A cloud-based internet information service system and method

Abstract

The application relates to the technical field of cloud platform information service, in particular to an internet information service system and method based on a cloud platform, which comprises the following steps: acquiring CPU occupancy, memory occupancy and response time of each server of the cloud platform, and the traffic of the cloud platform accepting requests, and judging the update time point of the server weight in each period; analyzing the correlation between the CPU occupancy, memory occupancy and response time of the server in each period, so as to acquire the load synchronization coefficient of the server in each period; acquiring the memory load coefficient and the CPU load coefficient through the fluctuation and change trend of the memory occupancy and the CPU occupancy; acquiring the comprehensive load coefficient in combination with the load synchronization coefficient, so as to construct the weight adjustment coefficient of each server in each period, and acquire the server weight of each server in the next period of each period. The application can improve the rationality and accuracy of the request distribution of the cloud platform.

Description

Technical Field

[0001] This application relates to the field of cloud platform information service technology, specifically to an internet information service system and method based on a cloud platform. Background Technology

[0002] A cloud platform's internet information service system is a network service architecture built on cloud computing technology. It provides users with various internet-based information services by highly virtualizing and centralizing computing, storage, and network resources. Cloud platform information service systems are typically deployed across multiple data centers, which maintain high-speed, stable network connections to ensure users can quickly access the service system. When a large number of users request access, the cloud platform's load balancing function can distribute these requests reasonably across different servers, preventing a single server from overloading and crashing due to excessive requests.

[0003] During application, the number of requests received by the system and the server load status are dynamically changing. Conventional load balancing algorithms fail to fully consider the situation of multiple user accesses, resulting in unreasonable allocation of requests by the cloud platform and reducing the computing performance of the information service system. Publication No. CN113938488B proposes a load balancing method based on dynamic and static weighted round-robin. This method generates node performance weights based on the performance parameters of each node in the server cluster. It only combines the server load parameters and node performance weights to statically or dynamically adjust the node performance weights, ignoring the specific changing characteristics of the request volume and server load status. This makes it difficult to handle sudden surges in request traffic in a timely and efficient manner, and the accuracy of server weight adjustment is insufficient. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide an internet information service system and method based on a cloud platform, the specific technical solution of which is as follows:

[0005] This application provides an internet information service method based on a cloud platform, comprising the following steps:

[0006] Obtain the CPU utilization, memory utilization, and response time of each server on the cloud platform at each sampling time within each time period, as well as the traffic of requests received by the cloud platform at each sampling time.

[0007] Based on the comparison results of all traffic data before each sampling time with the average level of all traffic data in the historical period, as well as the sudden changes in traffic at each sampling time, the update time point of the server weight in each period is determined.

[0008] The correlation between CPU utilization, memory utilization and response time of the server in each time period is analyzed to obtain the load synchronization coefficient of the server in each time period. The memory load coefficient and CPU load coefficient of the server in each time period are obtained by observing the fluctuation and trend of the memory utilization and CPU utilization of the server in each time period. Combined with the load synchronization coefficient, the comprehensive load coefficient of the server in each time period is obtained.

[0009] Analyze the differences in the overall load coefficient between each server and other servers in different time periods, construct the weight adjustment coefficient of each server in each time period, and then update the server weight of each server in the next time period.

[0010] Preferably, the update time points for determining the server weights in each time period include:

[0011] Calculate the cumulative sum of all traffic before the current sampling time, calculate the average of all traffic within a historical preset time period, and determine the update time point of the server weight for each time period based on the comparison between the cumulative sum obtained at the current sampling time and the average obtained within the historical preset time period, combined with the sudden changes in traffic data at the current sampling time.

[0012] Preferably, the update time point for determining the server weight in each time period further includes:

[0013] For each time period, if the cumulative sum obtained at the current sampling time is greater than the average value obtained in the historical preset time period, or if the traffic at the current sampling time is a sudden change position, then the current sampling time is taken as the update time point of the server weight within the time period; otherwise, the end time of the time period is taken as the update time point of the server weight; where the time period is a preset time length.

