Online service system capacity evaluation method and related equipment

Abstract

The invention discloses an online service system capacity evaluation method and device, a server and a computer readable storage medium. According to the method, the flow test is carried out through the two sub-clusters respectively deployed in different machine rooms, the single machine room can undertake the online flow through the online service double-machine-room deployment characteristic, if one or more services have capacity risks during the capacity assessment period, the flow can be quickly recovered to the double machine rooms, and the capacity assessment efficiency is improved. Therefore, online service product functions can be continuously provided for the user; besides, according to the method, the received user traffic is subjected to traffic configuration according to the traffic proportion and the traffic hierarchy, the traffic is guided to the corresponding sub-clusters according to the configuration, and the traffic configuration is performed by combining the traffic grading and the traffic proportion, so that the flexibility is high, and different test requirements in different application scenes can be met; in addition, the real flow is used for capacity evaluation, the problem that the simulation degree of test data is not high is solved, and the reliability of a capacity evaluation result is guaranteed.

Description

Technical Field

[0001] The present disclosure generally relates to the field of electronic technology, and in particular to an online service system capacity assessment method, device, server, and computer-readable storage medium. Background Art

[0002] In the Internet field, the underlying technologies of various apps used by users and web services visited are all implemented through the collaboration of multiple different services. In order to ensure that product functions can be provided to users continuously and uninterruptedly, to discover potential bottlenecks in advance, to avoid system crashes due to excessive load, and to reasonably evaluate capacity requirements, avoid over-allocation of resources, and reduce hardware and operation and maintenance costs, it is necessary to conduct capacity assessment of online service systems.

[0003] Currently, the technology of simulating online traffic or creating test data is often used to stress test online service systems. That is, through the service interface, interface access requests are simulated, and test traffic is created to stress test the service, thereby evaluating the capacity limit of the service.

[0004] For a single online service, it is effective to simulate traffic to conduct targeted stress testing on the interface. However, the system is a relationship of cross-combination and correlation requests of multiple online services. The system is complex. If the capacity of a single online service is abnormal, it will cause the business process to fail, resulting in asset loss and affecting user experience. In addition, when a single service is abnormal due to capacity problems and cannot process requests in time, it will cause the upstream service to trigger a retry, thereby amplifying local access traffic. In this scenario, this technology cannot cover it and cannot evaluate the capacity risk of the system. Moreover, the simulation degree of simulated traffic is not high, the sample quality is not comprehensive enough and there is a large gap with online traffic, which cannot reflect the actual capacity of business operations. Therefore, using simulated traffic is not enough to evaluate the capacity of the entire system. Capacity problems can only be handled in a lossy way, which takes a long time, and the loss is proportional to the stop loss time. Summary of the invention

[0005] In view of the above-mentioned defects or deficiencies in the prior art, it is desired to provide an online service system capacity assessment method, device, server and computer-readable storage medium, which realizes a true and effective assessment of the overall capacity of the system while also ensuring the continuity of the system's online service product functions.

[0006] In a first aspect, an embodiment of the present application provides a method for evaluating capacity of an online service system, comprising:

[0007] The BFE platform determines the current traffic configuration to be tested; wherein the traffic configuration includes: the traffic ratio and traffic level of two sub-clusters respectively deployed in different computer rooms;

[0008] After receiving the client request, according to the traffic ratio, calling the polling algorithm to direct the traffic to the sub-cluster according to the traffic level, calling the service corresponding to the sub-cluster to respond to the client request, and determining the service response quality;

[0009] A capacity assessment result is generated according to the service response quality under the traffic configuration.

[0010] In one embodiment, before determining the current traffic configuration to be tested, the method further includes:

[0011] Generate multiple traffic configurations from low to high traffic according to the evaluation strategy;

[0012] Accordingly, the determining of the current flow configuration to be tested includes: extracting the generated flow configurations in order from low to high flow rates as the current flow configuration to be tested;

[0013] Generating a capacity assessment result according to the service response quality under the traffic configuration includes: counting the service response quality under each of the traffic configurations, and generating a capacity assessment result according to the statistical result.

[0014] In one embodiment, generating a capacity assessment result according to the service response quality under the traffic configuration includes:

[0015] If the traffic configuration reaches the maximum configurable traffic of the sub-cluster and the service response is normal, it is determined that the sub-cluster can bear the traffic peak.

[0016] In one embodiment, it also includes:

[0017] Determine the business logic labels of each service in the sub-cluster that can carry the traffic peak;

[0018] Instances of the same type as the business logic tag are shielded in order, and the service response quality is determined.

