A high-concurrency information platform performance optimization system

Abstract

The present application relates to the field of data processing technology and provides a high-concurrency information platform performance optimization system, comprising a visit volume prediction module, a generation module, a resource pool management module, and a service circuit breaker protection module, wherein the visit volume prediction module, the generation module, and the resource pool management module are sequentially connected, and the service circuit breaker protection module is connected to the resource pool management module. The system provided in this embodiment uses the visit volume prediction module to predict the predicted visit volume corresponding to each business at preset intervals, thereby resolving the technical defect of traditional resource management systems that cannot accurately predict business fluctuations. The generation module automatically generates accurate cloud server management instructions, and combined with the elastic scaling capabilities of the resource pool management module, realizes on-demand allocation of computing resources, thereby preventing both performance degradation caused by insufficient resources and cost waste caused by over-allocation of resources.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a high-concurrency information platform performance optimization system. Background Art

[0002] With the continuous advancement of informatization, various industries undergoing digital transformation are relying on highly integrated cloud computing platforms to support business operations. This is particularly true in industries such as the internet, finance, e-commerce, and social media. With the rapid growth in user numbers and business volume, high concurrent access has become a major technical challenge facing these platforms. Cloud computing platforms leverage virtualization technology to dynamically allocate computing resources, ensuring high availability and high performance. In this context, cloud service platforms must be able to handle high-concurrency requests to meet the resource demands of diverse business scenarios and effectively ensure stable system operation.

[0003] However, traditional resource management systems often cannot accurately predict the fluctuations in traffic for different businesses, resulting in insufficient or excessive waste of resources, which in turn affects platform performance. Summary of the Invention

[0004] This application provides a high-concurrency information platform performance optimization system to solve the problems raised by the above background technology.

[0005] The present application provides a high-concurrency information platform performance optimization system, including an access volume prediction module, a generation module, a resource pool management module, and a service fuse protection module, wherein 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;

[0006] The visit volume prediction module is used to predict the predicted visit volume of each business within a first preset time period;

[0007] The generation module is configured 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; wherein the first cloud server management instruction is a mapping relationship sequence, wherein each mapping relationship in the mapping relationship sequence 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 relationship sequence is obtained by sequentially arranging the mapping relationships corresponding to each service based on the priority corresponding to each service;

[0008] The service fuse protection module is used to, for each physical server, when the operating status 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;

[0009] 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.

[0010] In a possible implementation, the visit volume prediction module predicts the predicted visit volume of each service within a first preset time period, including:

[0011] For each of the services, historical detected visit volume information and historical predicted visit volume information of the service within the first preset time period are obtained, and predicted visit volume of the service within the first preset time period is predicted based on the historical detected visit volume information and the historical predicted visit volume information.

[0012] In a possible implementation, the predicting the predicted visit volume of the service within the first preset time period based on the historical detected visit volume information and the historical predicted visit volume information includes:

[0013] Determining whether each historical detected page views in the historical detected page view information is completely consistent with each historical predicted page views in the historical predicted page view information;

[0014] If they are completely consistent, determining any of the historical detected page views in the historical detected page view information as the predicted page views;

[0015] If they are not completely consistent, the predicted visit volume of the service in the first preset time period is predicted based on the historical detected visit volume information.

[0016] In a possible implementation, the predicting the predicted visit volume of the service within the first preset time period based on the historically detected visit volume information includes:

[0017] Arranging the historical detection access quantities in the historical detection access quantity information in order based on the order in which the historical detection access quantities are collected to obtain a historical detection access quantity sequence;

[0018] Determining whether each historical detection access volume in the historical detection access volume sequence shows an increasing or decreasing trend;

[0019] If yes, predicting the predicted visits of the service within the first preset time period based on the historical detected visit volume sequence;

[0020] If not, the maximum historical detected visit amount in the historical detected visit amount sequence is determined as the predicted visit amount.

[0021] In a possible implementation, the predicting the predicted visit volume of the service within the first preset time period based on the historical detected visit volume sequence includes:

[0022] If each historical detection visit amount in the historical detection visit amount sequence shows an increasing trend;

[0023] Sequentially obtaining first differences between adjacent historical detection access amounts in the historical detection access amount sequence;

[0024] Determine the sum of the last historical detected visit amount in the historical detected visit amount sequence and the maximum first difference among the first differences as the predicted visit amount;

[0025] If the historical detection visit volume in the historical detection visit volume sequence shows a decreasing trend;

[0026] The last historical detected page views in the historical detected page views sequence is determined as the predicted page views.

