Information system dynamic load balancing and maintaining method based on cloud computing

Abstract

The invention discloses an information system dynamic load balancing and maintaining method based on cloud computing, which belongs to the technical field of network communication, and comprises the following steps: S10, establishing a Web server cluster, presetting at least 100 Web servers included in the Web server cluster, and collecting access data of users accessing the preset Web servers, the access data comprising browsing data of the users; marking the collected users as reference users; s20, constructing a database based on the browsing data, and analyzing browsing features of different users in the database; according to the method, intelligent congestion degree evaluation and user request distribution are utilized, the congestion degree of the Web server is calculated in real time, and the user requests are dynamically distributed according to the congestion degree, so that the load of each server can be effectively balanced, the performance and the response speed of the whole system are improved, efficient operation and stable maintenance of the system are realized, and the service life of the system is prolonged. And powerful technical support is provided for an information system in a cloud computing environment.

Description

Technical Field

[0001] The present invention relates to the field of network communication technology, and in particular to a method for dynamic load balancing and maintenance of an information system based on cloud computing. Background Art

[0002] Load balancing is a key network technology in cloud computing environments, which aims to optimize resource allocation and improve system performance and availability. Through load balancing, network traffic can be distributed among multiple servers or computing nodes to ensure that each node can handle requests evenly, thereby avoiding single point failures and improving the overall performance of the system.

[0003] Load balancing technology mainly relies on load balancing algorithms and load balancers. The load balancing algorithm determines how traffic is distributed among multiple servers. Common algorithms include Round Robin, Weighted Round Robin, and Least Connections. The load balancer is responsible for forwarding requests to the appropriate server based on the algorithm.

[0004] Information system load balancing plays an important role in many industries. In the Internet and e-commerce, load balancing is used to distribute requests from users to different servers in a Web server cluster to ensure that the load of each server is relatively balanced and improve the performance and availability of the entire system. For example, during promotional activities, e-commerce websites use load balancing to distribute user requests to multiple Web servers to avoid overloading a single server and ensure the stable operation of the website.

[0005] In real life, the number of user visits to e-commerce websites during promotional activities is usually very large, especially during the peak period of promotional activities, when the number of user visits will increase sharply. However, when facing a huge number of user visits, the existing technology lacks the inadequacy of predicting the number of visits and / or the length of the visit at the front end, which makes it difficult to manage and maintain multiple Web server nodes as the number of visits increases. In particular, when dynamically expanding and reducing resources, it is difficult to dynamically adjust the request allocation strategy according to the current load / congestion situation, resulting in a large gap in the load / congestion of some Web servers. When processing large-scale data and analysis tasks, it is impossible to improve the data processing speed and efficiency, which in turn affects the content loading speed and user experience. Summary of the invention

[0006] In view of the above problems existing in the technical field of existing network communications, the present invention is proposed.

[0007] Therefore, one of the objects of the present invention is to provide a dynamic load balancing and maintenance method for an information system based on cloud computing, which utilizes intelligent congestion level assessment and user request allocation, calculates the congestion level of Web servers in real time, and dynamically allocates user requests according to the congestion level, thereby being able to effectively balance the load of each server, thereby improving the performance and response speed of the entire system, achieving efficient operation and stable maintenance of the system, and providing strong technical support for information systems in a cloud computing environment.