[0019] In one embodiment, it also includes:

[0020] If the service response is abnormal, reduce the traffic according to the abnormal adjustment strategy.

[0021] In one embodiment, if the service response is abnormal, reducing the traffic according to the abnormal adjustment strategy includes:

[0022] If the traffic configuration does not reach the maximum configurable traffic of the subcluster and the service response is abnormal, the traffic ratio is rolled back to an equal ratio.

[0023] In one embodiment, if the service response is abnormal, reducing the traffic according to the abnormal adjustment strategy includes:

[0024] If the instances of the same type as the business logic tag are blocked in sequence, the service responds abnormally and the traffic ratio is reversed.

[0025] In a second aspect, an embodiment of the present application provides an online service system capacity assessment device, which is applied to a BFE platform. The device includes:

[0026] A configuration determination unit, used to determine the current traffic configuration to be tested; wherein the traffic configuration includes: the traffic ratio and traffic level of two sub-clusters respectively deployed in different computer rooms;

[0027] A traffic diversion unit, configured to, after receiving a client request, call a polling algorithm according to the traffic ratio to divert the traffic to the sub-cluster according to the traffic level, call a service corresponding to the sub-cluster to respond to the client request, and determine the service response quality;

[0028] The quality assessment unit is used to generate a capacity assessment result according to the service response quality under the traffic configuration.

[0029] In a third aspect, an embodiment of the present application provides a server, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the embodiment of the present application when executing the program.

[0030] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described in the embodiment of the present application.

[0031] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Other features, objects and advantages of the present application will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0033] Figure 1 A schematic diagram of a process for evaluating the capacity of an online service system provided in an embodiment of the present application is shown;

[0034] Figure 2 A schematic diagram of BFE traffic reception provided by an embodiment of the present application is shown;

[0035] Figure 3 An exemplary structural block diagram of an online service system capacity assessment device provided in an embodiment of the present application is shown;

[0036] Figure 4A schematic diagram of the structure of a computer system suitable for implementing a server of an embodiment of the present application is shown. DETAILED DESCRIPTION

[0037] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It is to be understood that the specific embodiments described herein are only used to explain the relevant invention, rather than to limit the invention. It is also necessary to explain that, for ease of description, only the parts related to the invention are shown in the accompanying drawings.

[0038] It should be noted that, in the absence of conflict, the embodiments in the present application and the features in the embodiments can be combined with each other. The present application will be described in detail below with reference to the drawings and in combination with the embodiments. Although the embodiments of the present application provide the method operation instruction steps shown in the following embodiments or drawings, more or fewer operation instruction steps may be included in the method based on routine or no creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiments of the present application. The method may be executed in the order of the methods shown in the embodiments or drawings or in parallel during the actual processing process or when the device is executed.

[0039] Please refer to Figure 1 , Figure 1 FIG. 1 is a flow chart showing a method for evaluating the capacity of an online service system provided by an embodiment of the present application. Figure 1 As shown, the method includes:

[0040] S101. The BFE platform determines the current traffic configuration to be tested; wherein the traffic configuration includes: the traffic ratio and traffic level of two sub-clusters respectively deployed in different computer rooms;

[0041] The sub-cluster refers to the sub-cluster of the BFE platform (online traffic access and forwarding platform). BFE is compatible with BNS (Backend Name Service, a backend name service that provides a mapping from service name to service running instance). Each BNS corresponds to a cluster concept in BFE. The BFE cluster name is generally the same as the BNS, one-to-one corresponding. BFE sub-cluster is a concept subdivided on the basis of BFE cluster, and subdivided into sub-machine rooms, such as Figure 2 The figure shows a schematic diagram of BFE traffic reception provided by this embodiment, similarly, a BFE cluster corresponds to a BFE sub-cluster A and a BFE sub-cluster B, and A and B have different computer room attributes.

[0042] The traffic ratio refers to the ratio of traffic distributed to two subclusters. The traffic distribution ratio is adjusted to achieve different traffic in the computer room. For example, the BFE platform can configure optional service subcluster traffic ratios: 5:5, 6:4, 7:3, 8:2, 9:1, 10:0, so that users can directly configure and select. The specific ratio setting is not limited here.

[0043] Traffic level refers to the traffic classification within a specified time range. For example, traffic can be classified by day. The levels include: low peak, medium-low peak, medium peak, medium-high peak, peak, etc. The specific level division is not limited here.

[0044] This method is based on the deployment of dual computer rooms for services, utilizes the characteristics of BFE traffic distribution, combines traffic classification with traffic ratio, and performs traffic configuration in pairs to achieve flexible online service capacity evaluation.

