WEB application accelerated access method based on cloud computer

Abstract

The invention discloses a WEB application accelerated access method based on a cloud computer, and relates to the technical field of WEB application management. Comprising the following steps: establishing a WEB application accelerated access system, monitoring page view data of WEB applications in a cloud computer application management platform through a data acquisition module, acquiring WEB application loading data, analyzing the acquired page view data and WEB application loading data information through an intelligent prediction module, and mining laws and trends in the data; and the resource use condition and the network topology information of each resource pool are obtained in real time through a WEB application server scheduling module, a scheduling instruction is sent to the determined resource pool equipment through the WEB application server scheduling module, and the WEB application server is scheduled to the resource pool.

Description

Technical Field

[0001] The invention discloses a WEB application accelerated access method based on a cloud computer, and relates to the technical field of WEB application management. Background Art

[0002] With the rapid development of cloud computing technology, cloud computing, as a new computing model, provides users with convenient and efficient access to computing resources. Cloud computing application management platforms integrate a variety of applications and services. Web applications, with their widespread use, provide cloud computing users with critical capabilities such as massive data management, information consultation, and process management. However, in practice, web application access within cloud computing application management platforms faces several challenges. For example, with the growing number of users and increasingly complex business needs, web application traffic is rapidly increasing. This leads to excessive load on web application servers, prolonged response times, and users waiting for content to load, severely impacting user efficiency and user experience. Furthermore, due to the randomness and uncertainty of the time and content of web application access, traditional static resource allocation methods struggle to flexibly adjust to actual access conditions and are unable to effectively address peak resource usage, further exacerbating web application access congestion. Summary of the Invention

[0003] In response to the problems of the prior art, the present invention provides a cloud computer-based WEB application accelerated access method, optimizes the WEB application access process of the cloud computer application management platform, improves the response speed and service quality of WEB applications, and meets users' growing information acquisition needs.

[0004] The specific scheme proposed by the present invention is:

[0005] The present invention provides a method for accelerating access to WEB applications based on cloud computers, comprising:

[0006] Establish a WEB application accelerated access system, which includes a data acquisition module, an intelligent prediction module, a WEB application server scheduling module, and a WEB application server management module.

[0007] The data collection module monitors the access data of WEB applications in the cloud computer application management platform. The access data includes access frequency and access time distribution; and collects WEB application loading data. The WEB application loading data includes the size of the loaded data and the source of the loaded data.

[0008] The intelligent prediction module analyzes the collected access data and WEB application loading data information to explore the patterns and trends in the data; based on the analysis results, the prediction algorithm is used to predict the hot WEB application information in the future, and the hot WEB application information prediction results are generated. The prediction results include the identification of the hot WEB application and the expected access volume, and the prediction results are passed to the WEB application server scheduling module.

[0009] The WEB application server scheduling module obtains the resource usage and network topology information of each resource pool in real time. Resource usage includes CPU usage, memory usage, and remaining storage space. Network topology information includes network delay and bandwidth between the resource pool and the user.

[0010] After receiving the prediction results, the system combines the user's network location information and determines the available resource pool closest to the user based on a preset scheduling strategy that takes into account resource usage, network topology information, and predicted traffic.

[0011] The WEB application server scheduling module sends a scheduling instruction to the determined resource pool device to schedule the WEB application server to the resource pool.

[0012] Furthermore, the WEB application accelerated access system established by the cloud computer-based WEB application accelerated access method also includes a continuous monitoring module, which continuously monitors the access status of the WEB application and the load status of the resource pool; when the monitored WEB application access status changes or the load of the resource pool becomes abnormal, the data collection module is triggered to re-collect data, and the intelligent prediction module re-predicts, and the WEB application server is re-scheduled according to the new prediction results by the WEB application server scheduling module.

[0013] Furthermore, the cloud computer-based WEB application accelerated access method preprocesses the collected data through a data acquisition module, and the preprocessing includes data cleaning, format unification and outlier removal.

[0014] Furthermore, the cloud computer-based WEB application accelerated access method performs service deployment feasibility verification on the target resource pool through the WEB application server scheduling module before scheduling the EB application server. The feasibility verification includes checking whether the storage capacity, computing power and network bandwidth of the resource pool meet the requirements.

