription quantity trend of each target container, a subscription service call frequency trend of each target container, and a performance deviation degree of each target container; whether the analysis result is abnormal or not is judged, if the analysis result is abnormal, early warning prompt information is generated and sent to a target object, and the target object guarantees the system efficiency of the distributed system based on the early warning prompt information. Through application of the method and the device, the problem of relatively low efficiency caused by manual analysis of efficiency hidden dangers of the distributed system in related technologies is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of distributed technology, in particular to a system performance guarantee method and device of a distributed system, a medium and an electronic device. BACKGROUND

[0002] With the transformation of financial institutions from traditional centralized mainframe systems or centralized platform systems to distributed platform systems, containers become the carrier of a certain type of functional services divided by service level and business granularity. Service dependencies and communication between different containers are completed through service subscription. However, with the increase and exit of business functions, invalid service subscriptions in containers are increasing, leading to a decline in system performance. At present, in the related art, the efficiency is low by manually analyzing the performance risks of the distributed system and implementing inefficient function exit, invalid code cleaning and the like.

[0003] In view of the problem of low efficiency in the related art by manually analyzing the performance risks of the distributed system, an effective solution has not yet been proposed. SUMMARY

[0004] The main purpose of the present application is to provide a system performance guarantee method and device of a distributed system, a medium and an electronic device, to solve the problem of low efficiency in the related art by manually analyzing the performance risks of the distributed system.

[0005] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a system performance guarantee method of a distributed system is provided. The method comprises: acquiring performance data of each target container in the distributed system based on a preset time point by a target server, wherein the target container is used to implement at least one business scenario of the distributed system; analyzing the performance data of each target container by using a target analysis model to obtain an analysis result, wherein the target analysis model is obtained by training an initial analysis model according to a training sample set, and the analysis result includes at least one of the following: service subscription quantity trend of each target container, subscription service call frequency trend of each target container, and performance deviation degree of each target container; judging whether the analysis result is abnormal, if the analysis result is abnormal, generating a warning prompt information, and sending the warning prompt information to a target object, wherein the target object implements system performance guarantee of the distributed system based on the warning prompt information.

[0006] Further, in the case that the analysis result comprises the service subscription quantity trend of each target container, the subscription service call frequency trend of each target container, and the performance deviation of each target container, the performance data of each target container is analyzed by using the target analysis model to obtain the analysis result, which comprises: the service subscription quantity trend of each target container is obtained by analyzing the service subscription quantity in the performance data of each target container by using the target analysis model; the subscription service call frequency trend of each target container is obtained by analyzing the subscription service call frequency in the performance data of each target container by using the target analysis model; the performance deviation of each target container is obtained by analyzing the performance data other than the service subscription quantity and the subscription service call frequency in the performance data of each target container by using the target analysis model; and the analysis result is composed of the service subscription quantity trend of each target container, the subscription service call frequency trend of each target container, and the performance deviation of each target container.

[0007] Further, the determination of whether the analysis result is abnormal comprises: comparing the analysis result with a preset threshold to obtain a comparison result; if the comparison result is that the analysis result is greater than the preset threshold, it is determined that the analysis result is abnormal; and if the comparison result is that the analysis result is less than or equal to the preset threshold, it is determined that the analysis result is not abnormal.

[0008] Further, before the performance data of each target container in the distributed system is obtained by the target server based on the preset time, the method further comprises: configuring a container performance monitoring rule and container information of the target container, wherein the container performance monitoring rule is used to define the collection strategy of the performance data of the target container; and pushing the container performance monitoring rule to the target container according to the container information.

[0009] Further, after the container performance monitoring rule is pushed to the target container according to the container information, the method further comprises: updating the configuration file of the target container according to the container performance monitoring rule by the target container to enable the performance monitoring function; and after the performance monitoring function is enabled, the running state of the target container is monitored in real time, the performance data of the target container is collected, and the performance data of the target container is uploaded to the preset database.

[0010] Further, after the analysis result is obtained, the method further comprises: generating the performance information view corresponding to each target container according to the analysis result, and displaying the performance information view.

[0011] Further, the target analysis model is generated by the following steps: obtaining historical performance data of a plurality of sample containers, performing data preprocessing on the historical performance data to obtain preprocessed data, wherein the data preprocessing at least includes data cleaning, data standardization and feature engineering; determining a plurality of target feature data from the preprocessed data, and grouping the plurality of target feature data to form a training sample set, wherein the plurality of target feature data at least includes service subscription quantity change rate, service call frequency change rate and performance index deviation; and training an initial analysis model by using the training sample set until a preset condition is met, to obtain the target analysis model.

[0012] To achieve the above object, according to another aspect of the present application, a system performance guarantee device of a distributed system is provided. The device comprises: an acquisition unit configured to acquire performance data of each target container in the distributed system based on a preset time point by a target server, wherein the target container is configured to implement at least one business scenario of the distributed system; an analysis unit configured to analyze the performance data of each target container by using a target analysis model to obtain an analysis result, wherein the target analysis model is obtained by training an initial analysis model based on a training sample set, and the analysis result comprises at least one of the following: service subscription quantity trend of each target container, subscription service call frequency trend of each target container, and performance deviation of each target container; and a processing unit configured to determine whether the analysis result is abnormal, and if the analysis result is abnormal, generate a warning prompt information and send the warning prompt information to a target object, wherein the target object implements system performance guarantee of the distributed system based on the warning prompt information.

[0013] Further, in the case that the analysis result comprises the service subscription quantity trend of each target container, the subscription service call frequency trend of each target container and the performance deviation of each target container, the analysis unit comprises: a first analysis subunit configured to analyze the service subscription quantity in the performance data of each target container by using the target analysis model to obtain the service subscription quantity trend of each target container; a second analysis subunit configured to analyze the subscription service call frequency in the performance data of each target container by using the target analysis model to obtain the subscription service call frequency trend of each target container; a third analysis subunit configured to analyze the performance data other than the service subscription quantity and the subscription service call frequency in the performance data of each target container by using the target analysis model to obtain the performance deviation of each target container; and a first determination subunit configured to group the service subscription quantity trend of each target container, the subscription service call frequency trend of each target container and the performance deviation of each target container to form the analysis result.