[0008] In order to solve the above technical problems, the present invention provides the following technical solutions: The method for dynamic load balancing and maintenance of information system based on cloud computing includes the following steps: S10: Establishing a Web server cluster, presetting the number of Web servers included in the Web server cluster to at least 100, collecting access data of users accessing the preset Web servers, wherein the access data includes browsing data of the users; and marking the collected users as reference users; S20: Building a database based on the browsing data, and analyzing browsing characteristics of different users in the database; the browsing characteristics include page browsing behavior, product browsing characteristics, interactive behavior, shopping cart and purchase behavior, device and network characteristics, and time characteristics; S30: calculating the congestion degree of each of the preset Web servers in the Web server cluster in real time, including calculating in a manner of performance indicator analysis, wherein the performance indicator analysis includes response time, throughput, number of concurrent users, and error rate; and dividing the preset Web servers into low congestion degree Web servers, medium congestion degree Web servers, and high congestion degree Web servers according to the calculation results; S40: Based on the congestion degree caused by the access data of the reference user to the preset Web server, the users are divided into browsing users, purchasing users and searching users; wherein the browsing users are marked as users causing a high congestion degree; the purchasing users are marked as users causing a medium congestion degree; and the searching users are marked as users causing a low congestion degree; according to the calculation result of the congestion degree of the preset Web server, when the reference user visits the Web server cluster in the future period, a Web server is allocated to the reference user based on the classification type of the reference user; S50: updating the congestion level of the preset Web server based on the allocation result, and updating the division result of the preset Web server according to the congestion level; when a user other than the reference user accesses the Web server cluster, the user is allocated to a Web server with a low congestion level according to the updated division result of the preset Web server; S60: monitoring and early warning management are performed on the preset Web servers in the Web server cluster, and based on the result of the update division of the preset Web servers, the congestion change of the Web servers with low congestion levels is calculated with a calculation cycle of every ten seconds, and when the load change shows an upward trend within three consecutive calculation cycles, a network congestion warning is issued to users who newly access the Web server cluster.

[0009] As a preferred solution of the present invention, wherein: in said S20, said page browsing behavior includes browsing depth, browsing time and page jump path; The product browsing characteristics include the number of product browsing times, product browsing time, and product category browsing; The interactive behaviors include click behaviors, search behaviors, and comment and evaluation browsing; The shopping cart and purchase behavior include shopping cart addition behavior, shopping cart residence time, and purchase conversion rate; The device and network characteristics include access device type and network environment; The time characteristics include access time and access frequency.

[0010] As a preferred solution of the present invention, wherein: in said S30, said response time includes first byte time and page loading time; wherein said first byte time includes the time from when the user initiates the request to when the first byte returned by the server is received; said page loading time includes the time from when the page starts loading to when all resources are completely loaded; The throughput includes the number of requests processed by the server per unit time; The number of concurrent users includes the number of user requests processed simultaneously by the server; The error rate includes the proportion of error responses returned by the server; Among them, the response time is preset based on the first byte time and the page loading time, the response time corresponding to the first byte time is ≤300 milliseconds; the response time corresponding to the page loading time is ≤4 seconds; if the time from the user initiating the request to the receipt of the first byte returned by the server and / or the time from the start of page loading to the completion of all resources loading exceeds the preset response time, it is determined that the corresponding preset Web server processes the request slowly and is in a congested operating state; otherwise, no determination is made.

[0011] As a preferred solution of the present invention, in said S40, said allocation method includes allocating said browsing users to a Web server with a low congestion level; allocating said purchasing users to a Web server with a medium congestion level; and allocating said searching users to a Web server with a high congestion level.

[0012] As a preferred solution of the present invention, wherein: based on the time characteristics, the reference users are divided into low-access users, medium-access users and high-access users; the low-access users, medium-access users and high-access users correspond to low access time and frequency, medium access time and frequency, and high access time and frequency respectively; when the high-access users access the Web server cluster in the future period, they are assigned to the low-congestion Web server; the medium-access users are assigned to the medium-congestion Web server; and the low-access users are assigned to the high-congestion Web server.

[0013] As a preferred solution of the present invention, among the reference users, the proportion of the number of high-access users to the number of reference users is calculated, and the records of the high-access users' historical access to the Web server cluster are collected; the 100 to 200 accesses with the longest access time are intercepted from the records, and the time pattern of the access is analyzed, and according to the change of the congestion level of the low-congestion Web server, the number of high-access users who access the Web server cluster in the future time period is calculated with each hour as the statistical period, and the time required for the low-congestion Web server to change into the medium-congestion Web server is calculated according to the number of users, and the result is calculated according to the following formula: ;in, Indicates When a high-access user accesses the Web server cluster, the low-congestion Web server The congestion change data bit / s collected each time; In the formula, Indicates that based on the proportion of the number of high-access users to the number of reference users, The calculated weight change is calculated in the The sum of the congestion change data; Indicates the average number of high-access users accessing the Web server cluster per hour; According to the result of the calculated required duration, the result is marked as a reference duration; if the remaining duration of the future period is less than half of the reference duration, a network congestion warning is issued to high-access users who newly access the Web server cluster.