[0015] Furthermore, the WEB application accelerated access system established by the cloud computer-based WEB application accelerated access method also includes establishing a WEB application access log recording module, which records the user's access operations, access time and access results of the WEB application through the WEB application access log recording module, so as to facilitate subsequent analysis and optimization of the intelligent prediction module.

[0016] The present invention also provides a WEB application accelerated access system based on cloud computers, including a data acquisition module, an intelligent prediction module, a WEB application server scheduling module, and a WEB application server management module.

[0017] The data collection module monitors the access data of WEB applications in the cloud computer application management platform. The access data includes access frequency and access time distribution; and collects WEB application loading data. The WEB application loading data includes the size of the loaded data and the source of the loaded data.

[0018] The intelligent prediction module analyzes the collected access data and WEB application loading data information to explore the patterns and trends in the data; based on the analysis results, it uses the prediction algorithm to predict the hot WEB application information in the future and generates the hot WEB application information prediction results, which include the identification of the hot WEB application and the expected access volume, and transmits the prediction results to the WEB application server scheduling module.

[0019] The WEB application server scheduling module obtains the resource usage and network topology information of each resource pool in real time. Resource usage includes CPU usage, memory usage, and remaining storage space. Network topology information includes network delay and bandwidth between the resource pool and the user.

[0020] After receiving the prediction results, the system combines the user's network location information and determines the available resource pool closest to the user based on a preset scheduling strategy that takes into account resource usage, network topology information, and predicted traffic.

[0021] The WEB application server scheduling module sends a scheduling instruction to the determined resource pool device to schedule the WEB application server to the resource pool.

[0022] Furthermore, the cloud computer-based WEB application accelerated access system also includes a continuous monitoring module, which continuously monitors the access status of the WEB application and the load status of the resource pool; when the monitored WEB application access status changes or the load of the resource pool becomes abnormal, the data collection module is triggered to re-collect data, and the intelligent prediction module re-predicts, and the WEB application server is re-scheduled by the WEB application server scheduling module according to the new prediction results.

[0023] Furthermore, the data acquisition module of the cloud computer-based WEB application accelerated access system preprocesses the collected data, and the preprocessing includes data cleaning, format unification and outlier removal.

[0024] Furthermore, the WEB application server scheduling module of the cloud computer-based WEB application accelerated access system performs service deployment feasibility verification on the target resource pool before scheduling the EB application server. The feasibility verification includes checking whether the storage capacity, computing power and network bandwidth of the resource pool meet the requirements.

[0025] Furthermore, the cloud computer-based WEB application accelerated access system also includes establishing a WEB application access log recording module, which records the user's access operations, access time and access results of the WEB application through the WEB application access log recording module, so as to facilitate subsequent analysis and optimization of the intelligent prediction module.

[0026] The benefits of the present invention are:

[0027] This platform provides a technical solution to address slow response times and heavy loads in web application access on cloud computing application management platforms. It optimizes the web application access process on cloud computing application management platforms, improving web application response speed and service quality to meet users' growing demand for information. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION

[0029] The present invention will be further described below with reference to the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it. However, the embodiments are not intended to limit the present invention.

[0030] Example 1

[0031] The present invention provides a method for accelerating access to WEB applications based on cloud computers, comprising:

[0032] Establish a WEB application accelerated access system, which includes a data acquisition module, an intelligent prediction module, a WEB application server scheduling module, and a WEB application server management module.

[0033] The data acquisition module is used to monitor the access data of WEB applications in the cloud computer application management platform. The access data includes access frequency and access time distribution; and the WEB application loading data is collected. The WEB application loading data includes the size of the loaded data and the source of the loaded data. The collected data is preprocessed by the data acquisition module. The preprocessing includes data cleaning, format unification and outlier removal.

[0034] The intelligent prediction module analyzes the collected access data and WEB application loading data information to explore the patterns and trends in the data; based on the analysis results, the prediction algorithm is used to predict the hot WEB application information in the future, and the hot WEB application information prediction results are generated. The prediction results include the identification of the hot WEB application and the expected access volume, and the prediction results are passed to the WEB application server scheduling module.

[0035] The WEB application server scheduling module obtains the resource usage and network topology information of each resource pool in real time. Resource usage includes CPU usage, memory usage, and remaining storage space. Network topology information includes network delay and bandwidth between the resource pool and the user.