[0014] Further, the processing unit comprises: a first processing sub-unit, configured to compare the analysis result with a preset threshold to obtain a comparison result; a second determining sub-unit, configured to determine that the analysis result is abnormal if the comparison result is that the analysis result is greater than the preset threshold; and a third determining sub-unit, configured to determine that the analysis result is normal if the comparison result is that the analysis result is less than or equal to the preset threshold.

[0015] Further, the device further comprises: a configuration unit, configured to configure a container performance monitoring rule and container information of the target container before the target server acquires the performance data of each target container in the distributed system based on the preset time; and a sending unit, configured to push the container performance monitoring rule to the target container according to the container information.

[0016] Further, the device further comprises: an updating unit, configured to update a configuration file of the target container according to the container performance monitoring rule by the target container after the container performance monitoring rule is pushed to the target container according to the container information, so as to enable the performance monitoring function; and an acquisition unit, configured to monitor a running state of the target container in real time and acquire the performance data of the target container after the performance monitoring function is enabled, and upload the performance data of the target container to a preset database.

[0017] Further, the device further comprises: a display unit, configured to generate a performance information view corresponding to each target container according to the analysis result after the analysis result is obtained, and display the performance information view.

[0018] Further, the device further comprises the following units for generating the target analysis model by the following steps: a first training unit, configured to acquire historical performance data of a plurality of sample containers, and perform data preprocessing on the historical performance data to obtain preprocessed data, wherein the data preprocessing at least includes data cleaning, data standardization and feature engineering; a second training unit, configured to determine a plurality of target feature data from the preprocessed data, and compose a training sample set according to the plurality of target feature data, wherein the plurality of target feature data at least includes a service subscription quantity change rate, a service call quantity change rate and a deviation degree of a performance index; and a third training unit, configured to train an initial analysis model by using the training sample set until a preset condition is met, to obtain the target analysis model.

[0019] According to another aspect of the embodiment of the present application, an electronic device is also provided, comprising: a memory storing an executable program; and a processor configured to run the program, wherein the program is configured to execute the system performance guarantee method of the distributed system according to any one of the above aspects when running.

[0020] According to another aspect of the embodiments of the present application, a computer readable storage medium is also provided, the storage medium storing a program, wherein the program controls a device where the storage medium is located to perform the system performance guarantee method of the distributed system according to any one of the above aspects when the program is executed.

[0021] In the embodiments of the present application, the following steps are adopted: obtaining, by a target server, performance data of each target container in a distributed system based on a preset time point, wherein the target container is used to implement at least one business scenario of the distributed system; performing analysis on the performance data of each target container by using a target analysis model to obtain an analysis result, wherein the target analysis model is obtained by training an initial analysis model based on a training sample set, and the analysis result includes at least one of the following: a service subscription quantity trend of each target container, a subscription service call quantity trend of each target container, and a performance deviation degree of each target container; determining whether the analysis result is abnormal, and if the analysis result is abnormal, generating a warning prompt information and sending the warning prompt information to a target object, wherein the target object implements system performance guarantee for the distributed system based on the warning prompt information. The technical problem of low efficiency in the related art that the performance hidden danger of the distributed system is analyzed by manual analysis is solved.

[0022] In the present solution, the performance data of each container in the distributed environment is comprehensively monitored, the performance data of each target container in the distributed system is obtained and intelligent analysis is performed by using the target analysis model, the analysis efficiency and accuracy are improved, the performance hidden danger such as invalid service subscription can be found in time, and the reliability and stability of the container are improved, so that the system performance of the distributed system can be guaranteed. BRIEF DESCRIPTION OF DRAWINGS

[0023] The accompanying drawings, which form a part of the present application, are intended to provide further understanding of the present application, and the illustrative embodiments of the present application and their description serve the purpose of explaining the present application. In the drawings:

[0024] Figure 1 Fig. 1 shows a hardware structure block diagram of a computer terminal for implementing a system performance guarantee method of a distributed system;

[0025] Figure 2 Fig. 2 is a flowchart of a system performance guarantee method of a distributed system according to an embodiment of the present application;

[0026] Figure 3 Fig. 3 is a schematic diagram of a system performance guarantee system of a distributed system according to an embodiment of the present application;

[0027] Figure 4 Fig. 4 is a structure schematic diagram of a container performance analysis processing server according to an embodiment of the present application;

[0028] Figure 5 is a processing flow schematic diagram of a system performance guarantee system of a distributed system according to an embodiment of the present application;

[0029] Figure 6 is a schematic diagram of a system performance guarantee device of a distributed system according to an embodiment of the present application;

[0030] Figure 7 is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to enable persons skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0032] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product, or device that includes a series of steps or units does not necessarily have to be limited to only those steps or units clearly listed, but can include other steps or units that are not clearly listed or inherent to the process, method, product, or device.

[0033] It should be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, analyzed data, etc.) involved in the present application are information and data authorized by the user or authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure, and application of related data comply with relevant laws, regulations, and standards, take necessary security measures, do not violate public order and good customs, and provide corresponding operation portals for users to choose authorization or refusal. For example, the system and related users or institutions are provided with an interface to provide corresponding operation portals for users to choose to agree or refuse automatic decision results; if the user chooses to refuse, the expert decision process is entered.