[0027] In one possible implementation, the generating module is configured to generate a first cloud server management instruction based on the predicted traffic of each service, including:

[0028] For each of the services, determining the number of cloud servers required for the service in the first preset time period based on the predicted traffic corresponding to the service, and establishing a mapping relationship between the service and the number of cloud servers;

[0029] The mapping relationships corresponding to the services are arranged in order based on the priorities of the services to obtain the first cloud server management instruction.

[0030] In one possible implementation, the service fuse protection module, for each physical server, fuses the physical server and generates a second cloud server management instruction corresponding to the physical server when the operating status of the physical server is abnormal within a second preset time period, including:

[0031] For each of the physical servers, the business processing duration information of the physical server within the second preset time period is obtained, and based on the business processing duration information, it is determined whether there is an abnormality in the physical server. If there is an abnormality, the physical server is fused and a second cloud server management instruction corresponding to the physical server is generated; 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 backup cloud server that matches the physical server.

[0032] In a possible implementation, determining whether the physical server is abnormal based on the service processing duration information includes:

[0033] Determining whether the business processing time is increasing based on the business processing time information;

[0034] If there is an increasing trend, it is determined that the physical server has an abnormality;

[0035] If there is no increasing trend, determining a target number of business processing durations in the business processing duration information that are greater than a preset business processing duration, and determining whether the target number is greater than a preset number;

[0036] If the number is greater than a preset number, it is determined that an abnormality exists in the physical server;

[0037] If it is not greater than the preset number, it is determined that there is no abnormality in the physical server.

[0038] In one possible implementation, the second cloud server management instruction is a traffic information switching instruction corresponding to the abnormal physical server 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:

[0039] For each of the second cloud server management instructions, switching 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;

[0040] The cloud servers of the services corresponding to the respective mapping relationships are processed in sequence based on the mapping relationship sequence.

[0041] The present application provides a high-concurrency information platform performance optimization system, including an access volume prediction module, a generation module, a resource pool management module and a service fuse protection module, wherein 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 business 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 business, 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 there is an abnormality in the operating status of the physical server 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 second cloud server management instruction. The system provided in this embodiment, on the one hand, predicts the predicted visits corresponding to each business at preset time intervals through the visit prediction module, thereby solving the technical defect that traditional resource management systems cannot accurately predict business fluctuations, providing a scientific basis for dynamic resource scheduling, and helping to avoid resource shortages under sudden traffic. On the other hand, the generation module automatically generates accurate cloud server management instructions, and combined with the elastic scaling capabilities of the resource pool management module, realizes on-demand allocation of computing resources, which not only prevents performance degradation caused by insufficient resources, but also avoids cost waste caused by excessive resource configuration, and significantly improves resource utilization. On the other hand, the service circuit breaker protection module detects the operating status of the physical server at preset time intervals, quickly triggers the circuit breaker mechanism when an anomaly is detected, isolates the faulty node, and effectively blocks the service avalanche effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 This is a schematic block diagram of the structure of the high-concurrency information platform performance optimization system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0044] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0045] The flowcharts shown in the accompanying drawings are for illustrative purposes only and do not necessarily include all contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, combined, or partially merged, so the actual execution order may vary depending on the actual situation.

[0046] It should also be understood that the terms used in this specification are for the purpose of describing specific embodiments only and are not intended to limit the present application. As used in this specification and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0047] It should be further understood that the term "and / or" used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0048] The following describes some embodiments of the present application in detail with reference to the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0049] See also Figure 1 , Figure 1 A schematic block diagram of the structure of the high-concurrency information platform performance optimization system provided in the embodiment of the present application is shown in FIG. Figure 1 As shown, the high-concurrency information platform performance optimization system provided in the embodiment of the present application includes an access volume prediction module, a generation module, a resource pool management module, and a service fuse protection module, wherein 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;

[0050] The visit volume prediction module is used to predict the predicted visit volume of each business within a first preset time period;

[0051] The generating module is used to generate a first cloud server management instruction based on the predicted access volume of each business, and send the first cloud server management instruction to the resource pool management module;

[0052] The service fuse protection module is used to, for each physical server, fuse the physical server and generate a second cloud server management instruction corresponding to the physical server when the operating status of the physical server is abnormal within a second preset time period, and send the second cloud server management instruction to the resource pool management module; wherein, the first preset time period is the same as the second preset time period in length, 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;

[0053] 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.