[0045] S102: After receiving the client request, based on the traffic ratio, call the polling algorithm to direct the traffic to the sub-cluster according to the traffic level, call the service corresponding to the sub-cluster to respond to the client request, and determine the service response quality;

[0046] Traffic is accessed from the client, which is the APP or WEB access entrance. BFE, as the access point of traffic, accesses the request traffic from customers accessed through the operator and forwards it to the real online service at the back end. The BFE platform configures the traffic ratio of the sub-cluster corresponding to the cluster. BFE uses a polling algorithm to direct traffic to the sub-cluster according to the traffic configuration ratio. The service corresponding to the sub-cluster actually processes the client request.

[0047] Based on the above traffic distribution process, the cluster corresponds to the BNS of the online service, and the sub-cluster corresponds to the BNS-A and BNS-B of the online service. The traffic can be isolated according to the computer room dimension, which is the basis for the online system capacity evaluation.

[0048] S103: Generate a capacity assessment result according to the service response quality under the traffic configuration.

[0049] Obtain the service response quality under the current traffic configuration, such as normal service response, abnormal service response (delay exceeds threshold, no response), etc., and generate the capacity assessment result under the traffic configuration.

[0050] The above steps introduce the capacity evaluation process under one traffic configuration. On this basis, the above steps can be repeated according to different traffic configurations based on actual test needs, and no limitation is made here.

[0051] Optionally, before determining the current flow configuration to be tested in step S101, a plurality of flow configurations with flow rates from low to high may be generated first according to the evaluation strategy;

[0052] Accordingly, step S101 determines the current flow configuration to be tested, which may be specifically: extracting the generated flow configurations in order from low to high flow rates as the current flow configuration to be tested;

[0053] Accordingly, step S102 generates a capacity assessment result according to the service response quality under the traffic configuration, which may be specifically: counting the service response quality under each traffic configuration, and generating a capacity assessment result according to the statistical result.

[0054] This method groups the BFE platform traffic proportions and business traffic levels in pairs, generates traffic configurations in order from low to high, and uses a combined progressive strategy to conduct a comprehensive capacity assessment and capacity verification of the system. This can gradually discover service shortcomings in the system while ensuring the stability of system operation.

[0055] It should be noted that although the operations of the method of the present invention are described in a particular order in the drawings, this does not require or imply that the operations must be performed in this particular order or that all illustrated operations must be performed to achieve desired results.

[0056] Based on the above introduction, the method provided in this embodiment performs traffic testing on the capacity assessment of the online service system through two sub-clusters deployed in different computer rooms respectively. The dual-computer room deployment feature of the online service can enable a single computer room to bear the online traffic. If one or more services encounter capacity risks during the capacity assessment, the traffic can be quickly restored to the dual computer rooms, thereby ensuring that the online service product functions can be provided to users continuously and uninterruptedly, and the online service has strong stability. In addition, the method configures the received user traffic according to the traffic ratio and traffic level, and directs the traffic to the corresponding sub-cluster according to the configuration. The traffic configuration is performed by combining traffic classification with traffic ratio, which has high flexibility, can improve the test accuracy, meet different test requirements in different application scenarios, and achieve effective and reliable capacity assessment. In addition, the use of real traffic for capacity assessment eliminates the problem of low simulation degree of test data and ensures the reliability of the generated capacity assessment results.

[0057] In one embodiment, a capacity assessment method based on service response quality is: if the traffic configuration reaches the maximum configurable traffic of the sub-cluster and the service response is normal, it is determined that the sub-cluster can bear the traffic peak.

[0058] The maximum configurable flow refers to the configuration of the flow ratio and flow level so that the subcluster can be allocated all the actual schedulable flow. For example, the flow ratio between this subcluster and other subclusters is 10:0, and the flow level is peak.

[0059] As for the sub-cluster that can carry the traffic peak, in one embodiment, the following operations can be further performed: determining the business logic label of each service in the sub-cluster that can carry the traffic peak; sequentially shielding instances of the same type as the business logic label, and determining the service response quality.

[0060] If a single computer room carries online traffic and the peak traffic level cannot reach the service capacity limit, the capacity limit can be further detected by adjusting the number of instances of the service itself that undertakes online traffic.