[0034] Embodiment 1

[0035] According to the embodiments of the present application, a method for guaranteeing system performance of a distributed system is also provided. It should be noted that the steps shown in the flowchart can be executed in a computer system such as a set of computer executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0036] The method provided by the embodiment one of the present application can be executed in a mobile terminal, a computer terminal or similar computing device. Figure 1 A hardware structure block diagram of a computer terminal (or mobile device) for implementing the method for guaranteeing system performance of a distributed system is shown. As shown in the figure, Figure 1 the computer terminal 10 (or mobile device) can include one or more processors 102 (the processor 102 can include but not limited to a microprocessor MCU or a programmable logic device FPGA processing device), a memory 104 for storing data, and a transmission device 106 for communication function. In addition, it can also include a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which can be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. Those skilled in the art can understand that, Figure 1 the structure shown in the figure is only schematic, and it does not limit the structure of the above-mentioned electronic device. For example, the computer terminal 10 can include more or less components than those shown in the figure, or have a different configuration from that shown in the figure. Figure 1 Figure 1

[0037] It should be noted that the one or more processors 102 and / or other data processing circuits described above can be referred to as "data processing circuits" herein. The data processing circuit can be embodied in whole or in part as software, hardware, firmware or any combination thereof. In addition, the data processing circuit can be a single independent processing module, or any one of the other elements combined into the computer terminal 10 (or mobile device) in whole or in part. As referred to in the embodiments of the present application, the data processing circuit serves as a processor control (for example, selection of variable resistance terminal path connected with the interface).

[0038] ​​The memory 104 can be used to store software programs of application software and modules, such as program instructions / data storage devices corresponding to the system performance guarantee method of the distributed system in the embodiments of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implements the system performance guarantee method of the distributed system as described above. The memory 104 can include a high-speed random access memory, and can also include a non-volatile memory, such as one or more magnetic storage devices, flash memories, or other non-volatile solid-state memories. In some examples, the memory 104 can further include memories remotely arranged with respect to the processor 102, which can be connected to the computer terminal 10 through a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0039] The transmission device 106 is used to receive or send data via a network. Specific examples of the above-mentioned network can include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to be able to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, RF) module, which is used to communicate with the Internet in a wireless manner.

[0040] The display can be a touch screen type liquid crystal display (LCD), which can enable a user to interact with the user interface of the computer terminal 10 (or a mobile device).

[0041] In the above-mentioned operating environment, the present application provides a system performance guarantee method of a distributed system as shown in Figure 2 Figure 2 is a flowchart of the system performance guarantee method of the distributed system according to Embodiment One of the present application. The system performance guarantee method of the distributed system includes:

[0042] In step S201, the performance data of each target container in the distributed system is obtained by the target server based on a preset time, wherein the target container is used to implement at least one business scenario of the distributed system.

[0043] Optionally, Figure 3 is a schematic diagram of a system performance guarantee system of a distributed system according to the embodiments of the present application, as shown in Figure 3 The system includes a service composition layer container 1, a service provider container 2, a service composition layer database 3, a container performance analysis processing server 4, and a container performance analysis database 5.

[0044] ​The service combination layer container 1 refers to a container running a business process processing program of a certain specific business scenario by combining the business component services provided by other containers through service subscription, service calling and the like. The container intercepts performance data through the basic framework and stores the performance data in the service combination layer database 3. The service combination layer container can be a service provider container at the same time.

[0045] The service provider container 2 refers to a container running a function program that can be called by an external service provider according to a business processing logic to complete data processing of a certain business object. The service provider container can be a service combination layer container at the same time.

[0046] The service combination layer database 3 is used to store system data, technical parameters and business data required in the running process of each function module of the service combination layer container 1, and store container performance rule configuration parameters, container performance data and the like.

[0047] The container performance analysis processing server 4 refers to an application server used to store configuration performance monitoring rules, acquire, diagnose, warn and display the performance data of each service combination layer container. Figure 4 The structure of the container performance analysis processing server provided by the embodiment of the application is shown in FIG. 4. Figure 4 As shown in FIG. 4, the container performance analysis processing server includes a rule configuration module, a data acquisition module, a data diagnosis module, a warning and display module.

[0048] The container performance analysis database 5 is used to store configuration data generated by the rule configuration module of the container performance analysis processing server 4, each container performance data acquired by the data acquisition module, and performance view data generated by the data diagnosis module.

[0049] Optionally, the target server can be the container performance analysis processing server described above, and the target container can be the service combination layer container described above. The container performance analysis processing server acquires the performance data of the service combination layer container from the service combination layer database based on a preset time. For example, the data acquisition module of the container performance analysis processing server acquires the performance data intercepted by the service combination layer container from the service combination layer database at the end of each day, such as the daily service subscription quantity and service calling frequency data of all containers, and then aggregates the data and stores the data in the container performance analysis database.

[0050] In step S202, the performance data of each target container is analyzed by using a target analysis model to obtain an analysis result, wherein the target analysis model is obtained by training an initial analysis model according to a training sample set, and the analysis result includes at least one of the following: a service subscription quantity trend of each target container, a subscription service calling frequency trend of each target container, and a performance deviation degree of each target container.

[0051] Optionally, the target analysis model can be a container performance analysis large model, and the analysis result can be service subscription quantity trend of each container, service call frequency trend of each container, and performance deviation of each container. The deviation is used to represent the difference between actual running performance and expected or historical baseline performance. For example, service response time deviation, central processing unit usage rate deviation, etc.

[0052] Optionally, the data diagnosis module diagnoses the performance data of each container by the container performance analysis large model based on the performance data collected by the data collection module, identifies the performance hidden danger of the container, and generates performance view data and stores it in the container performance analysis database. For example, the data diagnosis module finds that the service subscription quantity of a container has abnormally increased in the past week, or the call frequency of a service has been 0 for a long time, which are signals of performance hidden danger.

[0053] In step S203, it is determined whether the analysis result is abnormal. If the analysis result is abnormal, a warning prompt information is generated, and the warning prompt information is sent to a target object. The target object realizes system performance guarantee of the distributed system based on the warning prompt information.