[0036] Before scheduling the EB application server, the WEB application server scheduling module verifies the feasibility of service deployment on the target resource pool. The feasibility verification includes checking whether the storage capacity, computing power and network bandwidth of the resource pool meet the requirements.

[0037] Based on the received prediction results and in combination with the user's network location information, determining the available resource pool closest to the user according to a preset scheduling strategy, wherein the scheduling strategy takes into account resource usage, network topology information, and predicted access volume;

[0038] The WEB application server scheduling module sends a scheduling instruction to the determined resource pool device to schedule the WEB application server to the resource pool.

[0039] The WEB application accelerated access system established also includes a continuous monitoring module, which continuously monitors the access status of the WEB application and the load status of the resource pool; when the monitored WEB application access status changes or the load of the resource pool becomes abnormal, the data collection module is triggered to re-collect data, and the intelligent prediction module re-predicts, and the WEB application server scheduling module re-schedules the WEB application server according to the new prediction results.

[0040] The established WEB application accelerated access system also includes establishing a WEB application access log recording module, which records the user's access operations, access time and access results to the WEB application for subsequent analysis and optimization of the intelligent prediction module.

[0041] Example 2

[0042] The present invention also provides a WEB application accelerated access system based on cloud computers, including a data acquisition module, an intelligent prediction module, a WEB application server scheduling module, and a WEB application server management module.

[0043] The data collection module monitors the access data of WEB applications in the cloud computer application management platform. The access data includes access frequency and access time distribution; and collects WEB application loading data. The WEB application loading data includes the size of the loaded data and the source of the loaded data.

[0044] The intelligent prediction module analyzes the collected access data and WEB application loading data information to explore the patterns and trends in the data; based on the analysis results, it uses the prediction algorithm to predict the hot WEB application information in the future and generates the hot WEB application information prediction results, which include the identification of the hot WEB application and the expected access volume, and transmits the prediction results to the WEB application server scheduling module.

[0045] The WEB application server scheduling module obtains the resource usage and network topology information of each resource pool in real time. Resource usage includes CPU usage, memory usage, and remaining storage space. Network topology information includes network delay and bandwidth between the resource pool and the user.

[0046] After receiving the prediction results, the system combines the user's network location information and determines the available resource pool closest to the user based on a preset scheduling strategy that takes into account resource usage, network topology information, and predicted traffic.

[0047] The WEB application server scheduling module sends a scheduling instruction to the determined resource pool device to schedule the WEB application server to the resource pool.

[0048] The information interaction, execution process and other contents between the modules in the above system are based on the same concept as the embodiment of the method of the present invention. For specific contents, please refer to the description in the embodiment of the method of the present invention and will not be repeated here.

[0049] Similarly, the system of the present invention provides a technical solution for slow response and heavy load of WEB application access in cloud computer application management platform. It optimizes the WEB application access process of cloud computer application management platform, improves the response speed and service quality of WEB application, and meets the growing information acquisition needs of users.

[0050] It should be noted that not all steps and modules in the above-mentioned processes and system structures are required, and certain steps or modules can be omitted according to actual needs. The execution order of each step is not fixed and can be adjusted as needed. The system structure described in the above-mentioned embodiments can be a physical structure or a logical structure, that is, some modules may be implemented by the same physical entity, or some modules may be implemented by multiple physical entities, or may be implemented by certain components in multiple independent devices.

[0051] The above embodiments are merely preferred embodiments for the purpose of fully illustrating the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are within the scope of protection of the present invention. The scope of protection of the present invention shall be subject to the claims.

