High-concurrency informatization platform performance optimization system

Abstract

The invention relates to the technical field of data processing, and provides a high-concurrency informatization platform performance optimization system, which comprises a page view prediction module, a generation module, a resource pool management module and a service fusing protection module, and is characterized in that the page view prediction module, the generation module and the resource pool management module are connected in sequence; and the service fusing protection module is connected with the resource pool management module. According to the system provided by the embodiment of the invention, the predicted page view corresponding to each service is predicted at preset time intervals through the page view prediction module, the technical defect that a traditional resource management system cannot accurately pre-judge service fluctuation is solved, and the accurate cloud server management instruction is automatically generated through the generation module, so that the management efficiency is improved. And in combination with the elastic capacity expansion and shrinkage capability of the resource pool management module, allocation of computing resources according to needs is realized, so that performance reduction caused by insufficient resources is prevented, and cost waste caused by excessive resource configuration is also avoided.

Description

Technical Field

[0001] This application relates to the technical field of data processing, and in particular to a system for optimizing the performance of a high-concurrency information platform. Background Art

[0002] With the continuous advancement of the informatization process, in the process of digital transformation in various industries, they all rely on highly integrated cloud computing platforms to support business operations. Especially in industries such as the Internet, finance, e-commerce, and social media, with the rapid growth of the number of users and business volume, high-concurrency access has become one of the main technical challenges faced by the platform. Through virtualization technology, the cloud computing platform can dynamically allocate computing resources to ensure the high availability and high performance of services. In this context, the cloud service platform needs to have the ability to handle high-concurrency requests to meet the resource requirements of different business scenarios and effectively ensure the stable operation of the system.

[0003] However, traditional resource management systems often cannot accurately predict the fluctuations in the access volume of different services, resulting in insufficient or excessive waste of resource scheduling, which in turn affects the platform performance. Summary of the Invention

[0004] This application provides a system for optimizing the performance of a high-concurrency information platform to solve the problems raised in the above background art.

[0005] This application provides a system for optimizing the performance of a high-concurrency information platform, including an access volume prediction module, a generation module, a resource pool management module, and a service fuse protection module. Among them, the access volume prediction module, the generation module, and the resource pool management module are connected in sequence, and the service fuse protection module is connected to the resource pool management module; The access volume prediction module is used to predict the predicted access volume of each service within a first preset time period; The generation module is used to generate a first cloud server management instruction based on the predicted access volume of each service and send the first cloud server management instruction to the resource pool management module; where the first cloud server management instruction is a sequence of mapping relationships, and each mapping relationship in the sequence of mapping relationships is a mapping relationship between a certain service and the number of cloud servers corresponding to it within the first preset time period, and the mapping relationships corresponding to each service are arranged in sequence based on the priorities of each service to obtain the sequence of mapping relationships; The service fuse protection module is used for each physical server. When the operating state of the physical server is abnormal within a second preset time period, fuse the physical server and generate a second cloud server management instruction corresponding to the physical server, and send the second cloud server management instruction to the resource pool management module; The resource pool management module manages the cloud servers within the first preset time period based on the first cloud server management instruction and each of the second cloud server management instructions.

[0006] In a possible implementation manner, the access volume prediction module predicts the predicted access volume of each service within the first preset time period, including: For each of the services, obtain the historical detected access volume information and historical predicted access volume information of the service within the first preset time period, and predict the predicted access volume of the service within the first preset time period based on the historical detected access volume information and the historical predicted access volume information.

[0007] In a possible implementation manner, predicting the predicted access volume of the service within the first preset time period based on the historical detected access volume information and the historical predicted access volume information includes: Determine whether each historical detected access volume in the historical detected access volume information is exactly the same as each historical predicted access volume in the historical predicted access volume information; If they are exactly the same, determine any one of the historical detected access volumes in the historical detected access volume information as the predicted access volume; If they are not exactly the same, predict the predicted access volume of the service within the first preset time period based on the historical detected access volume information.

[0008] In a possible implementation manner, predicting the predicted access volume of the service within the first preset time period based on the historical detected access volume information includes: Arrange each of the historical detected access volumes in the historical detected access volume information in sequence according to the order in which they are collected to obtain a historical detected access volume sequence; Determine whether each historical detected access volume in the historical detected access volume sequence shows an increasing or decreasing trend; If so, predict the predicted access volume of the service within the first preset time period based on the historical detected access volume sequence; If not, determine the maximum historical detected access volume in the historical detected access volume sequence as the predicted access volume.