[0054] Optionally, the data diagnosis module determines whether there is a performance hidden danger, such as service subscription quantity surge and service call frequency anomaly, based on the result of the container performance analysis large model. For example, the data diagnosis module finds that the service subscription quantity of a container in the aggregation acquirer service combination layer has suddenly increased in the past week and exceeds the warning threshold, which is determined to be abnormal. The warning and display module generates a warning prompt information, such as notifying the relevant operation and maintenance personnel through an instant messaging tool or an email, and generates a container performance view for display. If no abnormality is found, the routine monitoring state is returned, and the next data summarization and analysis is waited.

[0055] In summary, the performance data of each container in the distributed environment is comprehensively monitored, the performance data of each target container in the distributed system is obtained, and intelligent analysis is performed through the target analysis model, which improves the analysis efficiency and accuracy, can timely find performance hidden dangers such as invalid service subscription, improves the reliability and stability of the container, and thus guarantees the system performance of the distributed system.

[0056] Optionally, in the system performance guarantee method of the distributed system provided in the embodiments of the present application, in the case that the analysis result includes the service subscription quantity trend of each target container, the subscription service call frequency trend of each target container, and the performance deviation degree of each target container, the performance data of each target container is analyzed by using the target analysis model to obtain the analysis result, including: the service subscription quantity in the performance data of each target container is analyzed by using the target analysis model to obtain the service subscription quantity trend of each target container; the subscription service call frequency in the performance data of each target container is analyzed by using the target analysis model to obtain the subscription service call frequency trend of each target container; the performance data of each target container except the service subscription quantity and the subscription service call frequency is analyzed by using the target analysis model to obtain the performance deviation degree of each target container; and the analysis result is composed of the service subscription quantity trend of each target container, the subscription service call frequency trend of each target container, and the performance deviation degree of each target container.

[0057] In an optional embodiment, by analyzing the service subscription quantity in the performance data of each target container by using the container performance analysis large model, the service subscription quantity trend of each target container can be obtained, for example, the change of the service subscription quantity of each target container in a certain period (such as day, week, month) is analyzed, by comparing the historical data and the current data, the increasing or decreasing trend of the subscription quantity and the possible abnormal fluctuation are identified. By analyzing the subscription service call frequency in the performance data of each target container by using the container performance analysis large model, the subscription service call frequency trend of each target container can be obtained, for example, based on the calling frequency of each target container to the subscribed service, the average calling frequency in a period (such as every hour, every day) is calculated, and compared with the historical baseline, the rising or falling trend of the calling frequency and the potential zero calling service are identified. By analyzing the performance data of each target container except the service subscription quantity and the subscription service call frequency by using the container performance analysis large model, the performance deviation degree of each target container can be obtained, for example, the difference between the performance data (such as central processing unit usage rate, memory occupancy rate, network delay, etc.) of each target container and its expected performance or historical average performance is evaluated to identify the performance degradation.

[0058] By using the container performance analysis large model for deep analysis, potential problems such as performance bottleneck and abnormal service dependency relationship can be identified, and the analysis efficiency and accuracy are improved.

[0059] Optionally, in the method for guaranteeing system performance of a distributed system provided in the embodiments of the present application, the step of determining whether the analysis result is abnormal includes: comparing the analysis result with a preset threshold to obtain a comparison result; if the comparison result is that the analysis result is greater than the preset threshold, it is determined that the analysis result is abnormal; and if the comparison result is that the analysis result is less than or equal to the preset threshold, it is determined that the analysis result is not abnormal.

[0060] In an optional embodiment, the data diagnosis module compares the analysis result with a preset threshold, and if the analysis result is greater than the preset threshold, it is determined that the analysis result is abnormal, and if the analysis result is less than or equal to the preset threshold, it is determined that the analysis result is not abnormal. For example, based on historical data and business requirements, an abnormal threshold is set for each performance indicator of the target container, such as that the number of service subscriptions increases by more than a certain percentage, the number of service calls decreases significantly compared with the historical average, the performance deviation exceeds the preset range, etc. The analysis result obtained by the container performance analysis large model is compared with the set abnormal threshold to check whether there is a performance indicator that exceeds the threshold.

[0061] By comparing the analysis result with the preset threshold, the abnormal situation in the analysis result can be effectively determined and recognized, so that measures can be taken in time to prevent or solve the performance problem, and the stable operation of the distributed system is ensured.

[0062] Optionally, in the method for guaranteeing system performance of a distributed system provided in the embodiments of the present application, before the performance data of each target container in the distributed system is acquired by the target server based on the preset time, the method further includes: configuring a container performance monitoring rule and container information of the target container, wherein the container performance monitoring rule is used to define the collection strategy of the performance data of the target container; and pushing the container performance monitoring rule to the target container according to the container information.

[0063] In an optional embodiment, the container performance monitoring rule and the container information of the target container are configured first, and the container performance monitoring rule is pushed to the target container according to the container information. For example, the operation and maintenance personnel can set the corresponding container performance monitoring rule through the visual configuration interface provided by the rule configuration module of the container performance analysis processing server according to the specific situation of the application system, including the service combination layer container information to be monitored, the key indicators and rules of performance monitoring, the pre-warning information receiver, and the like. The rule configuration module distributes the performance monitoring rule to the service combination layer database connected to each service combination layer container that needs performance guarantee according to the container information, so as to ensure that each container can load and execute these monitoring rules when starting. Taking a bank acquirer business system as an example, there are 10 containers in the aggregated acquirer service combination layer and 6 containers in the non-aggregated acquirer service combination layer. The operation and maintenance personnel import the corresponding container address information and grouping information, associate the configured performance monitoring rule with the corresponding grouping to complete the configuration, for example, the performance monitoring rule is set as: the service subscription quantity exceeding 5000 records is regarded as an exception, and the subscription service with 0 service call times in the past week is regarded as invalid. The rule configuration module automatically synchronizes the monitoring rule information to each service combination layer database, and the 10 aggregated acquirer containers and the 6 non-aggregated acquirer containers automatically load these rules when starting, and start to monitor the service subscription and call performance in real time.