[0014] As a preferred solution of the present invention, wherein: if the remaining duration of the future time period is not less than half of the reference duration, the 10 access times with the largest number of high-access users are obtained in the analyzed time pattern, and the access times are sorted according to the number of high-access users, and the intervals between different access times are analyzed; and the sum of the increased congestion change data bit / s is predicted based on the number of high-access users at different access times; based on the sum of the predicted data, if a certain access time in the future will cause the remaining duration to be less than half of the reference duration, a network congestion warning will be issued to high-access users accessing the Web server cluster when the corresponding access time arrives.

[0015] As a preferred solution of the present invention, the 10 access times are divided into several evaluation indicators, and in the time pattern, the weight of each evaluation indicator is calculated according to the 100 to 200 visits with the most access times, and a hierarchical maintenance plan corresponding to different levels is formulated according to the calculation results; the hierarchical maintenance plan includes dividing several evaluation indicators into 5 evaluation indicators with high weight ratios and 5 evaluation indicators with low weight ratios; among the 5 evaluation indicators with high weight ratios, the high-access users are allocated to the low-congestion Web servers; and among the 5 evaluation indicators with low weight ratios, the high-access users are allocated to the medium-congestion Web servers, and at the same time, the low-access users are restricted from accessing the Web server cluster.

[0016] A computer device, characterized in that it includes a processor, an input interface, an output interface and a memory, wherein the processor, input interface, output interface and memory are interconnected, wherein the memory is used to store a computer program, and the computer program includes program instructions. The processor is configured to call the program instructions to execute a dynamic load balancing and maintenance method for an information system based on cloud computing.

[0017] A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes a cloud computing-based information system dynamic load balancing and maintenance method.

[0018] Beneficial effects: 1. By calculating the congestion level of the Web server in real time and dynamically allocating user requests according to the congestion level, the load of each Web server can be effectively balanced to avoid overloading of a single Web server, thereby improving the performance and response speed of the entire system.

[0019] 2. Perform refined server allocation based on user type and access characteristics, and assign different types of user requests to Web servers with corresponding congestion levels, thereby achieving reasonable allocation and optimal utilization of resources and improving the utilization rate of Web server resources.

[0020] 3. By allocating high-access users to Web servers with low congestion, users can get faster response speeds and better browsing experience, especially during peak traffic periods, which can effectively alleviate the problem of long waiting time for users.

[0021] 4. Real-time monitoring and early warning management of Web servers. When abnormal changes in the congestion level of Web servers are detected, early warnings can be issued in a timely manner and corresponding measures can be taken, which enhances the stability and reliability of the system and reduces the risk of service interruption caused by Web server overload.

[0022] 5. The system can dynamically adjust the allocation strategy and congestion level division of Web servers according to the real-time congestion level and user access situation. It has good adaptability and flexibility and can cope with the ever-changing network environment and user needs.

[0023] 6. Through the automated congestion calculation and user allocation mechanism, the workload of manual intervention and maintenance is reduced, the system maintenance difficulty and operating costs are reduced, and the maintenance efficiency is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them: Figure 1 A schematic diagram of a method flow chart of an embodiment of the present invention; Figure 2 It is a schematic diagram of the process structure of an embodiment of the present invention. DETAILED DESCRIPTION

[0025] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0026] Because the existing technology lacks the ability to predict the number of visits and / or visit duration at the front end when facing a huge amount of user visits, it becomes difficult to manage and maintain multiple Web server nodes as the scale of visits increases. In particular, when dynamically expanding and reducing resources, it is difficult to dynamically adjust the request allocation strategy according to the current load / congestion situation, resulting in a large gap in the load / congestion of some Web servers. When processing large-scale data and analysis tasks, it is impossible to improve data processing speed and efficiency, which in turn affects content loading speed and user experience.

[0027] Based on this, the present invention proposes a dynamic load balancing and maintenance method for information systems based on cloud computing. It utilizes intelligent congestion level assessment and user request allocation, calculates the congestion level of Web servers in real time, and dynamically allocates user requests according to the congestion level. It can effectively balance the load of each server, thereby improving the performance and response speed of the entire system, achieving efficient operation and stable maintenance of the system, and providing strong technical support for information systems in a cloud computing environment.

[0028] The present invention is further described in detail below through embodiments and in conjunction with the accompanying drawings.