[0009] In a possible implementation manner, predicting the predicted access volume of the service within the first preset time period based on the historical detected access volume sequence includes: If each historical detected access volume in the historical detected access volume sequence shows an increasing trend; Sequentially obtain the first differences between adjacent historical inspection access volumes in the historical detected access volume sequence; Determine the sum of the last historical detected access volume in the historical detected access volume sequence and the maximum first difference among the first differences as the predicted access volume; If the historical detected access volumes in the historical detected access volume sequence show a decreasing trend; Determine the last historical detected access volume in the historical detected access volume sequence as the predicted access volume.

[0010] In a possible implementation, the generating module is used to generate a first cloud server management instruction based on the predicted access volume of each service, including: For each service, determine the number of cloud servers required by the service in the first preset time period based on the predicted access volume corresponding to the service, and construct a mapping relationship between the service and the number of cloud servers; Arrange the mapping relationships corresponding to the services in sequence based on the priorities of the services to obtain the first cloud server management instruction.

[0011] In a possible implementation, for each physical server, when the running state of the physical server is abnormal within the second preset time period, the service fusing protection module fuses the physical server and generates a second cloud server management instruction corresponding to the physical server, including: For each physical server, obtain the service processing duration information of the physical server within the second preset time period, and determine whether the physical server is abnormal based on the service processing duration information. If it is abnormal, fuse the physical server and generate a second cloud server management instruction corresponding to the physical server; wherein, the second cloud server management instruction is to switch the traffic information of the physical server within the first preset time period to a standby cloud server matching the physical server.

[0012] In a possible implementation, determining whether the physical server is abnormal based on the service processing duration information includes: Determine whether the service processing duration shows an increasing trend based on the service processing duration information; If it shows an increasing trend, determine that the physical server is abnormal; If it does not show an increasing trend, determine the target number of service processing durations greater than the preset service processing duration in the service processing duration information, and determine whether the target number is greater than the preset number; If it is greater than the preset number, determine that the physical server is abnormal; If it is not greater than the preset number, determine that the physical server is not abnormal.

[0013] In a possible implementation manner, the second cloud server management instruction is a traffic information switching instruction corresponding to a physical server with an anomaly within the first preset time period, and the resource pool management module manages the cloud servers within the first preset time period based on the first cloud server management instruction and each of the second cloud server management instructions, including: For each of the second cloud server management instructions, switch the traffic information of the physical server corresponding to the second cloud server management instruction within the first preset time period to its corresponding standby cloud server; Process the cloud servers of the services corresponding to each mapping relationship in sequence based on the mapping relationship sequence.

[0014] The present application provides a high-concurrency information platform performance optimization system, including a traffic prediction module, a generation module, a resource pool management module, and a service fuse protection module. Among them, the traffic prediction module, the generation module, and the resource pool management module are connected in sequence, and the service fuse protection module is connected to the resource pool management module; the traffic prediction module is used to predict the predicted traffic volume of each service within the first preset time period; the generation module is used to generate a first cloud server management instruction based on the predicted traffic volume of each service and send the first cloud server management instruction to the resource pool management module; the service fuse protection module is used to, for each physical server, when the operating state of the physical server is abnormal within the second preset time period, fuse the physical server and generate a second cloud server management instruction corresponding to the physical server, and send the second cloud server management instruction to the resource pool management module; the resource pool management module manages the cloud servers within the first preset time period based on the first cloud server management instruction and each of the second cloud server management instructions. On the one hand, the system provided in this embodiment predicts the predicted traffic volume of each service at preset intervals through the traffic prediction module, solves the technical defect that the traditional resource management system cannot accurately predict service fluctuations, provides a scientific basis for dynamic resource scheduling, and helps avoid resource shortage problems under sudden traffic. On the other hand, the generation module automatically generates accurate cloud server management instructions, and combines with the elastic scaling ability of the resource pool management module to achieve the on-demand allocation of computing resources, which not only prevents performance degradation caused by resource shortage, but also avoids cost waste caused by over-allocation of resources, significantly improving resource utilization. On the other hand, the service fuse protection module detects the operating state of the physical server at preset intervals, and quickly triggers the fuse mechanism when an anomaly is detected, isolating the faulty node and effectively blocking the service avalanche effect. Description of the Drawings

[0015] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can also be obtained based on these drawings.