[0064] It should be noted that the configurable performance monitoring rule provides support for flexible expansion of the function of the performance guarantee system, and can realize unified opening and closing of each container and each performance monitoring rule, thereby improving the scalability of the system.

[0065] Optionally, in the system performance guarantee method of the distributed system provided in the embodiments of the present application, after the container performance monitoring rule is pushed to the target container according to the container information, the method further includes: updating the configuration file of the target container according to the container performance monitoring rule through the target container to enable the performance monitoring function; after the performance monitoring function is enabled, the running state of the target container is monitored in real time, the performance data of the target container is collected, and the performance data of the target container is uploaded to the preset database.

[0066] In an optional embodiment, the configuration file of the service combination layer container is updated by the service combination layer container according to the container performance monitoring rule to enable the performance monitoring function; after the performance monitoring function is enabled, the running state of the service combination layer container is monitored in real time, and the performance data of the service combination layer container is collected and uploaded to the service combination layer database (i.e., the preset database). For example, the service combination layer container loads and executes the performance monitoring rule in the service combination layer database when starting, and in the running process, the performance data is intercepted and stored according to the performance monitoring rule, such as the number of service subscriptions, the number of service calls, etc. Taking the aggregation acquirer container as an example, the container can realize the interception of service subscription data through the post-processor provided by the middleware or framework, record the call frequency of the related service, and collect the central processing unit usage rate, memory occupation and other key performance indicators during the running of the container, and store them in the service combination layer database.

[0067] Through the above process, the performance monitoring rule can take effect in the target container in real time, realize the continuous monitoring of the running state of the container and the accurate collection of the performance data, and thus provide real-time data support for performance analysis and early warning.

[0068] Optionally, in the system performance guarantee method of the distributed system provided in the embodiments of the present application, after the analysis result is obtained, the method further includes: generating a performance information view corresponding to each target container according to the analysis result, and displaying the performance information view.

[0069] In an optional embodiment, the container performance analysis processing server can generate a performance information view corresponding to each target container according to the analysis result, and display the performance information view. For example, the data diagnosis module generates a performance information view corresponding to each service combination layer container according to the analysis result, and the early warning and display module displays the performance information view in the form of images, statistical data, etc. according to the dimensions of container grouping, performance monitoring rule, etc.

[0070] By displaying the performance information view, the abnormal container can be located by the operation and maintenance personnel, thereby improving the operation and maintenance efficiency and the system stability.

[0071] Optionally, in the method for guaranteeing system performance of a distributed system provided in the embodiments of the present application, the target analysis model is generated by the following steps: obtaining historical performance data of a plurality of sample containers, performing data preprocessing on the historical performance data to obtain preprocessed data, wherein the data preprocessing at least includes data cleaning, data standardization, and feature engineering; determining a plurality of target feature data from the preprocessed data, and forming a training sample set according to the plurality of target feature data, wherein the plurality of target feature data at least includes service subscription quantity change rate, service call frequency change rate, and performance index deviation; and training an initial analysis model through the training sample set until a preset condition is met, to obtain the target analysis model.

[0072] In an optional embodiment, the historical performance data of a plurality of sample containers is obtained, for example, the historical performance data including service subscription quantity, service call frequency, central processing unit usage rate, memory usage, network bandwidth usage, and the like is collected. The historical performance data is preprocessed, including data cleaning (removing invalid or erroneous data), data standardization (ensuring that different indicators are in the same order of magnitude), and feature engineering (extracting or constructing features related to performance analysis from the original data), to obtain preprocessed data. Then, feature selection is performed to determine a plurality of target feature data from the preprocessed data, for example, based on the preprocessed data, statistical analysis, correlation analysis, and the like are used to select the features that have the greatest impact on container performance, such as service subscription quantity change rate, average value and standard deviation of service call frequency, and performance index deviation. Then, a training sample set is formed according to the plurality of target feature data, and an initial analysis model is trained through the training sample set until a preset condition is met, to obtain the target analysis model. For example, according to the selected features and performance monitoring requirements, an initial analysis model is designed, such as a supervised learning model, an unsupervised learning model, a time series analysis model, or a deep learning model, and the model is trained using a historical data set. The trained and optimized model is deployed to a container performance analysis processing server and integrated into a data diagnosis module to realize automatic performance analysis and anomaly detection.

[0073] By constructing the container performance analysis large model, the container performance data can be automatically and accurately analyzed, and abnormal conditions can be timely discovered, thereby effectively guaranteeing the performance and stability of the distributed system.

[0074] In an optional embodiment, Figure 5 is a processing flow diagram of a system for guaranteeing system performance of a distributed system provided in the embodiments of the present application, as Figure 5 shown, mainly includes the following steps:

[0075] Step 501, set container information configuration and performance monitoring rule configuration. Set the required guaranteed container information, and group all containers on the same node; configure performance monitoring rules to identify various performance monitoring through rule names, support setting different performance monitoring rules according to different container groups, and control whether to execute each container performance monitoring data collection through a unified switch on the server; configure the monitoring rule early warning receiver.

[0076] Step 502, push performance monitoring rules. Push the set performance monitoring rules to the containers of the specified group, and the monitoring rules take effect in real time after being pushed.

[0077] Step 503, intercept container performance data. The container with the set monitoring rule realizes performance data interception, for example, the aggregation acquirer service combination layer container of the bank acquirer business system realizes the interception of service subscription data through the post-processor of the basic framework, and writes the data to the service combination layer database.

[0078] Step 504, obtain each container performance data at the end of the day. The container performance analysis processing server collects each container performance data from the service combination layer database every day, and stores it in the container performance analysis database.

[0079] Step 505, call the container performance analysis large model to analyze the performance data. Analyze the service subscription quantity trend, service call frequency trend, and various deviation degrees of each container.