[0014] Preferably, obtaining the load synchronization coefficient of the server for each time period includes:

[0015] Calculate the Spearman correlation coefficients between CPU utilization, memory utilization and response time for each server in each time period, and use the sum of all Spearman correlation coefficients obtained for each server in each time period as the load synchronization coefficient for each server in each time period.

[0016] Preferably, obtaining the server's memory load coefficient and CPU load coefficient for each time period further includes:

[0017] For the memory usage of each server in each time period, calculate the product of the standard deviation and the mean of the server memory usage in each time period, and use a trend test algorithm to obtain the trend strength of the memory usage of each server in each time period. Based on the product and the trend strength, calculate the memory load coefficient of the server in each time period.

[0018] Accordingly, based on the CPU utilization of each server in each time period, the memory load coefficient of the server in each time period is calculated to obtain the CPU load coefficient of the server in each time period.

[0019] Preferably, the formula for calculating the server's memory load coefficient for each time period is: D i，t =B i，t ×exp(C i，t ), where exp() is an exponential function with base to the natural constant, D i，t Let B be the memory load coefficient of the i-th server in the t-th time period. i，t C is the product of the standard deviation and the mean of the memory utilization rate of the i-th server within the t-th time period. i，t The trend strength of the memory usage rate of the i-th server within the t-th time period.

[0020] Preferably, the formula for calculating the comprehensive load factor of the server in each time period is: G i，t =A i，t ×(D i，t +F i，t In the formula, G i，t Let A be the overall load factor of the i-th server in the t-th time period. i,t Let D be the load synchronization coefficient of the i-th server in the t-th time period. i，t F i，t These are the memory load coefficient and CPU load coefficient of the i-th server in the t-th time period, respectively.

[0021] Preferably, the construction of the weight adjustment coefficient for each server in each time period includes: calculating the sum of the differences between the comprehensive load coefficient of each server and the comprehensive load coefficient of other servers in each time period, and using it as the weight adjustment coefficient for each server in each time period.

[0022] Preferably, updating the server weights of each server for the next time period further includes:

[0023] The normalized results of the absolute values ​​of the weight adjustment coefficients of each server in each time period are statistically analyzed. The sign of each normalized result is adjusted, and the result after adjustment is used as the weight adjustment amount of each server in each time period. The sign of each normalized result is set to be the opposite of the sign of the weight adjustment coefficient.

[0024] The sum of the server weights and their weight adjustments for each server in each time period is used as the server weights for each server in the next time period.

[0025] This application also provides an internet information service system based on a cloud platform, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0026] As can be seen from the above, the cloud-based internet information service system and method provided in this application have at least the following beneficial effects:

[0027] The Internet information service system described in this application comprises an infrastructure layer, a platform layer, and a software service layer. Specifically, addressing the potential issue of uneven load distribution during user request allocation at the platform layer, the weight update time is dynamically adjusted by analyzing the abrupt changes in traffic data received from requests. Compared to conventional fixed-duration adjustment methods, its advantage lies in its ability to handle sudden surges in requests promptly and improve load distribution efficiency.

[0028] Based on the dynamic adjustment of weight update time points, the status of each server in each time period is analyzed. The correlation between CPU utilization, memory usage and response time in each time period is analyzed, as well as the fluctuation and trend of CPU utilization and memory usage data. Based on the differences in the overall load between servers, the weight adjustment coefficient of each server in each time period is constructed to optimize the server weight. Timely and dynamic perception of the actual load of the server helps to improve the rationality of request allocation by the cloud platform and the computing performance of the service system. Attached Figure Description

[0029] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 A flowchart illustrating the steps of an internet information service method based on a cloud platform provided in this application. Detailed Implementation