[0016] Figure 1 It is a schematic block diagram of the structure of the high-concurrency information platform performance optimization system provided by the embodiments of the present application. Specific embodiments

[0017] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the scope of protection of the present invention.

[0018] The flowcharts shown in the accompanying drawings are only illustrative examples, and do not necessarily include all contents and operations / steps, nor do they necessarily need to be executed in the described order. For example, some operations / steps can also be decomposed, combined, or partially merged. Therefore, the actual execution order may be changed according to the actual situation.

[0019] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0020] It should be further understood that the term "and / or" used in the specification of the present application and the appended claims refers to any combination and all possible combinations of one or more of the related listed items, and includes these combinations.

[0021] The following will elaborate on some embodiments of the present application in conjunction with the accompanying drawings. Without conflict, the features in the following embodiments and the embodiments can be combined with each other.

[0022] Please refer to Figure 1 , Figure 1 which is a schematic block diagram of the structure of the high-concurrency information platform performance optimization system provided by the embodiments of the present application, as Figure 1As shown in the figure, the high-concurrency information platform performance optimization system provided by the embodiments of the present application includes a traffic prediction module, a generation module, a resource pool management module, and a service fuse protection module. Among them, the traffic prediction module, the generation module, and the resource pool management module are sequentially connected, and the service fuse protection module is connected to the resource pool management module; The traffic prediction module is used to predict the predicted traffic volume of each service within a first preset time period; The generation module is used to generate a first cloud server management instruction based on the predicted traffic volume of each service, and send the first cloud server management instruction to the resource pool management module; The service fuse protection module is used for each physical server. When the running state of the physical server is abnormal within a second preset time period, fuse the physical server and generate a second cloud server management instruction corresponding to the physical server, and send the second cloud server management instruction to the resource pool management module; wherein, the duration of the first preset time period is the same as that of the second preset time period, the first preset time period is the time period before the current moment, and the second preset time period is the time period after the current moment; The resource pool management module manages the cloud servers within the first preset time period based on the first cloud server management instruction and each of the second cloud server management instructions.

[0023] It should be noted that in this embodiment, each service corresponds to a unique physical server, and the high-concurrency information platform performance optimization system performs the above operations once every duration corresponding to the first preset time period and the second preset time period.

[0024] For the system provided in this embodiment, on the one hand, the traffic prediction module predicts the predicted traffic volume corresponding to each service every preset duration, solves the technical defect that the traditional resource management system cannot accurately predict service fluctuations, provides a scientific basis for dynamic resource scheduling, and helps to avoid resource shortages under sudden traffic. On the other hand, the generation module automatically generates accurate cloud server management instructions, and combines with the elastic scaling ability of the resource pool management module to achieve on-demand allocation of computing resources, which not only prevents performance degradation caused by resource shortages, but also avoids cost waste caused by over-allocation of resources, significantly improving resource utilization. On the other hand, the service fuse protection module detects the running state of the physical server every preset duration, and quickly triggers the fuse mechanism when an abnormality is detected, isolating the faulty node and effectively blocking the service avalanche effect.

[0025] In some embodiments, the traffic prediction module predicts the predicted traffic volume of each service within a first preset time period, including the following steps: For each of the said services, obtain the historical detected access volume information and historical predicted access volume information of the service within the first preset time period, and predict the predicted access volume of the service within the first preset time period based on the historical detected access volume information and the historical predicted access volume information. Exemplarily, if the first preset time period is 8:00 pm - 8:30 pm, then the historical detected access volume information is the historical detected access volume within 8:00 pm - 8:30 pm every day, and the historical predicted access volume information is the historical predicted access volume within 8:00 pm - 8:30 pm every day.

[0026] The system provided in this embodiment helps to improve the accuracy of business traffic prediction by combining the dual data sources of historical detected access volume information and historical predicted access volume information.