[0080] Step 506, determine whether there is a performance hidden danger. Determine whether the trend and deviation obtained by analysis reach the early warning index, for example, the service subscription quantity of a certain container increases sharply, the service call frequency is significantly higher than that of other containers, and the service call frequency of a certain container is 0 for a long time.

[0081] Step 507, generate and push performance early warning. In the case where the performance analysis data of a certain container reaches the early warning index, notify the preset monitoring rule early warning receiver through instant messaging tools, emails and the like.

[0082] Step 508, generate a container performance view. Generate summary statistical reports according to the container grouping, rule and other dimensions, and display them through a data visualization panel.

[0083] The distributed system performance guarantee method provided by the embodiment of the application comprehensively monitors the performance data of each container in the distributed environment, intelligently analyzes the performance data of each target container in the distributed system through a target analysis model, improves the analysis efficiency and accuracy, can timely discover performance hidden dangers such as invalid service subscription, improves the reliability and stability of the container, and thus can guarantee the system performance of the distributed system.

[0084] It is noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0085] Embodiment 2

[0086] The embodiment of the present application also provides a system performance guarantee device of a distributed system. It is noted that the system performance guarantee device of the distributed system of the embodiment of the present application can be used to execute the system performance guarantee method for the distributed system provided by the embodiment of the present application. The system performance guarantee device of the distributed system provided by the embodiment of the present application is introduced as follows.

[0087] According to the embodiment of the present application, a system performance guarantee device of a distributed system for implementing the system performance guarantee method of the distributed system is also provided, as shown in Figure 6 The device includes an acquisition unit 601, an analysis unit 602, and a processing unit 603.

[0088] The acquisition unit 601 is configured to acquire, by a target server, performance data of each target container in the distributed system based on a preset time point, wherein the target container is used to implement at least one business scenario of the distributed system.

[0089] The analysis unit 602 is configured to analyze the performance data of each target container by using a target analysis model to obtain an analysis result, wherein the target analysis model is obtained by training an initial analysis model based on a training sample set, and the analysis result includes at least one of the following: a service subscription quantity trend of each target container, a subscription service call frequency trend of each target container, and a performance deviation degree of each target container.

[0090] The processing unit 603 is configured to determine whether the analysis result is abnormal, and if the analysis result is abnormal, generate a warning prompt information and send the warning prompt information to a target object, wherein the target object implements system performance guarantee for the distributed system based on the warning prompt information.

[0091] The system performance guarantee device of the distributed system provided in the embodiments of the present application comprises: an obtaining unit 601, which obtains performance data of each target container in the distributed system based on a preset time point by a target server, wherein the target container is used to implement at least one business scenario of the distributed system; an analysis unit 602, which analyzes the performance data of each target container by using a target analysis model to obtain an analysis result, wherein the target analysis model is obtained by training an initial analysis model according to a training sample set, and the analysis result comprises at least one of the following: a service subscription quantity trend of each target container, a subscription service call frequency trend of each target container, and a performance deviation degree of each target container; and a processing unit 603, which judges whether the analysis result is abnormal, generates a warning prompt information if the analysis result is abnormal, and sends the warning prompt information to a target object, wherein the target object implements system performance guarantee of the distributed system based on the warning prompt information. In the distributed environment, the performance data of each container is comprehensively monitored, the performance data of each target container in the distributed system is obtained and intelligently analyzed by the target analysis model, the analysis efficiency and accuracy are improved, performance hidden dangers such as invalid service subscription can be found in time, and the reliability and stability of the container are improved, so as to guarantee the system performance of the distributed system.

[0092] Optionally, in the system performance guarantee device of the distributed system provided in the embodiments of the present application, in the case where the analysis result comprises the service subscription quantity trend of each target container, the subscription service call frequency trend of each target container, and the performance deviation degree of each target container, the analysis unit 602 comprises: a first analysis subunit, which analyzes the service subscription quantity in the performance data of each target container by using the target analysis model to obtain the service subscription quantity trend of each target container; a second analysis subunit, which analyzes the subscription service call frequency in the performance data of each target container by using the target analysis model to obtain the subscription service call frequency trend of each target container; a third analysis subunit, which analyzes the performance data other than the service subscription quantity and the subscription service call frequency in the performance data of each target container by using the target analysis model to obtain the performance deviation degree of each target container; and a first determination subunit, which comprises the analysis result according to the service subscription quantity trend of each target container, the subscription service call frequency trend of each target container, and the performance deviation degree of each target container.

[0093] Optionally, in the system performance guarantee device of the distributed system provided in the embodiments of the present application, the processing unit 603 comprises: a first processing subunit, which compares the analysis result with a preset threshold to obtain a comparison result; a second determination subunit, which determines that the analysis result is abnormal if the comparison result is that the analysis result is greater than the preset threshold; and a third determination subunit, which determines that the analysis result is not abnormal if the comparison result is that the analysis result is less than or equal to the preset threshold.

[0094] Optionally, in the system performance guarantee apparatus of the distributed system provided by the embodiment of the present application, the apparatus further comprises: a configuration unit, configured to configure a container performance monitoring rule and container information of the target container before the target server acquires the performance data of each target container in the distributed system based on the preset time, wherein the container performance monitoring rule is used to define an acquisition strategy for the performance data of the target container; and a sending unit, configured to push the container performance monitoring rule to the target container according to the container information.

[0095] Optionally, in the system performance guarantee apparatus of the distributed system provided by the embodiment of the present application, the apparatus further comprises: an updating unit, configured to update a configuration file of the target container according to the container performance monitoring rule by the target container after the container performance monitoring rule is pushed to the target container according to the container information, so as to enable the performance monitoring function; and an acquisition unit, configured to monitor a running state of the target container in real time and acquire the performance data of the target container after the performance monitoring function is enabled, and upload the performance data of the target container to a preset database.