[0031] To further illustrate the technical means and effects adopted by this application to achieve the intended inventive purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an internet information service system and method based on a cloud platform proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0032] Unless otherwise specified and limited, terms such as “comprising,” “including,” or any other variations thereof are intended to cover a non-exclusive inclusion, such that a circuit structure, article, or device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such an article or device. Without further limitation, an element defined by the phrase “comprising one…” does not exclude the presence of other identical elements in the article or device that includes said element. Furthermore, the term “and / or” as used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0033] Please see Figure 1 The diagram illustrates a flowchart of a cloud-based internet information service method according to an embodiment of this application. The specific solution of the cloud-based internet information service system and method provided in this application will be described in detail below with reference to the accompanying drawings:

[0034] First, in this embodiment, the cloud platform-based internet information service system consists of the following parts:

[0035] Part One, Infrastructure Layer:

[0036] The infrastructure layer includes the basic hardware and system services required by the Internet information service system, including but not limited to servers, network equipment, and storage facilities.

[0037] The server contains the core hardware for system operation, handling various information service requests. When a user accesses a web application, the computing server executes relevant code logic to generate webpage content and return it to the user. Server performance varies depending on system load requirements. The storage server also contains storage for large amounts of data, including but not limited to user-uploaded files and database information. In this embodiment, the server uses a disk array to provide data redundancy and high-performance storage.

[0038] In network devices, routers are responsible for forwarding data packets between the inside and outside of the cloud platform. They determine the data transmission path, and by configuring routing policies, routers can optimize network traffic, thereby ensuring efficient information transmission between different servers and users. Firewalls are crucial devices for ensuring system security. Their functions include, but are not limited to, blocking unauthorized network access, allowing only data packets that conform to security rules to enter or leave the system, and blocking access requests from specific malicious IP addresses to prevent hacker attacks. Switches are used to connect servers and other network devices, building internal networks. They can forward data packets to the correct device ports based on MAC addresses, enabling high-speed data exchange.

[0039] In storage facilities, block storage devices provide access to storage volumes, which can be used to store operating systems and applications. In a virtual hosting environment, each virtual machine can be allocated one or more block storage volumes to install the operating system and store data.

[0040] Part Two, Platform Layer:

[0041] The platform layer provides developers with a complete development and runtime environment, which in this embodiment includes an operating system, a database management system, and middleware. The platform layer also supports upper-layer applications and services, while the operating system manages server hardware resources. Relational and non-relational databases store various types of information for users. Application server middleware such as Tomcat and JBoss are important components of the platform layer, participating in the allocation of suitable servers to users. In a web server cluster, all servers run an operating system based on the platform layer to provide web services.

[0042] Furthermore, the platform layer supports full lifecycle management of applications, providing a one-stop management service from application creation, development, testing, deployment to operation and maintenance. Developers can quickly create application projects on the platform, use the platform's development tools for coding and debugging, perform testing through automated testing tools, and then deploy the application to the production environment with one click, monitoring and managing it using the platform's operation and maintenance tools. In addition to the basic development and runtime environment, the platform layer also provides a series of value-added services, including but not limited to database services, caching services, message queue services, and API management services. These services help developers build and manage applications more efficiently, improving application performance and reliability.

[0043] Part Three, Software Service Layer:

[0044] The main components of the software service layer include providing complete software applications, meeting diverse business needs, implementing multi-tenant architecture, and providing continuous updates and maintenance.

[0045] Users do not need to install or maintain software; they can access the software applications provided by the software service layer simply by connecting to the network. This software service layer covers various business areas, including but not limited to office automation, financial management, human resource management, customer relationship management, and project management. Users can choose appropriate software service layer applications based on their business needs to quickly achieve digital transformation. Furthermore, multiple users share the same set of application instances and resources during application, but each user's data and configurations are isolated. This architecture improves resource utilization, reduces operating costs, and provides users with a consistent user experience.