[0027] In some embodiments, predicting the predicted access volume of the service within the first preset time period based on the historical detected access volume information and the historical predicted access volume information includes the following steps: Determine whether each historical detected access volume in the historical detected access volume information is exactly the same as each historical predicted access volume in the historical predicted access volume information; If they are exactly the same, determine any one of the historical detected access volumes in the historical detected access volume information as the predicted access volume; If they are not exactly the same, predict the predicted access volume of the service within the first preset time period based on the historical detected access volume information.

[0028] The system provided in this embodiment, on the one hand, when each historical detected access volume in the historical detected access volume information is exactly the same as each historical predicted access volume in the historical predicted access volume information, determines any one of the historical detected access volumes in the historical detected access volume information as the predicted access volume, avoiding the resource overhead caused by repeated calculations. On the other hand, when each historical detected access volume in the historical detected access volume information is not exactly the same as each historical predicted access volume in the historical predicted access volume information, predicting the predicted access volume of the service within the first preset time period based on the historical detected access volume information helps to prevent the problem of insufficient resource preparation.

[0029] In some embodiments, predicting the predicted access volume of the service within the first preset time period based on the historical detected access volume information includes the following steps: Arrange each of the historical detected access volumes in the order of their collection sequence based on the historical detected access volume information to obtain a historical detected access volume sequence; Determine whether each historical detected access volume in the historical detected access volume sequence shows an increasing or decreasing trend; If so, predict the predicted access volume of the service within the first preset time period based on the historical detected access volume sequence; If not, determine the maximum historical detected access volume in the historical detected access volume sequence as the predicted access volume.

[0030] On the one hand, the system provided in this embodiment can accurately identify the change law of service traffic by arranging the historical detected access volume in time series and judging the trend. When a continuous growth or decline trend is detected, a trend extrapolation algorithm is used for prediction, which helps to improve the prediction accuracy. On the other hand, for the fluctuating scenario without a significant trend, a maximum value prediction strategy is adopted, and the previous peak value is used as the benchmark prediction value, which helps to avoid the risk of insufficient resource preparation.

[0031] In some embodiments, predicting the predicted access volume of the service within the first preset time period based on the historical detected access volume sequence includes the following steps: If the historical detected access volumes in the historical detected access volume sequence show an increasing trend; Sequentially obtain the first differences between adjacent historical inspection access volumes in the historical detected access volume sequence; Determine the sum of the last historical detected access volume in the historical detected access volume sequence and the maximum first difference among the first differences as the predicted access volume; If the historical detected access volumes in the historical detected access volume sequence show a decreasing trend; Determine the last historical detected access volume in the historical detected access volume sequence as the predicted access volume.

[0032] The system provided in this embodiment adopts a prediction strategy of "last value + maximum increment" for the growth trend scenario and a conservative last value prediction method for the decline trend scenario, which helps to avoid the risk of insufficient resource preparation.

[0033] In some embodiments, the generating module is used to generate a first cloud server management instruction based on the predicted access volume of each service, including the following steps: For each service, determine the number of cloud servers required by the service within the first preset time period based on the predicted access volume corresponding to the service, and construct a mapping relationship between the service and the number of cloud servers; Arrange the mapping relationships corresponding to each service in sequence based on the priorities of each service to obtain the first cloud server management instruction.

[0034] On the one hand, the system provided in this embodiment realizes the quantitative calculation of resource requirements by constructing the mapping relationship between services and the number of corresponding cloud servers, enabling each service to obtain computing resources that match its actual needs. On the other hand, by arranging the mapping relationships corresponding to the services in sequence based on the priorities of the services, the first cloud server management instruction is obtained, which helps to preferentially arrange computing resources for high-priority services in the subsequent process and prevent the risk of interruption of high-priority services.

[0035] In some embodiments, for each physical server, when the operating state of the physical server is abnormal within a second preset time period, the service fuse protection module fuses the physical server and generates a second cloud server management instruction corresponding to the physical server, including the following steps: For each physical server, obtain the service processing duration information of the physical server within the second preset time period, and judge whether the physical server is abnormal based on the service processing duration information. If it is abnormal, fuse the physical server and generate a second cloud server management instruction corresponding to the physical server; wherein, the second cloud server management instruction is to switch the traffic information of the physical server within the first preset time period to a standby cloud server that matches the physical server.