Claims

1. A WEB application accelerated access method based on cloud computers, characterized by include: Establish a WEB application accelerated access system, which includes a data acquisition module, an intelligent prediction module, a WEB application server scheduling module, and a WEB application server management module. The data collection module monitors the access data of WEB applications in the cloud computer application management platform. The access data includes access frequency and access time distribution; and collects WEB application loading data. The WEB application loading data includes the size of the loaded data and the source of the loaded data. The intelligent prediction module analyzes the collected access data and WEB application loading data information to explore the patterns and trends in the data; based on the analysis results, the prediction algorithm is used to predict the hot WEB application information in the future, and the hot WEB application information prediction results are generated. The prediction results include the identification of the hot WEB application and the expected access volume, and the prediction results are passed to the WEB application server scheduling module. The WEB application server scheduling module obtains the resource usage and network topology information of each resource pool in real time. Resource usage includes CPU usage, memory usage, and remaining storage space. Network topology information includes network delay and bandwidth between the resource pool and the user. After receiving the prediction results, the system combines the user's network location information and determines the available resource pool closest to the user based on a preset scheduling strategy that takes into account resource usage, network topology information, and predicted traffic. The WEB application server scheduling module sends a scheduling instruction to the determined resource pool device to schedule the WEB application server to the resource pool.

2. A WEB application accelerated access method based on cloud computers according to claim 1, characterized in that The established WEB application accelerated access system also includes a continuous monitoring module, which continuously monitors the access status of the WEB application and the load status of the resource pool; when the monitored WEB application access status changes or the load of the resource pool becomes abnormal, the data collection module is triggered to re-collect data, and the intelligent prediction module re-predicts, and the WEB application server scheduling module re-schedules the WEB application server according to the new prediction results.

3. A WEB application accelerated access method based on cloud computer according to claim 1, characterized in that The collected data is preprocessed through the data acquisition module, and the preprocessing includes data cleaning, format unification and outlier removal.

4. A WEB application accelerated access method based on cloud computer according to claim 1, characterized in that Before scheduling the EB application server, the WEB application server scheduling module verifies the feasibility of service deployment in the target resource pool. The feasibility verification includes checking whether the storage capacity, computing power and network bandwidth of the resource pool meet the requirements.

5. A WEB application accelerated access method based on cloud computer according to claim 1, characterized in that The established WEB application accelerated access system also includes establishing a WEB application access log recording module, which records the user's access operations, access time and access results to the WEB application for subsequent analysis and optimization of the intelligent prediction module.

6. A WEB application accelerated access system based on cloud computers, characterized by Including data collection module, intelligent prediction module, WEB application server scheduling module, WEB application server management module, The data collection module monitors the access data of WEB applications in the cloud computer application management platform. The access data includes access frequency and access time distribution; and collects WEB application loading data. The WEB application loading data includes the size of the loaded data and the source of the loaded data. The intelligent prediction module analyzes the collected access data and WEB application loading data information to explore the patterns and trends in the data; based on the analysis results, it uses the prediction algorithm to predict the hot WEB application information in the future and generates the hot WEB application information prediction results, which include the identification of the hot WEB application and the expected access volume, and transmits the prediction results to the WEB application server scheduling module. The WEB application server scheduling module obtains the resource usage and network topology information of each resource pool in real time. Resource usage includes CPU usage, memory usage, and remaining storage space. Network topology information includes network delay and bandwidth between the resource pool and the user. After receiving the prediction results, the system combines the user's network location information and determines the available resource pool closest to the user based on a preset scheduling strategy that takes into account resource usage, network topology information, and predicted traffic. The WEB application server scheduling module sends a scheduling instruction to the determined resource pool device to schedule the WEB application server to the resource pool.

7. A WEB application accelerated access system based on cloud computers according to claim 6, characterized in that It also includes a continuous monitoring module, which continuously monitors the access status of the WEB application and the load status of the resource pool; when the monitored WEB application access status changes or the load of the resource pool becomes abnormal, the data collection module is triggered to re-collect data, and the intelligent prediction module re-predicts, and the WEB application server scheduling module re-schedules the WEB application server according to the new prediction results.

8. A WEB application accelerated access system based on cloud computers according to claim 6, characterized in that The data acquisition module preprocesses the collected data, which includes data cleaning, format unification and outlier removal.

9. A WEB application accelerated access system based on cloud computers according to claim 6, characterized in that Before scheduling the EB application server, the WEB application server scheduling module verifies the feasibility of service deployment on the target resource pool. The feasibility verification includes checking whether the storage capacity, computing power and network bandwidth of the resource pool meet the requirements.

10. A cloud computer-based WEB application accelerated access system according to claim 6, characterized in that It also includes establishing a WEB application access log recording module, which records the user's access operations, access time and access results to the WEB application for subsequent analysis and optimization of the intelligent prediction module.

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