[0029] Reference Figure 1 to Figure 2 , is an embodiment of the present invention, which provides a method for dynamic load balancing and maintenance of an information system based on cloud computing, comprising the following steps: S10: Establish a Web server cluster, preset the number of Web servers included in the Web server cluster to be at least 100, collect access data of users accessing the preset Web servers, the access data including browsing data of the users; and mark the collected users as reference users; It should be noted that this includes access data collected within at least one year; S20: Building a database based on the browsing data, and analyzing the browsing characteristics of different users in the database; the browsing characteristics include page browsing behavior, product browsing characteristics, interaction behavior, shopping cart and purchase behavior, device and network characteristics, and time characteristics; It should be noted that in this embodiment, in S20, the page browsing behavior includes browsing depth, browsing time and page jump path; It should be noted that browsing depth includes the number of pages visited by users; users with higher browsing depth may be more interested in the website content and browse multiple product detail pages, category pages, etc. Browsing time refers to the time users stay on a website. A longer browsing time indicates that users pay more attention to the website content and may be carefully selecting products or reading related information. Page jump path refers to the sequence of pages that users visit on a website. For example, a user may jump from the homepage to the category page, then to the product detail page, and finally to the shopping cart page. Analyzing the jump path helps us understand the user's shopping intentions and behavior habits. Product browsing characteristics include product browsing times, product browsing duration, and product category browsing; It should be noted that the number of product views refers to the frequency of users viewing a product. Products with high number of views may be potential purchase targets or popular products of users. Product browsing time refers to the time users spend on the product details page. A longer browsing time may mean that users have conducted in-depth browsing of product details, reviews, and other information. Product category browsing refers to the behavior of users browsing different product categories. For example, users may browse the clothing category first and then the electronic product category. This helps us understand the user's interest range and shopping needs. Interaction behaviors include click behaviors, search behaviors, and comment and review browsing; It should be noted that click behavior refers to the click operation of users on the website, including clicks on products, categories, search boxes, navigation bars, etc. Click behavior can reflect the user's focus and operation habits; Search behavior refers to the frequency with which users use the website search function and the keywords they search for; search behavior reveals users’ shopping intent and demand for specific products; Review and evaluation browsing refers to the behavior of users browsing product reviews and evaluations, which indicates that users will refer to other consumers' feedback and experience before making a purchase decision; Shopping cart and purchase behavior include shopping cart addition behavior, shopping cart dwelling time and purchase conversion rate; Device and network characteristics include access device type and network environment; It should be noted that users may have different browsing habits and needs based on the type of device they use, such as mobile phones, tablets, PCs, etc. The user's network connection speed and stability, network environment affects page loading speed and user experience, which in turn affects the user's browsing behavior; Temporal characteristics include visit time and visit frequency; It should be noted that the access time is the time period when the user visits the website; for example, the user may visit the website in the evening on weekdays or during the day on weekends, which helps to understand the user's shopping time preference; The access frequency is the number of times a user visits a website within a certain period of time. A high access frequency indicates that the user has a high loyalty and demand for the website. This embodiment further distinguishes reference users into low-access users, medium-access users and high-access users based on time characteristics; low-access users, medium-access users and high-access users correspond to low access time and frequency, medium access time and frequency, and high access time and frequency respectively; when high-access users access the Web server cluster in the future period, they are allocated to Web servers with low congestion; medium-access users are allocated to Web servers with medium congestion; and low-access users are allocated to Web servers with high congestion; S30: calculating the congestion degree of each preset Web server in the Web server cluster in real time, including calculating in accordance with a performance indicator analysis method, wherein the performance indicator analysis includes response time, throughput, number of concurrent users, and error rate; and dividing the preset Web servers into low congestion degree Web servers, medium congestion degree Web servers, and high congestion degree Web servers according to the calculation results; Specifically, in S30, the response time includes the first byte time and the page load time; wherein the first byte time includes the time from when the user initiates the request to when the first byte returned by the server is received; the page load time includes the time from when the page starts loading to when all resources are fully loaded; Throughput includes the number of requests processed by the server per unit time; The number of concurrent users includes the number of user requests processed by the server at the same time; The error rate includes the proportion of error responses returned by the server; The response time is preset based on the first byte time and the page loading time. The response time corresponding to the first byte time is ≤300 milliseconds; the response time corresponding to the page loading time is ≤4 seconds. If the time from the user initiating the request to the receipt of the first byte returned by the server and / or the time from the start of page loading to the completion of all resources loading exceeds the preset response time, it is determined that the corresponding preset Web server processes the request slowly and is in a congested operation state; otherwise, no determination is made. S40: Based