[0046] The software service layer also performs automatic software updates and maintenance, so users don't need to worry about software version upgrades or vulnerability fixes. Providers can regularly release new features and improvements, and users can automatically receive the latest software version, ensuring they always have access to the most up-to-date and secure software.

[0047] Through the coordinated work among the aforementioned layers, the efficient and effective operation of the cloud platform's internet information service system can be achieved.

[0048] Furthermore, in this embodiment, the specific method for providing internet information services on the cloud platform includes the following steps at the sampling time:

[0049] Step 1: Obtain the CPU utilization, memory utilization, and response time of each server on the cloud platform at each sampling time within each time period, as well as the traffic of requests received by the cloud platform at each sampling time.

[0050] Users continuously send requests to the server system while using cloud platform services. Due to the massive and explosive growth in the number of users, higher demands are placed on the servers. To provide high performance and high availability, server clusters have become the preferred efficient and cost-effective solution. Load balancing becomes a key focus when discussing server clusters. Load balancing technology distributes concurrent requests to multiple servers by balancing the load of backend services, efficiently utilizing all backend servers and improving server cluster performance. This embodiment uses a weighted round-robin approach for load balancing.

[0051] In load balancing, servers are typically assigned different weights based on their processing capacity, and client requests are distributed to each server sequentially according to these weights. The weight value represents the server's processing capacity or priority; a higher priority indicates a server with stronger processing capacity and should receive more requests. However, conventional service systems fail to dynamically perceive the actual load of servers in a timely manner when allocating requests. This can lead to a situation where a server, even with a high actual load, receives more requests simply because of its high priority. Furthermore, the performance of the weighted round-robin method is highly dependent on priority. If a server's priority is significantly higher than other servers, it will handle the majority of requests, even if its actual processing capacity is not significantly higher. Additionally, conventional load balancing methods update weights at fixed intervals, making it difficult to handle sudden traffic spikes. Therefore, this embodiment optimizes and adjusts server weights by comprehensively analyzing server performance and request traffic patterns.

[0052] To obtain various performance parameters of the server, this embodiment uses the system's monitoring tool to obtain CPU utilization, memory utilization, and response time data of each server at each sampling time within each time period. In this embodiment, a time period is set to 10 minutes, and the time interval for collecting various data is set to 1 second. At the same time, in this embodiment, to obtain the traffic status of the system receiving requests, the monitoring tool can collect the traffic data of requests received by the service system every 1 second.

[0053] Step 2: Based on the comparison results of all traffic data before each sampling time with the average level of all traffic data in the historical period, as well as the sudden changes in traffic at each sampling time, determine the update time point of the server weight in each period.

[0054] During the operation of an information service system, sudden surges in requests may occur, significantly increasing the overall system load. However, existing conventional load balancing methods typically update server weights based on fixed time intervals. During peak request traffic periods, this fixed-time weight update method may lead to a large number of requests converging on servers with higher weights, failing to handle sudden surges in requests efficiently and effectively, and thus easily causing uneven load distribution among servers. Therefore, this embodiment employs a strategy of dynamically adjusting the server weight update time to optimize request distribution and improve system load balance.

[0055] Before the first optimization, the service system updated weights at fixed intervals, using the sampling time of the last weight update as the starting point for subsequent data processing. During peak request traffic periods, the collected traffic data exhibited a rapid increase within a short period, resulting in noticeable abrupt changes. Therefore, abrupt change detection was performed on all traffic data prior to the current sampling time, specifically using the Pettitt algorithm with a significance level of 0.05. The algorithm outputs the p-value for significance testing; if the p-value is less than the set significance level, a sudden change in traffic data is considered to have occurred. This abrupt change location may serve as the time point for server weight updates. Furthermore, request traffic may remain consistently high without significant abrupt changes. Therefore, the weight update time is set by combining the total request traffic from the initial sampling time to the current sampling time with the abrupt change location.