[0036] In this embodiment, each physical server corresponds to a unique standby cloud server in the resource pool management module.

[0037] The system provided in this embodiment adopts a "one-to-one" standby cloud server configuration strategy to ensure that each physical server has a dedicated disaster recovery backup node, enabling traffic switching when a fuse is triggered and ensuring business continuity.

[0038] In some embodiments, the judging whether the physical server is abnormal based on the service processing duration information includes the following steps: Judge whether the service processing duration shows an increasing trend based on the service processing duration information; If it shows an increasing trend, determine that the physical server is abnormal; If it does not show an increasing trend, determine the target number of service processing durations greater than the preset service processing duration in the service processing duration information, and judge whether the target number is greater than the preset number; If it is greater than the preset number, determine that the physical server is abnormal; If it is not greater than the preset number, determine that the physical server is not abnormal.

[0039] On the one hand, the system provided by this embodiment can early detect the progressive deterioration of the server performance by analyzing the growth of the business processing duration through trend analysis, and give early warnings in time before the problems cause serious failures, realizing preventive maintenance. On the other hand, for non-trend anomalies, a dual judgment mechanism of threshold and quantity is adopted, which not only considers the severity of a single timeout event but also counts the concentration degree of abnormal events, helping to reduce the misjudgment rate.

[0040] In some embodiments, the first cloud server management instruction is a sequence of mapping relationships. Each mapping relationship in the sequence of mapping relationships is a mapping relationship between a certain service and the number of cloud servers corresponding to it within the first preset time period. The sequence of mapping relationships is obtained by arranging the mapping relationships corresponding to each service in sequence based on the priorities of the services. The second server management instruction is a traffic information switching instruction corresponding to a physical server with an anomaly within the first preset time period. The resource pool management module manages the cloud servers within the first preset time period based on the first cloud server management instruction and each of the second cloud server management instructions, including the following steps: For each of the second cloud server management instructions, switch the traffic information of the physical server corresponding to the second cloud server management instruction to its corresponding standby cloud server within the first preset time period; Process the cloud servers of each service corresponding to each mapping relationship in sequence based on the sequence of mapping relationships. Specifically, for each service, if it is necessary to increase the number of cloud servers, copy a certain number of standby cloud servers corresponding to the physical servers of the service based on the number of cloud servers corresponding to the service and the existing number of cloud servers corresponding to the service. If it is necessary to reduce the number of cloud servers, delete a certain number of existing cloud servers based on the number of cloud servers corresponding to the service and the existing number of cloud servers corresponding to the service.

[0041] On the one hand, the second cloud server management instruction designed for the abnormal physical server in this embodiment realizes the rapid isolation of the abnormal physical server and the migration of traffic information, helping to ensure the reliability of the system. On the other hand, the expansion mechanism of "copying standby cloud servers" and the reduction strategy of "directed deletion" are adopted, making the resource adjustment process more precisely controllable. It not only avoids the resource waste of traditional overall expansion but also prevents the service interruption that may be caused by random reduction.

[0042] The above is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should all be covered within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A high-concurrency information platform performance optimization system, characterized in that It includes a traffic prediction module, a generation module, a resource pool management module, and a service fuse protection module. Among them, the traffic prediction module, the generation module, and the resource pool management module are connected in sequence, and the service fuse protection module is connected to the resource pool management module; The traffic prediction module is used to predict the predicted traffic of each service within a first preset time period; The generation module is used to generate a first cloud server management instruction based on the predicted traffic of each service, and send the first cloud server management instruction to the resource pool management module; wherein, the first cloud server management instruction is a sequence of mapping relationships, and each mapping relationship in the sequence of mapping relationships is a mapping relationship between a certain service and the number of cloud servers corresponding to it within the first preset time period, and the mapping relationships corresponding to each service are arranged in sequence based on the priorities corresponding to each service to obtain the sequence of mapping relationships; The service fuse protection module is used for each physical server. When the running state of the physical server is abnormal within a second preset time period, fuse the physical server and generate a second cloud server management instruction corresponding to the physical server, and send the second cloud server management instruction to the resource pool management module; The resource pool management module manages the cloud servers within the first preset time period based on the first cloud server management instruction and each of the second cloud server management instructions.