on the congestion degree caused by the access data of the reference user to the preset Web server, the users are divided into browsing users, purchasing users and searching users; wherein the browsing users are marked as users causing a high congestion degree; the purchasing users are marked as users causing a medium congestion degree; and the searching users are marked as users causing a low congestion degree; according to the calculation result of the congestion degree of the preset Web server, when the reference user accesses the Web server cluster in the future period, a Web server is allocated to the reference user based on the classification type of the reference user; It should be emphasized in this embodiment that in S40, the allocation method includes allocating browsing users to Web servers with low congestion levels; allocating purchasing users to Web servers with medium congestion levels; and allocating search users to Web servers with high congestion levels. It should be noted in this embodiment that browsing users mainly browse products and information on the website, but have a low willingness to buy. They may be interested in multiple products and spend more time browsing product detail pages, evaluation pages, etc., but rarely add products to shopping carts or make purchases. Browsing users may be in the information collection stage, comparing and researching products, but have not yet made a purchase decision, so they are assigned to Web servers with low congestion levels; Purchasing users have clear purchasing purposes. When browsing products, they pay more attention to information such as prices, specifications, and inventory. They will add products to the shopping cart and finally complete the purchase. The browsing path of this type of users is usually relatively direct. They quickly jump from the product classification page or search result page to the product details page, and then enter the shopping cart and checkout page. Purchasing users have a relatively short browsing time, but a high conversion rate. Therefore, they are assigned to Web servers with medium congestion levels. Search users use the website's search function to find specific products or information. They usually enter keywords in the search box and then browse the products on the search results page. This type of user's browsing behavior is relatively concentrated, focusing on products related to the search keywords and browsing other products less frequently. Search users may have clear requirements for brands, functions, or prices, and will filter and compare based on search results; therefore, they are assigned to Web servers with high congestion levels; S50: updating the congestion level of the preset Web server based on the allocation result, and updating the division result of the preset Web server according to the congestion level; when a user other than the reference user accesses the Web server cluster, the user is allocated to a Web server with a low congestion level according to the updated division result of the preset Web server; S60: monitoring and early warning management of the preset Web servers in the Web server cluster, and based on the result of the update division of the preset Web servers, taking the Web servers with low congestion levels as the basis, calculating the congestion change of the Web servers with low congestion levels with a calculation cycle of every ten seconds, and when the load change shows an upward trend within three consecutive calculation cycles, issuing a network congestion warning to the users who newly access the Web server cluster; This embodiment also includes: among the reference users, calculating the proportion of the number of high-access users to the number of reference users, and collecting the records of the high-access users' historical access to the Web server cluster; intercepting the 100 to 200 visits with the longest access time in the records, analyzing the time pattern of the access, and calculating the number of high-access users who will access the Web server cluster in the future time period based on the change of the congestion level of the low-congestion Web server with each hour as the statistical period, and calculating the time required for the low-congestion Web server to change into the medium-congestion Web server based on the number of users, and calculating it according to the following formula: ;in, Indicates When a high-access user accesses the Web server cluster, the low-congestion Web server The congestion change data bit / s collected each time; In the formula, Indicates that based on the proportion of the number of high-access users to the number of reference users, The calculated weight change is calculated in the The sum of the congestion change data; Indicates the average number of high-access users accessing the Web server cluster per hour; According to the result of the calculated required time, the result is marked as the reference time. If the remaining time in the future period is less than half of the reference time, a network congestion warning is issued to the high-access users who newly access the Web server cluster. On the basis of the above, this embodiment obtains the 10 access times with the largest number of high-access users in the analyzed time pattern if the remaining time of the future time period is not less than half of the reference time, and sorts the access times according to the number of high-access users, and analyzes the intervals of different access times; and predicts the sum of the increased congestion change data bit / s according to the number of high-access users at different access times; based on the sum of the predicted data, if a certain access time in the future will cause the remaining time to be less than half of the reference time, then a network congestion warning is issued to the high-access users accessing the Web server cluster when the corresponding access time arrives; This embodiment further divides the 10 access times into several evaluation indicators, calculates the weight of each evaluation indicator according to the 100 to 200 accesses with the most access times in the time pattern, and formulates hierarchical maintenance plans corresponding to different levels according to the calculation results; the hierarchical maintenance plan includes dividing the several evaluation indicators into 5 evaluation indicators with high weight ratios and 5 evaluation indicators with low weight ratios; among the 5 evaluation indicators with high weight ratios, high-access users are allocated to low-congestion Web servers; and among the 5 evaluation indicators with low weight ratios, high-access users are allocated to medium-congestion Web servers, and low-access users are restricted from accessing the Web server cluster.