[0056] Therefore, the cumulative sum of all traffic before the current sampling time is calculated. In this embodiment, the cumulative sum is compared with the average of all traffic data over a historical one-week period. This embodiment sets the maximum adjustment time to 10 minutes, meaning each 10-minute period is considered a time interval. If the cumulative sum calculated at the current sampling time is greater than the average, or if the traffic at the current sampling time is at a sudden change point, then the current sampling time is used as the update time point for the server weights of the corresponding time interval; otherwise, the end time of the time interval in which the current sampling time falls, i.e., the end of the 10-minute period, is used as the update time point for the server weights. This timely updating of server weights helps improve the balance of load distribution.

[0057] This allows us to obtain the update time of server weights and divide the time into multiple consecutive time periods. When a sudden surge in requests occurs, this method can adjust the server weights in advance. Since server load varies and different servers have different loads, the weights of all servers in the next time period can be optimized based on the load levels and differences of each server in the previous time period, which helps the service system to allocate requests in a timely and efficient manner.

[0058] Step 3: Analyze the correlation between CPU utilization, memory utilization and response time of the server in each time period to obtain the load synchronization coefficient of the server in each time period. By analyzing the fluctuation and trend of the server's memory utilization and CPU utilization in each time period, obtain the memory load coefficient and CPU load coefficient of the server in each time period. Combine the load synchronization coefficient to obtain the comprehensive load coefficient of the server in each time period.

[0059] Further evaluation of server performance in the previous time period was conducted using CPU utilization, memory utilization, and response time. These data exhibit certain correlations, collectively reflecting the server's request processing capabilities. Response time refers to the total time from when all requests are sent by the client to when the server returns a response. Higher CPU utilization indicates that the server is performing more computational tasks, while higher memory utilization indicates that the server is heavily consuming storage space while processing requests. Therefore, longer response times and higher CPU or memory utilization will reduce remaining data processing capacity.

[0060] Different types of requests have varying CPU and memory usage levels. Computation-intensive requests have higher CPU usage and lower memory usage, while I / O-intensive requests have higher memory usage and lower CPU usage. Furthermore, different servers exhibit different performance characteristics. Additionally, a server may experience longer response times due to network fluctuations or short-term hardware issues, yet still possess sufficient request processing capabilities. Therefore, relying solely on CPU usage, memory usage, or response time cannot accurately assess a server's ability to handle subsequent requests. A stronger positive correlation between CPU or memory usage and response time within a given time period indicates greater synchronization among the server's components. In this embodiment, taking the i-th server as an example, the correlation between CPU usage, memory usage, and response time for the i-th server in each time period is calculated. This correlation can be calculated using Spearman's correlation coefficient or Kendall's rank correlation coefficient; in this embodiment, Spearman's correlation coefficient is used. The sum of all Spearman correlation coefficients obtained from the i-th server in each time period is taken as the load synchronization coefficient of the i-th server in each time period. The load synchronization coefficient of the i-th server in the t-th time period is denoted as A. i，t The larger the load synchronization coefficient, the more synchronized the operating status of each component in the server.

[0061] Furthermore, by combining the magnitude and variation characteristics of server CPU utilization and memory utilization, the current load status is obtained. Taking memory utilization as an example, if the memory utilization is more stable and its mean is larger in each time period, or if the increasing trend of memory utilization is more significant, it indicates a greater memory load. The product of the standard deviation and the mean of the memory utilization of each server in each time period is calculated to characterize the change of memory utilization of each server in each time period. Furthermore, the MK trend test algorithm is used to obtain the trend strength of the memory utilization of each server in each time period. The specific process is a well-known existing technology and will not be described in detail in this embodiment.

[0062] Based on the changes and trends in memory usage of each server over different time periods, the memory load coefficient of the i-th server is calculated. In this embodiment, the specific formula is: D i，t =B i，t ×exp(C i，t ), where exp() is an exponential function with the natural constant as its base, used to avoid interference from the positive and negative signs, D i,t B represents the memory load coefficient of the i-th server in the t-th time period, reflecting the memory load status of the server in the t-th time period. i，t C is the product of the standard deviation and the mean of the memory utilization rate of the i-th server within the t-th time period. i，t The trend strength of the memory usage rate of the i-th server within the t-th time period is obtained through a trend testing algorithm.