2. The high-concurrency information platform performance optimization system according to claim 1, wherein The traffic prediction module predicts the predicted traffic of each service within a first preset time period, including: For each service, obtain the historical detected traffic information and historical predicted traffic information of the service within the first preset time period, and predict the predicted traffic of the service within the first preset time period based on the historical detected traffic information and the historical predicted traffic information.

3. The high-concurrency information platform performance optimization system according to claim 2, characterized in that, The predicting the predicted traffic of the service within the first preset time period based on the historical detected traffic information and the historical predicted traffic information includes: Judge whether each historical detected traffic in the historical detected traffic information is exactly the same as each historical predicted traffic in the historical predicted traffic information; If they are exactly the same, determine any one of the historical detected traffics in the historical detected traffic information as the predicted traffic; If they are not exactly the same, predict the predicted traffic of the service within the first preset time period based on the historical detected traffic information.

4. The high-concurrency information platform performance optimization system according to claim 3, wherein The predicting the predicted traffic of the service within the first preset time period based on the historical detected traffic information includes: Arrange each of the historical detected traffics in the historical detected traffic information in sequence according to the order in which they are collected to obtain a historical detected traffic sequence; Judge whether the historical detected traffics in the historical detected traffic sequence show an increasing or decreasing trend; If so, predict the predicted traffic of the service within the first preset time period based on the historical detected traffic sequence; If not, determine the maximum historical detected traffic in the historical detected traffic sequence as the predicted traffic.

5. The high-concurrency information platform performance optimization system according to claim 4, characterized in that Predicting the predicted access volume of the service within the first preset time period based on the historical detected access volume sequence includes: If the historical detected access volumes in the historical detected access volume sequence show an increasing trend; Sequentially obtain the first differences between adjacent historical inspection access volumes in the historical detected access volume sequence; Determine the sum of the last historical detected access volume in the historical detected access volume sequence and the maximum first difference among the first differences as the predicted access volume; If the historical detected access volumes in the historical detected access volume sequence show a decreasing trend; Determine the last historical detected access volume in the historical detected access volume sequence as the predicted access volume.

6. The high-concurrency information platform performance optimization system according to claim 1, characterized in that The generating module is used to generate a first cloud server management instruction based on the predicted access volumes of each service, including: For each service, determine the number of cloud servers required by the service within the first preset time period based on the predicted access volume corresponding to the service, and construct a mapping relationship between the service and the number of cloud servers; Arrange the mapping relationships corresponding to each service in sequence based on the priority of each service to obtain the first cloud server management instruction.

7. The high-concurrency information platform performance optimization system according to claim 1, characterized in that, The service fuse protection module, for each physical server, when the operating state of the physical server is abnormal within the second preset time period, fuses the physical server and generates a second cloud server management instruction corresponding to the physical server, including: For each physical server, obtain the service processing duration information of the physical server within the second preset time period, and determine whether the physical server is abnormal based on the service processing duration information. If it is abnormal, fuse the physical server and generate a second cloud server management instruction corresponding to the physical server; wherein, the second cloud server management instruction is to switch the traffic information of the physical server within the first preset time period to a standby cloud server matching the physical server.

8. The high-concurrency information platform performance optimization system according to claim 7, characterized in that, Determining whether the physical server is abnormal based on the service processing duration information includes: Determine whether the service processing duration shows an increasing trend based on the service processing duration information; If it shows an increasing trend, determine that the physical server is abnormal; If it does not show an increasing trend, determine the target number of service processing durations greater than the preset service processing duration in the service processing duration information, and determine whether the target number is greater than the preset number; If it is greater than the preset number, determine that the physical server is abnormal; If it is not greater than the preset number, determine that the physical server is not abnormal.

9. The high-concurrency information platform performance optimization system according to claim 1, wherein The second cloud server management instruction is a traffic information switching instruction corresponding to the physical server with an abnormality within the first preset time period. The resource pool management module manages the cloud servers within the first preset time period based on the first cloud server management instruction and each second cloud server management instruction, including: For each second cloud server management instruction, switch the traffic information of the physical server corresponding to the second cloud server management instruction within the first preset time period to its corresponding standby cloud server; Process the cloud servers of the services corresponding to each mapping relationship in sequence based on the sequence of the mapping relationships.

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