[0030] A computer device, characterized in that it includes a processor, an input interface, an output interface and a memory, the processor, the input interface, the output interface and the memory are interconnected, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is configured to call the program instructions to execute a dynamic load balancing and maintenance method for an information system based on cloud computing.

[0031] A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program, the computer program includes program instructions, and when the program instructions are executed by a processor, the processor executes a dynamic load balancing and maintenance method for an information system based on cloud computing.

[0032] In summary, the present invention utilizes intelligent congestion level assessment and user request allocation, calculates the congestion level of Web servers in real time, and dynamically allocates user requests according to the congestion level, which can effectively balance the load of each server, thereby improving the performance and response speed of the entire system, achieving efficient operation and stable maintenance of the system, and providing strong technical support for information systems in cloud computing environments.

[0033] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for dynamic load balancing and maintenance of information systems based on cloud computing, characterized in that: The following steps are involved: S10: Establishing a Web server cluster, presetting the number of Web servers included in the Web server cluster to at least 100, collecting access data of users accessing the preset Web servers, wherein the access data includes browsing data of the users; and marking the collected users as reference users; S20: Building a database based on the browsing data, and analyzing browsing characteristics of different users in the database; the browsing characteristics include page browsing behavior, product browsing characteristics, interactive behavior, shopping cart and purchase behavior, device and network characteristics, and time characteristics; S30: calculating the congestion degree of each of the preset Web servers in the Web server cluster in real time, including calculating in a manner of performance indicator analysis, wherein the performance indicator analysis includes response time, throughput, number of concurrent users, and error rate; and dividing the preset Web servers into low congestion degree Web servers, medium congestion degree Web servers, and high congestion degree Web servers according to the calculation results; S40: Based on the congestion degree caused by the access data of the reference user to the preset Web server, the users are divided into browsing users, purchasing users and searching users; wherein the browsing users are marked as users causing a high congestion degree; the purchasing users are marked as users causing a medium congestion degree; and the searching users are marked as users causing a low congestion degree; according to the calculation result of the congestion degree of the preset Web server, when the reference user visits the Web server cluster in the future period, a Web server is allocated to the reference user based on the classification type of the reference user; S50: updating the congestion level of the preset Web server based on the allocation result, and updating the division result of the preset Web server according to the congestion level; when a user other than the reference user accesses the Web server cluster, the user is allocated to a Web server with a low congestion level according to the updated division result of the preset Web server; S60: monitoring and early warning management are performed on the preset Web servers in the Web server cluster, and based on the result of the update division of the preset Web servers, the congestion change of the Web servers with low congestion levels is calculated with a calculation cycle of every ten seconds, and when the load change shows an upward trend within three consecutive calculation cycles, a network congestion warning is issued to users who newly access the Web server cluster.

2. The method for dynamic load balancing and maintenance of information systems based on cloud computing according to claim 1, characterized in that: In S20, the page browsing behavior includes browsing depth, browsing time and page jump path; The product browsing characteristics include the number of product browsing times, product browsing time, and product category browsing; The interactive behaviors include click behaviors, search behaviors, and comment and evaluation browsing; The shopping cart and purchase behavior include shopping cart addition behavior, shopping cart residence time, and purchase conversion rate; The device and network characteristics include access device type and network environment; The time characteristics include access time and access frequency.