[0063] Correspondingly, the same steps can be used to obtain the CPU load coefficient for CPU utilization data, which reflects the CPU load status of each server in different time periods.

[0064] Furthermore, by combining the memory load coefficient, CPU load coefficient, and the load synchronization coefficient, the comprehensive load coefficient of each server in each time period is obtained. In this embodiment, the specific calculation formula is: G i，t =A i，t ×(D i，t +F i，t In the formula, G i，t Let A be the overall load factor of the i-th server in the t-th time period. i,t Let D be the load synchronization coefficient of the i-th server in the t-th time period. i，t F i,t These represent the memory load coefficient and CPU load coefficient of the i-th server in the t-th time period, respectively. A higher overall load coefficient indicates a higher current server load.

[0065] Thus, based on the above process in this embodiment, the overall load factor of each server in the cloud platform can be obtained.

[0066] Step 4: Analyze the differences in the overall load coefficient between each server and other servers in each time period, construct the weight adjustment coefficient of each server in each time period, and then update the server weight of each server in the next time period.

[0067] In a service system, a greater difference in load between different servers indicates a significant disparity in the weight allocation among them. For example, if a server's weight is much higher than others, it may receive too many requests during the weighted round-robin process, resulting in a heavy load while other servers may be idle, making it difficult for the system to process requests promptly. Therefore, by comparing the load states of each server, the differences in request allocation among them can be identified. Taking the i-th server as an example, the sum of the differences between the overall load coefficient of each server and the overall load coefficient of other servers during the same time period is calculated as the weight adjustment coefficient for each server during that time period. The positive or negative sign of the weight adjustment coefficient indicates whether the server's load is higher or lower than that of other servers, and the absolute value of the weight adjustment coefficient reflects the degree of load difference between the server and other servers. Thus, server load is optimized based on the weight adjustment coefficient.

[0068] Specifically, firstly, the absolute values ​​of the weight adjustment coefficients of all servers are normalized. Taking the i-th server as an example, the sign of its corresponding normalization result is set to the opposite of the sign of the weight adjustment coefficient, and the result after sign adjustment is used as the final weight adjustment amount. Before weight optimization in this embodiment, the weight of each server can be obtained according to its own hardware performance. The specific weight optimization method is as follows: the sum of the server weight of each server in each time period and its weight adjustment amount is used as the server weight of each server in the next time period. The server weight of each server is updated at the server weight update time point.

[0069] Understandably, a positive weight adjustment coefficient indicates that the server's load is higher than that of other servers, meaning it has a lower capacity to handle subsequent requests. Conversely, a negative weight adjustment coefficient, with the opposite sign, results in a smaller optimized weight, thus reducing the priority of request allocation. Optimizing the weights of all servers during the weighted round-robin load balancing process helps improve server load balance.

[0070] Based on the same inventive concept as the above methods, this application also provides an Internet information service system based on a cloud platform, which further includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described Internet information service methods based on a cloud platform.

[0071] It is understood that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0072] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0073] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Any equivalent structural or procedural transformations made based on the description and drawings of this application, or direct or indirect applications in other related technical fields, are similarly included within the protection scope of this application.