[0061] Specifically, if during the peak traffic period, when the BFE traffic ratio is 10:0, the system as a whole still has no shortcomings, which means that a single computer room can carry the total peak traffic. If the goal is for a single computer room to carry traffic, this solution is sufficient; if it is necessary to deal with a sudden increase in traffic, under such conditions, the service BNS's own routing capability test can be added according to actual usage needs, including: logically subdividing the service BNS, and dividing the logical labels of each service on the business link in proportion, BNS-A corresponds to BNS-Aa, BNS-Ab, BNS-Ac..., BNS-B corresponds to BNS-Ba, BNS-Bb, BNS-Bc..., starting from the upstream of the service (referring to the first layer away from the client), start to shield instances of the same type of labels in turn, and give priority to ensuring the stability of the service, because the fewer instances that carry online traffic, the higher the corresponding capacity risk. Through the above method, the capacity shortcomings in the system can be evaluated.

[0062] This embodiment provides a method for maintaining service stability. In an online service system capacity assessment based on actual traffic, if the determined service response quality shows that the service response is abnormal, the traffic can be reduced according to the abnormal adjustment strategy.

[0063] During the capacity assessment process, when dealing with emergencies, stop loss is given priority, overall risk is controlled, and the stability of system service quality is guaranteed to ensure that user traffic can still be handled normally in the event of an abnormal situation.

[0064] The specific setting of the abnormal adjustment strategy is not limited here, and can be set accordingly according to the actual needs of the system operation. To deepen the understanding, two abnormal adjustment strategies are introduced in this embodiment.

[0065] Specifically, if the traffic configuration does not reach the maximum configurable traffic of the sub-cluster and the service response is abnormal, the traffic ratio will be rolled back to an equal ratio. Here, an equal ratio means that the traffic of the two clusters is in an equal ratio, that is, 5:5.

[0066] When the BFE platform traffic ratio does not reach 10:0, the service response is abnormal, which means that the single computer room cannot carry the entire business traffic. At this time, the solution to deal with emergencies is: BFE platform traffic rolls back to 5:5 to quickly reduce the cluster's traffic to a balanced state between the two clusters.

[0067] Another abnormal adjustment strategy can be used to adjust the capacity limit detection of the sub-cluster that can carry the traffic peak in the above embodiment. Specifically, if the instances of the same type as the business logic label are blocked in sequence, the service responds abnormally and the traffic ratio is reversed.

[0068] The BFE platform traffic ratio reaches 10:0, which means that a single computer room can carry the entire business traffic. The routing capability adjustment of the service BNS itself is added for the cluster to conduct capacity assessment. The solution to cope with emergencies in this scenario is: the BFE platform traffic is adjusted to 0:10. This stop-loss solution is the fastest and most effective, because the service BNS is updated from a blocked state to an unblocked state with an effective period. Therefore, in order to reduce the effective time, this embodiment proposes to adjust the traffic ratio of the BFE platform to enable the system to quickly restore the effective service state.

[0069] Further references Figure 3 , which shows an exemplary structural block diagram of an online service system capacity assessment device provided according to this embodiment, the device is set up on the BFE platform, and mainly includes:

[0070] The configuration determination unit 101 is used to determine the current traffic configuration to be tested; wherein the traffic configuration includes: the traffic ratio and traffic level of two sub-clusters respectively deployed in different computer rooms;

[0071] The traffic diversion unit 102 is used to, after receiving a client request, call a polling algorithm according to the traffic ratio to divert the traffic to the sub-cluster according to the traffic level, call the service corresponding to the sub-cluster to respond to the client request, and determine the service response quality;

[0072] The quality evaluation unit 103 is used to generate a capacity evaluation result according to the service response quality under the traffic configuration.

[0073] It should be understood that the units described in the above device are similar to those in the reference Figure 1 The steps in the method described above correspond to each other. Therefore, the operations and features described above for the method are also applicable to the device and the units contained therein, and will not be repeated here. The device can be pre-implemented in the browser or other security application of the server, or loaded into the browser or its security application of the server by downloading or the like. The corresponding units in the device can cooperate with the units in the server to implement the solution of the embodiment of the present application.

[0074] For the several units mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more units described above can be embodied in one unit. On the contrary, the features and functions of one unit described above can be further divided into multiple units to be embodied.

[0075] It should be noted that for details not disclosed in the online service system capacity assessment device in the embodiment of the present application, please refer to the details disclosed in the above embodiments of the present application, which will not be repeated here.

[0076] Reference below Figure 4 , Figure 4 A schematic diagram of the structure of a computer system suitable for implementing a server of an embodiment of the present application is shown.