[0096] Optionally, in the system performance guarantee apparatus of the distributed system provided by the embodiment of the present application, the apparatus further comprises: a display unit, configured to generate a performance information view corresponding to each target container according to the analysis result after the analysis result is obtained, and display the performance information view.

[0097] Optionally, in the system performance guarantee apparatus of the distributed system provided by the embodiment of the present application, the apparatus further comprises the following units configured to generate the target analysis model by the following steps: a first training unit, configured to acquire historical performance data of a plurality of sample containers, perform data preprocessing on the historical performance data to obtain preprocessed data, wherein the data preprocessing at least includes data cleaning, data standardization and feature engineering; a second training unit, configured to determine a plurality of target feature data from the preprocessed data, and compose a training sample set according to the plurality of target feature data, wherein the plurality of target feature data at least includes a service subscription quantity change rate, a service call frequency change rate and a performance index deviation; and a third training unit, configured to train an initial analysis model through the training sample set until a preset condition is met, to obtain the target analysis model.

[0098] It should be noted that the acquisition unit 601, analysis unit 602, and processing unit 603 mentioned above correspond to steps S201 to S203 in Embodiment 1. The three units and their corresponding steps implement the same instances and application scenarios, but are not limited to the content disclosed in Embodiment 1. It should be noted that the above modules or units can be hardware or software components stored in memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above units can also be part of a device and run in the computer terminal 10 provided in Embodiment 1.

[0099] Example 3

[0100] Embodiments of this application may provide an electronic device. Figure 7 This is a structural block diagram of an electronic device according to an embodiment of this application. Figure 7 As shown, the electronic device may include: one or more ( Figure 7 (Only one is shown) processor 702, memory 704, memory controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0101] The memory can be used to store software programs and modules, such as the program instructions / modules corresponding to the methods and apparatus in the embodiments of this application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, thereby implementing the above-described methods. The memory may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include memory remotely located relative to the processor, and these remote memories can be connected to the terminal via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0102] The processor can call information and application programs stored in the memory through the transmission device to perform the following steps: obtaining performance data of each target container in the distributed system based on a preset time point through the target server, wherein the target container is used to implement at least one business scenario of the distributed system; analyzing the performance data of each target container by using a target analysis model to obtain an analysis result, wherein the target analysis model is obtained by training an initial analysis model according to a training sample set, and the analysis result includes at least one of the following: service subscription quantity trend of each target container, subscription service call frequency trend of each target container, and performance deviation of each target container; determining whether the analysis result is abnormal, and if the analysis result is abnormal, generating a warning prompt information and sending the warning prompt information to a target object, wherein the target object implements system performance guarantee of the distributed system based on the warning prompt information.

[0103] The processor can call information and application programs stored in the memory through the transmission device to perform the following steps: analyzing the service subscription quantity in the performance data of each target container by using the target analysis model to obtain the service subscription quantity trend of each target container; analyzing the subscription service call frequency in the performance data of each target container by using the target analysis model to obtain the subscription service call frequency trend of each target container; analyzing the performance data other than the service subscription quantity and the subscription service call frequency in the performance data of each target container by using the target analysis model to obtain the performance deviation of each target container; and composing the analysis result according to the service subscription quantity trend of each target container, the subscription service call frequency trend of each target container, and the performance deviation of each target container.

[0104] The processor can call information and application programs stored in the memory through the transmission device to perform the following steps: comparing the analysis result with a preset threshold to obtain a comparison result; if the comparison result is that the analysis result is greater than the preset threshold, it is determined that the analysis result is abnormal; and if the comparison result is that the analysis result is less than or equal to the preset threshold, it is determined that the analysis result is not abnormal.

[0105] The processor can call information and application programs stored in the memory through the transmission device to perform the following steps: before obtaining the performance data of each target container in the distributed system based on a preset time point through the target server, configuring a container performance monitoring rule and container information of the target container, wherein the container performance monitoring rule is used to define the collection strategy of the performance data of the target container; and pushing the container performance monitoring rule to the target container according to the container information.

[0106] The processor can call information and application programs stored in the memory through the transmission device to perform the following steps: after pushing the container performance monitoring rule to the target container according to the container information, updating the configuration file of the target container according to the container performance monitoring rule through the target container to enable the performance monitoring function; after enabling the performance monitoring function, monitoring the running state of the target container in real time, collecting the performance data of the target container, and uploading the performance data of the target container to the preset database.

[0107] The processor can call information and application programs stored in the memory through the transmission device to perform the following steps: after obtaining the analysis result, generating a performance information view corresponding to each target container according to the analysis result, and displaying the performance information view.

[0108] The processor can call information and application programs stored in the memory through the transmission device to perform the following steps: obtaining historical performance data of a plurality of sample containers, performing data preprocessing on the historical performance data to obtain preprocessed data, wherein the data preprocessing at least includes data cleaning, data standardization and feature engineering; determining a plurality of target feature data from the preprocessed data, and grouping the plurality of target feature data to form a training sample set, wherein the plurality of target feature data at least includes service subscription quantity change rate, service call frequency change rate and performance index deviation; training an initial analysis model through the training sample set until a preset condition is met to obtain a target analysis model.

[0109] Those skilled in the art can understand that, Figure 7 The structure shown is only schematic, and the electronic device can also be a terminal device such as a smart phone, a tablet computer, a palm computer, a Mobile Internet Device (MID), a PAD, etc. Figure 7 It does not limit the structure of the above-mentioned electronic device. For example, the electronic device can include more or less components (such as a network interface, a display device, etc.) than those shown in the figure, or have a different configuration from that shown in the figure. Figure 7 It does not limit the structure of the above-mentioned electronic device. For example, the electronic device can include more or less components (such as a network interface, a display device, etc.) than those shown in the figure, or have a different configuration from that shown in the figure. Figure 7 It does not limit the structure of the above-mentioned electronic device. For example, the electronic device can include more or less components (such as a network interface, a display device, etc.) than those shown in the figure, or have a different configuration from that shown in the figure.