Claims

1. A cloud platform-based Internet information service method, characterized by, The method comprises the following steps: obtaining CPU occupancy rate, memory occupancy rate and response time of each server of the cloud platform at each sampling moment in each time period, and obtaining the traffic of the cloud platform at each sampling moment; calculating the cumulative sum of all traffic before the current sampling moment, calculating the mean value of all traffic in the preset historical period, comparing the cumulative sum obtained at the current sampling moment with the mean value obtained in the preset historical period, and determining the update time point of the server weight in each time period according to the comparison result and the mutation of the traffic data at the current sampling moment; analyzing the correlation between the CPU occupancy rate, the memory occupancy rate and the response time of the server in each time period to obtain the load synchronization coefficient of the server in each time period, obtaining the memory load coefficient and the CPU load coefficient of the server in each time period according to the fluctuation and the change trend of the memory occupancy rate and the CPU occupancy rate of the server in each time period, and obtaining the comprehensive load coefficient of the server in each time period in combination with the load synchronization coefficient; analyzing the difference between each server and other servers in terms of the comprehensive load coefficient in each time period, constructing the weight adjustment coefficient of each server in each time period, calculating the normalized result of the absolute value of the weight adjustment coefficient of each server in each time period, adjusting the positive and negative signs of each normalized result, and taking the adjusted result as the weight adjustment amount of each server in each time period, wherein the positive and negative signs of each normalized result are set to be opposite to the sign of the weight adjustment coefficient; summing the server weight of each server in each time period and the weight adjustment amount thereof to obtain the server weight of each server in the next time period of each time period. The determination of the update time point of the server weight in each time period further comprises: for each time period, if the cumulative sum obtained at the current sampling moment is greater than the mean value obtained in the preset historical period, or the traffic at the current sampling moment is a mutation position, then the current sampling moment is taken as the update time point of the server weight in the time period; otherwise, the end moment of the time period is taken as the update time point of the server weight; wherein the time period is a preset time length.

2. The cloud platform-based Internet information service method of claim 1, wherein, The obtaining of the load synchronization coefficient of each server in each time period comprises: calculating the Spearman correlation coefficient between the CPU occupancy rate, the memory occupancy rate and the response time of each server in each time period, and taking the sum of all the Spearman correlation coefficients obtained for each server in each time period as the load synchronization coefficient of each server in each time period.

3. The cloud platform-based Internet information service method of claim 1, wherein, The process of obtaining the memory load coefficient and the CPU load coefficient of the server in each time period further comprises: for the memory occupancy rate of each server in each time period, calculating the product of the standard deviation and the mean value of the server memory occupancy rate in each time period, and obtaining the trend strength of the memory occupancy rate of each server in each time period by using a trend test algorithm, and calculating the memory load coefficient of the server in each time period according to the product and the trend strength; correspondingly, for the CPU occupancy rate of each server in each time period, the calculation method of the memory load coefficient of the server in each time period is used to obtain the CPU load coefficient of the server in each time period.

4. The cloud platform-based Internet information service method of claim 3, wherein, The formula for calculating the memory load coefficient of each period server is: D i,t = B i,t × exp(C i,t ), wherein exp() is an exponential function with a natural constant as the base, D i,t is the memory load coefficient of the i-th server in the t-th period, B i,t is the product of the standard deviation and the mean of the memory occupancy rate of the i-th server in the t-th period, and C i,t is the trend intensity of the memory occupancy rate of the i-th server in the t-th period.

5. The cloud platform-based Internet information service method of claim 1, wherein, The corresponding calculation formula of the comprehensive load coefficient of each time period server is: G i,t = A i,t × (D i,t + F i,t ), wherein G i,t is the comprehensive load coefficient of the i-th server in the t-th time period, A i,t is the load synchronization coefficient of the i-th server in the t-th time period, D i,t and F i,t are respectively the memory load coefficient and the CPU load coefficient of the i-th server in the t-th time period.

6. The cloud platform-based Internet information service method of claim 1, wherein, The weight adjustment coefficient of each server in each time period is constructed by calculating the cumulative sum of the difference between the comprehensive load coefficient of each server and the comprehensive load coefficient of other servers in each time period, and taking the cumulative sum as the weight adjustment coefficient of each server in each time period. 7.A cloud platform-based Internet information service system, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, The processor realizes the steps of the method of any one of claims 1-6 when executing the computer program.

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