[0077] like Figure 4 As shown, the computer system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 402 or the program loaded from the storage part 408 into the random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation instructions of the system are also stored. The CPU 401, the ROM 402, and the RAM 403 are connected to each other through a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0078] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, etc.; an output section 407 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 408 including a hard disk, etc.; and a communication section 409 including a network interface card such as a LAN card, a modem, etc. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. A removable medium 411, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 410 as needed, so that a computer program read therefrom is installed into the storage section 408 as needed.

[0079] It should be noted that the computer-readable medium shown in the present application can be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media can include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present application, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present application, a computer-readable signal medium can include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium such as a computer-readable storage medium that can send, propagate or transmit a program for use by or in conjunction with an instruction execution system, device or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, optical cable, RF, etc., or any suitable combination of the above.

[0080] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation instruction of the system and method according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the aforementioned module, a program segment or a part of a code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the box can also occur in a sequence different from that marked in the accompanying drawings. For example, the boxes represented by two connections can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs a specified function or operation instruction, or can be implemented by a combination of dedicated hardware and computer instructions.

[0081] The units or modules involved in the embodiments described in the present application may be implemented by software or hardware. The units or modules described may also be arranged in a processor. The names of these units or modules do not, in some cases, constitute limitations on the units or modules themselves.

[0082] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the server described in the above embodiment, or may exist independently without being installed in the server. The above computer-readable storage medium stores one or more programs, and when the above programs are used by one or more processors to execute the online service system capacity assessment method described in the present application.

[0083] The above description is only a preferred embodiment of the present application and an explanation of the technical principles used. Those skilled in the art should understand that the scope of disclosure involved in the present application is not limited to the technical solution formed by a specific combination of the above technical features, but should also cover other technical solutions formed by any combination of the above technical features or their equivalent features without departing from the aforementioned disclosed concept. For example, the above features are replaced with the technical features with similar functions disclosed in this application (but not limited to) by each other to form a technical solution.

Claims

1. A method for evaluating the capacity of an online service system, characterized in that: include: The BFE platform determines the current traffic configuration to be tested; wherein the traffic configuration includes: the traffic ratio and traffic level of two sub-clusters respectively deployed in different computer rooms; After receiving the client request, according to the traffic ratio, calling the polling algorithm to direct the traffic to the sub-cluster according to the traffic level, calling the service corresponding to the sub-cluster to respond to the client request, and determining the service response quality; A capacity assessment result is generated according to the service response quality under the traffic configuration.

2. The method according to claim 1, characterized in that Before determining the current traffic configuration to be tested, the method further includes: Generate multiple traffic configurations from low to high traffic according to the evaluation strategy; Accordingly, the determining of the current flow configuration to be tested includes: extracting the generated flow configurations in order from low to high flow rates as the current flow configuration to be tested; Generating a capacity assessment result according to the service response quality under the traffic configuration includes: counting the service response quality under each of the traffic configurations, and generating a capacity assessment result according to the statistical result.

3. The method according to claim 1, characterized in that Generating a capacity assessment result according to the service response quality under the traffic configuration includes: If the traffic configuration reaches the maximum configurable traffic of the sub-cluster and the service response is normal, it is determined that the sub-cluster can bear the traffic peak.

4. The method according to claim 3, characterized in that Also includes: Determine the business logic labels of each service in the sub-cluster that can carry the traffic peak; Instances of the same type as the business logic tag are shielded in order, and the service response quality is determined.

5. The method according to claim 1, characterized in that Also includes: If the service response is abnormal, reduce the traffic according to the abnormal adjustment strategy.

6. The method according to claim 5, characterized in that If the service response is abnormal, reduce the traffic according to the abnormal adjustment strategy, including: If the traffic configuration does not reach the maximum configurable traffic of the subcluster and the service response is abnormal, the traffic ratio is rolled back to an equal ratio.

7. The method according to claim 5, characterized in that If the service response is abnormal, reduce the traffic according to the abnormal adjustment strategy, including: If the instances of the same type as the business logic tags are blocked in sequence, the service responds abnormally and the traffic ratio is reversed.

8. An online service system capacity assessment device, characterized in that: Applied to the BFE platform, the device includes: A configuration determination unit, used to determine the current traffic configuration to be tested; wherein the traffic configuration includes: the traffic ratio and traffic level of two sub-clusters respectively deployed in different computer rooms; A traffic diversion unit, configured to, after receiving a client request, call a polling algorithm according to the traffic ratio to divert the traffic to the sub-cluster according to the traffic level, call a service corresponding to the sub-cluster to respond to the client request, and determine the service response quality; The quality assessment unit is used to generate a capacity assessment result according to the service response quality under the traffic configuration.

9. A server comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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