[0110] Those skilled in the art can understand that all or part of the steps in the various methods of the above-mentioned embodiments can be completed by instructing the relevant hardware of the terminal device through a program, and the program can be stored in a computer readable storage medium, which can include a flash disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, etc.

[0111] Embodiment 4

[0112] The embodiment of the present application further provides a computer readable storage medium. Optionally, in the embodiment, the storage medium can be used to save the program code executed by the system performance guarantee method of the distributed system provided in the first embodiment.

[0113] Optionally, in the embodiment, the storage medium can be located in any one of the computer terminals in the computer terminal group in the computer network, or in any one of the mobile terminals in the mobile terminal group.

[0114] The present application further provides a computer program product, which is adapted to execute the steps of the system performance guarantee method of the distributed system when executed on a data processing device.

[0115] The serial numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0116] In the above embodiments of the present application, the description of each embodiment has its own focus, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0117] In the several embodiments provided by the present application, it should be understood that the disclosed technology can be implemented in other ways. Of course, the unit described as the division is only a logical function division, and there can be another division way in actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual coupling or direct coupling or communication connection between the units can be indirect coupling or communication connection through some interface, unit or module, and can be electrical or other forms.

[0118] The units described as separate components can or can not be physically separate, and the components displayed as units can or can not be physical units, that is, they can be located in one place, or can be distributed on multiple network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiment.

[0119] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of software functional unit.

[0120] The integrated unit, if implemented in the form of a software function unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application essentially or say the part that contributes to the prior art or the whole or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a mobile hard disk, a magnetic disk or an optical disk, and various media that can store program codes.

[0121] The above is only the preferred embodiment of the present application, and it should be pointed out that for those skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can be made, which should be considered as the protection scope of the present application.

Claims

1. A method for ensuring the system performance of a distributed system, characterized in that, include: The target server acquires performance data of each target container in the distributed system at a preset time, wherein the target container is used to implement at least one business scenario of the distributed system; The performance data of each target container is analyzed using a target analysis model to obtain analysis results. The target analysis model is trained on an initial analysis model based on a training sample set. The analysis results include at least one of the following: the trend of the number of service subscriptions for each target container, the trend of the number of subscription service calls for each target container, and the performance deviation of each target container. If the analysis results are abnormal, an early warning message is generated and sent to the target object. The target object uses the early warning message to ensure the system performance of the distributed system.

2. The method according to claim 1, characterized in that, Given that the analysis results include the service subscription quantity trend, the subscription service call frequency trend, and the performance deviation of each target container, a target analysis model is used to analyze the performance data of each target container, and the analysis results include: The service subscription count in the performance data of each target container is analyzed using the target analysis model to obtain the trend of the service subscription count for each target container. The subscription service call count in the performance data of each target container is analyzed using the target analysis model to obtain the trend of the subscription service call count for each target container. The performance data of each target container, excluding the number of service subscriptions and the number of subscription service calls, is analyzed using the target analysis model to obtain the performance deviation of each target container. The analysis results are composed of the service subscription number trend, the subscription service call frequency trend, and the performance deviation of each target container.

3. The method according to claim 1, characterized in that, Determining whether the analysis results are abnormal includes: The analysis results are compared with a preset threshold to obtain a comparison result; If the comparison result is that the analysis result is greater than the preset threshold, then it is determined that the analysis result is abnormal; If the comparison result is that the analysis result is less than or equal to the preset threshold, then it is determined that the analysis result is not abnormal.

4. The method according to claim 1, characterized in that, Before obtaining performance data for each target container in the distributed system from the target server at a preset time, the method further includes: Configure container performance monitoring rules and container information of the target container, wherein the container performance monitoring rules are used to define the collection strategy for performance data of the target container; Based on the container information, the container performance monitoring rules are pushed to the target container.

5. The method according to claim 4, characterized in that, After pushing the container performance monitoring rules to the target container based on the container information, the method further includes: The target container's configuration file is updated according to the container performance monitoring rules to enable the performance monitoring function; After enabling the performance monitoring function, the running status of the target container is monitored in real time, and the performance data of the target container is collected and uploaded to a preset database.

6. The method according to claim 1, characterized in that, After obtaining the analysis results, the method further includes: Based on the analysis results, a performance information view corresponding to each target container is generated and displayed.

7. The method according to claim 1, characterized in that, The target analysis model is generated through the following steps: Historical performance data of multiple sample containers are obtained, and the historical performance data is preprocessed to obtain preprocessed data. The data preprocessing includes at least data cleaning, data standardization, and feature engineering. Multiple target feature data are determined from the preprocessed data, and the training sample set is formed based on the multiple target feature data, wherein the multiple target feature data includes at least the service subscription number change rate, the service call number change rate, and the deviation of performance indicators; The initial analysis model is trained using the training sample set until the preset conditions are met, thus obtaining the target analysis model.

8. A system performance assurance device for a distributed system, characterized in that, include: The acquisition unit is used to acquire performance data of each target container in the distributed system through the target server at a preset time, wherein the target container is used to implement at least one business scenario of the distributed system. The analysis unit is used to analyze the performance data of each target container using a target analysis model to obtain analysis results. The target analysis model is trained on an initial analysis model based on a training sample set. The analysis results include at least one of the following: the trend of the number of service subscriptions for each target container, the trend of the number of subscription service calls for each target container, and the performance deviation of each target container. The processing unit is used to determine whether there is an anomaly in the analysis result. If there is an anomaly in the analysis result, an early warning message is generated and sent to the target object. The target object uses the early warning message to ensure the system performance of the distributed system.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to perform the system performance assurance method for a distributed system according to any one of claims 1 to 7.

10. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program executes the system performance guarantee method for a distributed system according to any one of claims 1 to 7 when it runs.

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