3. The method for dynamic load balancing and maintenance of information systems based on cloud computing according to claim 1, characterized in that: In S30, the response time includes the first byte time and the page loading time; wherein the first byte time includes the time from the user initiating the request to the receipt of the first byte returned by the server; the page loading time includes the time from the start of page loading to the completion of the complete loading of all resources; The throughput includes the number of requests processed by the server per unit time; The number of concurrent users includes the number of user requests processed simultaneously by the server; The error rate includes the proportion of error responses returned by the server; Among them, the response time is preset based on the first byte time and the page loading time, the response time corresponding to the first byte time is ≤300 milliseconds; the response time corresponding to the page loading time is ≤4 seconds; if the time from the user initiating the request to the receipt of the first byte returned by the server and / or the time from the start of page loading to the completion of all resources loading exceeds the preset response time, it is determined that the corresponding preset Web server processes the request slowly and is in a congested operating state; otherwise, no determination is made.

4. The method for dynamic load balancing and maintenance of information systems based on cloud computing according to claim 1, characterized in that: In S40, the allocation method includes allocating the browsing users to a Web server with a low congestion level; allocating the purchasing users to a Web server with a medium congestion level; and allocating the searching users to a Web server with a high congestion level.

5. The method for dynamic load balancing and maintenance of information systems based on cloud computing according to claim 2, characterized in that: Based on the time feature, the reference users are divided into low-access users, medium-access users and high-access users; the low-access users, medium-access users and high-access users correspond to low access time and frequency, medium access time and frequency, and high access time and frequency, respectively; When the high-access users access the Web server cluster in the future, they will be allocated to the Web servers with low congestion levels; the medium-access users will be allocated to the Web servers with medium congestion levels; and the low-access users will be allocated to the Web servers with high congestion levels.

6. The method for dynamic load balancing and maintenance of information systems based on cloud computing according to claim 5, characterized in that: Among the reference users, the proportion of the number of high-access users to the number of reference users is calculated, and the records of the high-access users' historical access to the Web server cluster are collected; the 100 to 200 visits with the longest access time are intercepted from the records, and the time pattern of the access is analyzed, and according to the change of the congestion level of the low-congestion Web server, the number of high-access users who access the Web server cluster in the future time period is calculated with each hour as the statistical period, and the time required for the low-congestion Web server to change into the medium-congestion Web server is calculated according to the number of users, and the result is calculated according to the following formula: ;in, Indicates When a high-access user accesses the Web server cluster, the low-congestion Web server The congestion change data bit / s collected each time; In the formula, Indicates that based on the proportion of the number of high-access users to the number of reference users, The calculated weight change is calculated in the The sum of the congestion change data; Indicates the average number of high-access users accessing the Web server cluster per hour; According to the result of the calculated required duration, the result is marked as a reference duration; if the remaining duration of the future period is less than half of the reference duration, a network congestion warning is issued to high-access users who newly access the Web server cluster.

7. The method for dynamic load balancing and maintenance of information systems based on cloud computing according to claim 6, characterized in that: If the remaining duration of the future time period is not less than half of the reference duration, the 10 access times with the largest number of high-access users are obtained from the analyzed time pattern, and the access times are sorted according to the number of high-access users, and the intervals between different access times are analyzed; and the sum of the increased congestion change data bit / s is predicted based on the number of high-access users at different access times; based on the sum of the predicted data, if a certain access time in the future will cause the remaining duration to be less than half of the reference duration, a network congestion warning will be issued to high-access users accessing the Web server cluster when the corresponding access time arrives.

8. The method for dynamic load balancing and maintenance of information systems based on cloud computing according to claim 7, characterized in that: The 10 access times are divided into a number of evaluation indicators, and in the time pattern, the weight of each evaluation indicator is calculated according to the intercepted 100 to 200 visits with the most access times, and a hierarchical maintenance plan corresponding to different levels is formulated according to the calculation results; the hierarchical maintenance plan includes dividing the several evaluation indicators into 5 evaluation indicators with high weight ratios and 5 evaluation indicators with low weight ratios; among the 5 evaluation indicators with high weight ratios, the high-access users are allocated to the Web server with low congestion level; Among the five evaluation indicators with low weights, the high-access users are allocated to the Web servers with medium congestion levels, while the low-access users are restricted from accessing the Web server cluster.

9. A computer device, characterized in that: The method comprises a processor, an input interface, an output interface and a memory, wherein the processor, the input interface, the output interface and the memory are interconnected, wherein the memory is used to store a computer program, the computer program comprises program instructions, and the processor is configured to call the program instructions to execute the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program includes program instructions, and when the program instructions are executed by a processor, the processor is enabled to perform the method according to any one of claims 1 to 8.

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