[0054] It should be noted that, in this embodiment, each business corresponds to a unique physical server, and the high-concurrency information platform performance optimization system performs the above operation once every time the first preset time period corresponds to the second preset time period.

[0055] The system provided in this embodiment, on the one hand, predicts the predicted visits corresponding to each business at preset time intervals through the visit prediction module, thereby solving the technical defect that traditional resource management systems cannot accurately predict business fluctuations, providing a scientific basis for dynamic resource scheduling, and helping to avoid resource shortages under sudden traffic. On the other hand, the generation module automatically generates accurate cloud server management instructions, and combined with the elastic scaling capabilities of the resource pool management module, realizes on-demand allocation of computing resources, which not only prevents performance degradation caused by insufficient resources, but also avoids cost waste caused by excessive resource configuration, and significantly improves resource utilization. On the other hand, the service circuit breaker protection module detects the operating status of the physical server at preset time intervals, quickly triggers the circuit breaker mechanism when an anomaly is detected, isolates the faulty node, and effectively blocks the service avalanche effect.

[0056] In some embodiments, the visit volume prediction module predicts the predicted visit volume of each service within a first preset time period, including the following steps:

[0057] For each of the services, historical detected visit volume information and historical predicted visit volume information for the service within the first preset time period are obtained, and predicted visit volume for the service within the first preset time period is predicted based on the historical detected visit volume information and the historical predicted visit volume information. For example, if the first preset time period is 8:00 pm - 8:30 pm, then the historical detected visit volume information is the historical detected visit volume between 8:00 pm and 8:30 pm every day, and the historical predicted visit volume information is the historical predicted visit volume between 8:00 pm and 8:30 pm every day.

[0058] The system provided in this embodiment helps to improve the accuracy of business traffic prediction by combining the dual data sources of historical detection visit volume information and historical prediction visit volume information.

[0059] In some embodiments, the predicting the predicted visit volume of the service within the first preset time period based on the historical detected visit volume information and the historical predicted visit volume information includes the following steps:

[0060] Determining whether each historical detected page views in the historical detected page view information is completely consistent with each historical predicted page views in the historical predicted page view information;

[0061] If they are completely consistent, determining any of the historical detected page views in the historical detected page view information as the predicted page views;

[0062] If they are not completely consistent, the predicted visit volume of the service in the first preset time period is predicted based on the historical detected visit volume information.

[0063] The system provided in this embodiment, on the one hand, determines that any one of the historical detected access volumes in the historical detected access volume information is the predicted access volume when each historical detected access volume in the historical detected access volume information is completely consistent with each historical predicted access volume in the historical predicted access volume information, thereby avoiding resource overhead caused by repeated calculation; on the other hand, when each historical detected access volume in the historical detected access volume information is not completely consistent with each historical predicted access volume in the historical predicted access volume information, predicts the predicted access volume of the service within the first preset time period based on the historical detected access volume information, which helps to prevent the problem of insufficient resource preparation.

[0064] In some embodiments, the predicting of the predicted visit volume of the service within the first preset time period based on the historical detected visit volume information includes the following steps:

[0065] Arranging the historical detection access quantities in the historical detection access quantity information in order based on the order in which the historical detection access quantities are collected to obtain a historical detection access quantity sequence;

[0066] Determining whether each historical detection access volume in the historical detection access volume sequence shows an increasing or decreasing trend;

[0067] If yes, predicting the predicted visits of the service within the first preset time period based on the historical detected visit volume sequence;

[0068] If not, the maximum historical detected visit amount in the historical detected visit amount sequence is determined as the predicted visit amount.

[0069] The system provided in this embodiment, on the one hand, can accurately identify the changing patterns of business traffic by arranging historical detection visits in time series and judging trends. When a continuous growth or downward trend is detected, a trend extrapolation algorithm is used for prediction, which helps to improve the accuracy of the prediction. On the other hand, for fluctuation scenarios without significant trends, a maximum value prediction strategy is adopted, using the previous peak value as the benchmark prediction value, which helps to avoid the risk of insufficient resource preparation.

[0070] In some embodiments, the predicting of the predicted visit volume of the service within the first preset time period based on the historical detected visit volume sequence includes the following steps:

[0071] If each historical detection visit amount in the historical detection visit amount sequence shows an increasing trend;

[0072] Sequentially obtaining first differences between adjacent historical detection access amounts in the historical detection access amount sequence;

[0073] Determine the sum of the last historical detected visit amount in the historical detected visit amount sequence and the maximum first difference among the first differences as the predicted visit amount;

[0074] If the historical detection visit volume in the historical detection visit volume sequence shows a decreasing trend;

[0075] The last historical detected page views in the historical detected page views sequence is determined as the predicted page views.

[0076] The system provided in this embodiment adopts a "final value + maximum increment" prediction strategy for growth trend scenarios, and adopts a conservative final value prediction method for downward trend scenarios, which helps to avoid the risk of insufficient resource preparation.

[0077] In some embodiments, the generating module is configured to generate a first cloud server management instruction based on the predicted traffic of each service, comprising the following steps:

[0078] For each of the services, determining the number of cloud servers required for the service in the first preset time period based on the predicted traffic corresponding to the service, and establishing a mapping relationship between the service and the number of cloud servers;

[0079] The mapping relationships corresponding to the services are arranged in order based on the priorities of the services to obtain the first cloud server management instruction.

[0080] The system provided in this embodiment, on the one hand, realizes the quantitative calculation of resource requirements by constructing a mapping relationship between the business and the corresponding number of cloud servers, so that the computing resources obtained by each business match its actual needs. On the other hand, by arranging the mapping relationships corresponding to each business in sequence based on the priority of each business, the first cloud server management instruction is obtained, which helps to prioritize computing resources for high-priority businesses in the future and prevent the risk of interruption of high-priority businesses.

[0081] In some embodiments, the service fuse protection module, for each physical server, when the operating status of the physical server is abnormal within a second preset time period, fuses the physical server and generates a second cloud server management instruction corresponding to the physical server, including the following steps:

[0082] For each of the physical servers, the business processing duration information of the physical server within the second preset time period is obtained, and based on the business processing duration information, it is determined whether there is an abnormality in the physical server. If there is an abnormality, the physical server is fused and a second cloud server management instruction corresponding to the physical server is generated; 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 backup cloud server that matches the physical server.

[0083] In this embodiment, each of the physical servers corresponds to a unique backup cloud server in the resource pool management module.

[0084] The system provided in this embodiment adopts a "one-to-one" backup cloud server configuration strategy to ensure that each physical server has a dedicated disaster recovery backup node. When a circuit breaker is triggered, traffic switching can be achieved to ensure business continuity.

[0085] In some embodiments, determining whether the physical server is abnormal based on the service processing duration information includes the following steps:

[0086] Determining whether the business processing time is increasing based on the business processing time information;

[0087] If there is an increasing trend, it is determined that the physical server has an abnormality;

[0088] If there is no increasing trend, determining a target number of business processing durations in the business processing duration information that are greater than a preset business processing duration, and determining whether the target number is greater than a preset number;

[0089] If the number is greater than a preset number, it is determined that an abnormality exists in the physical server;

[0090] If it is not greater than the preset number, it is determined that there is no abnormality in the physical server.

[0091] The system provided in this embodiment, on the one hand, can determine the growth of business processing time through trend analysis, thereby being able to detect the progressive degradation of server performance at an early stage and provide timely warnings before the problem causes serious failures, thus achieving 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 of abnormal events, helping to reduce the misjudgment rate.

[0092] In some embodiments, the first cloud server management instruction is a mapping relationship sequence, each mapping relationship in the mapping relationship sequence 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 relationship sequence is obtained by arranging the mapping relationships corresponding to each service in sequence based on the priority corresponding to each service. The second server management instruction is a traffic information switching instruction corresponding to an abnormal physical server 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:

[0093] For each of the second cloud server management instructions, switching 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;

[0094] The cloud servers of the business corresponding to each mapping relationship are processed in sequence based on the mapping relationship sequence; specifically, for each business, if it is necessary to add cloud servers, a certain number of backup cloud servers corresponding to the physical servers corresponding to the business are copied based on the number of cloud servers corresponding to the business and the number of existing cloud servers corresponding to the business; if it is necessary to reduce cloud servers, a certain number of existing cloud servers are deleted based on the number of cloud servers corresponding to the business and the number of existing cloud servers corresponding to the business.

[0095] The system provided in this embodiment, on the one hand, uses the second cloud server management instructions designed for abnormal physical servers to achieve rapid isolation of abnormal physical servers and migration of traffic information, which helps to ensure the reliability of the system. On the other hand, the use of the "copy backup cloud server" expansion mechanism and the "targeted deletion" reduction strategy makes the resource adjustment process more accurate and controllable, avoiding both the resource waste of traditional overall expansion and the business interruption that may be caused by random reduction.

[0096] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present application, and such modifications or substitutions should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A high-concurrency information platform performance optimization system, characterized in that: It includes an access volume prediction module, a generation module, a resource pool management module and a service fuse protection module, wherein 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 visit volume prediction module is used to predict the predicted visit volume of each business within a first preset time period; The generation module is configured 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; wherein the first cloud server management instruction is a mapping relationship sequence, wherein each mapping relationship in the mapping relationship sequence 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 relationship sequence is obtained by sequentially arranging the mapping relationships corresponding to each service based on the priority corresponding to each service; The service fuse protection module is used to, for each physical server, when the operating status 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; The visit volume prediction module predicts the predicted visit volume of each service within the first preset time period, including: For each of the services, obtaining historical detected visit volume information and historical predicted visit volume information of the service within the first preset time period, and predicting the predicted visit volume of the service within the first preset time period based on the historical detected visit volume information and the historical predicted visit volume information; The predicting the predicted visit volume of the service within the first preset time period based on the historical detected visit volume information and the historical predicted visit volume information includes: Determining whether each historical detected page views in the historical detected page view information is completely consistent with each historical predicted page views in the historical predicted page view information; If they are completely consistent, determining any of the historical detected page views in the historical detected page view information as the predicted page views; If they are not completely consistent, predicting the predicted visits of the service within the first preset time period based on the historical detected visit information; The service fuse protection module, for each physical server, fuses the physical server and generates a second cloud server management instruction corresponding to the physical server when the operating status of the physical server is abnormal within a second preset time period, including: For each of the physical servers, the business processing duration information of the physical server within the second preset time period is obtained, and based on the business processing duration information, it is determined whether there is an abnormality in the physical server. If there is an abnormality, the physical server is fused and a second cloud server management instruction corresponding to the physical server is generated; 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 backup cloud server that matches the physical server.

2. The high-concurrency information platform performance optimization system according to claim 1 is characterized in that: The predicting the predicted visit volume of the service within the first preset time period based on the historical detected visit volume information includes: Arranging the historical detection access quantities in the historical detection access quantity information in order based on the order in which the historical detection access quantities are collected to obtain a historical detection access quantity sequence; Determining whether each historical detection access volume in the historical detection access volume sequence shows an increasing or decreasing trend; If yes, predicting the predicted visits of the service within the first preset time period based on the historical detected visit volume sequence; If not, the maximum historical detected visit amount in the historical detected visit amount sequence is determined as the predicted visit amount.

3. The high-concurrency information platform performance optimization system according to claim 2 is characterized in that: The predicting the predicted visit volume of the service within the first preset time period based on the historical detected visit volume sequence includes: If each historical detection visit amount in the historical detection visit amount sequence shows an increasing trend; Sequentially obtaining first differences between adjacent historical detection access amounts in the historical detection access amount sequence; Determine the sum of the last historical detected visit amount in the historical detected visit amount sequence and the maximum first difference among the first differences as the predicted visit amount; If the historical detection visit volume in the historical detection visit volume sequence shows a decreasing trend; The last historical detected page views in the historical detected page views sequence is determined as the predicted page views.

4. 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 visit volume of each business, including: For each of the services, determining the number of cloud servers required for the service in the first preset time period based on the predicted traffic corresponding to the service, and establishing a mapping relationship between the service and the number of cloud servers; The mapping relationships corresponding to the services are arranged in order based on the priorities of the services to obtain the first cloud server management instruction.

5. The high-concurrency information platform performance optimization system according to claim 4 is characterized in that: The determining whether the physical server is abnormal based on the service processing duration information includes: Determining whether the business processing time is increasing based on the business processing time information; If there is an increasing trend, it is determined that the physical server has an abnormality; If there is no increasing trend, determining a target number of business processing durations in the business processing duration information that are greater than a preset business processing duration, and determining whether the target number is greater than a preset number; If the number is greater than a preset number, it is determined that an abnormality exists in the physical server; If it is not greater than the preset number, it is determined that there is no abnormality in the physical server.

6. The high-concurrency information platform performance optimization system according to claim 1, characterized in that: The second cloud server management instruction is a traffic information switching instruction corresponding to the abnormal physical server 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: For each of the second cloud server management instructions, switching 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; The cloud servers of the services corresponding to the respective mapping relationships are processed in sequence based on the mapping relationship sequence.

Images