CDN-based file resource management system and method

Abstract

The invention provides a CDN (Content Delivery Network)-based file resource management system and method. The file resource management method comprises the following steps: carrying out network configuration of a CDN (Content Delivery Network) according to a resource management demand set by a user; the CDN screens target edge nodes corresponding to the users according to the file resource uploading parameters of the users, and determines the target edge nodes corresponding to the users; the CDN network sends and caches the content information uploaded in real time of the file resource to a target edge node in the CDN network, and the target edge node performs data sharing with other edge nodes of the CDN network; and monitoring the operation parameters of the CDN network in real time, and giving an abnormity alarm when the CDN network is abnormal. The system comprises modules corresponding to the steps of the method.

Description

Technical Field

[0001] The invention proposes a CDN-based file resource management system and method, belonging to the technical field of file resource management. Background Art

[0002] With the rapid development of the Internet, the popularity of network applications and the sharp increase in the scale of users, the management and distribution of file resources are facing unprecedented challenges. Traditional file resource management methods usually rely on a single central server for file storage and distribution. This method often leads to problems such as excessive server load, increased network latency, and decreased user experience when facing large-scale concurrent access by users. These problems are particularly prominent in cross-regional and cross-operator network environments. Traditional management requires all user requests to directly access the source server, which not only increases the load pressure on the source server, but also significantly increases network latency due to factors such as long network transmission paths and cross-operator mutual visits. Especially during peak hours, when a large number of users access the same resource at the same time, network congestion is particularly serious, greatly affecting the user experience. Summary of the invention

[0003] The present invention provides a CDN-based file resource management system and method to solve the above technical problems in the prior art. The technical solutions adopted are as follows:

[0004] A file resource management method based on CDN, the file resource management method comprising:

[0005] Perform network configuration of the CDN network according to the resource management requirements set by the user;

[0006] The CDN network screens the target edge node corresponding to the user according to the file resource upload parameters of the user, and determines the target edge node corresponding to the user;

[0007] The CDN network sends and caches the real-time uploaded content information of the file resource to the target edge node within the CDN network, and the target edge node shares the data with other edge nodes of the CDN network;

[0008] The operating parameters of the CDN network are monitored in real time, and an abnormality alarm is issued when an abnormality occurs in the CDN network.

[0009] Furthermore, the network configuration of the CDN network is performed according to the resource management requirements set by the user, including:

[0010] Real-time monitoring of whether the resource management demand information input by the user is received;

[0011] Determining a CDN network corresponding to the resource management demand information of the user according to the resource management demand information input by the user;

[0012] Perform CDN network configuration for the determined CDN network corresponding to the user's resource management demand information;

[0013] The resource management demand information includes file resource types, file resource access volume and user data user group distribution area; the file resource types include pictures, videos and documents.

[0014] Furthermore, the CDN network configuration is performed for the determined CDN network corresponding to the resource management requirement information of the user, including:

[0015] Configure the basic operating parameters of the CDN network according to the user's resource management demand information, wherein the basic operating parameters of the CDN network include downstream traffic, acceleration area, acceleration package size, etc.;

[0016] According to the acceleration zone corresponding to the user, the domain name corresponding to the acceleration zone is automatically added on the CDN console corresponding to the CDN network, and the domain name ownership is verified;

[0017] After the domain name ownership verification is completed, the basic parameters of the server group corresponding to the user are configured; wherein the server group corresponding to the user is the source station corresponding to the user, and the basic parameters include the domain name, the back-to-source address, the back-to-source protocol and the source station weight; meanwhile, the source station weight refers to the initial weight parameter corresponding to each server included in the server group;

[0018] The CDN network performs initial traffic distribution when the CDN node returns to the source according to the initial weight parameters corresponding to each server;

[0019] The weight is adjusted according to the file resource upload amount of each server in the server group corresponding to the user, and the traffic distribution when the CDN node returns to the source in the next operation monitoring cycle is adjusted according to the adjusted weight parameter.

[0020] Further, weight adjustment is performed according to the file resource upload amount of each server in the server group corresponding to the user, including:

[0021] Extract the time length corresponding to the preset operation monitoring cycle;

[0022] According to the time length corresponding to the preset operation monitoring cycle, real-time monitoring of the file resource upload volume of each server in the server group within each operation monitoring cycle;

[0023] The weight adjustment coefficient corresponding to each server is obtained by using the file resource upload amount of each server; wherein the weight adjustment coefficient is obtained by the following formula:

[0024]

[0025] Where s represents the weight adjustment coefficient corresponding to each server; n represents the number of file resource uploads of the server in the currently completed operation monitoring cycle; C i Indicates the uploaded data volume corresponding to the i-th file resource upload in the currently completed operation monitoring cycle; C max and C min Indicates the maximum and minimum values ​​of the uploaded data volume corresponding to the i-th file resource upload in the currently completed operation monitoring cycle; C px Indicates the average uploaded data volume of the file resources in the previous running monitoring cycle of the currently ended running monitoring cycle; C xmax and C xmin Indicates the maximum and minimum values ​​of the uploaded data volume of the file resource in the previous running monitoring cycle of the currently ended running monitoring cycle; c 01 and c 02 denote the first coefficient and the second coefficient respectively, and the first coefficient is obtained by the following formula:

[0026]

[0027] Among them, c 01 represents the first coefficient; m represents the number of times the file resource was uploaded in the previous operation monitoring cycle of the currently ended operation monitoring cycle; C j Indicates the data volume corresponding to the j-th file resource upload in the previous running monitoring cycle of the currently ended running monitoring cycle; C z01 Indicates the median value of the data volume corresponding to the file resource upload in the previous running monitoring cycle of the currently ended running monitoring cycle;

[0028] And, the second coefficient is obtained by the following formula:

[0029]

[0030] Among them, c 02 represents the second coefficient; n represents the number of times the file resource is uploaded to the server in the currently completed operation monitoring cycle; C j Indicates the uploaded data volume corresponding to the i-th file resource upload in the currently completed operation monitoring cycle; C z02 Indicates the median value of the data volume corresponding to the file resource upload of the currently completed running monitoring cycle;

[0031] Extracting the initial weight parameters corresponding to each server;

[0032] The adjusted weight parameter corresponding to each server is obtained by using the weight adjustment coefficient corresponding to each server and the initial weight parameter corresponding to each server, wherein the adjusted weight parameter is obtained by the following formula:

[0033] w t =(1+s)·w c

[0034] Among them, w t represents the adjusted weight parameter corresponding to each server; w c represents the initial weight parameter corresponding to each server; s represents the weight adjustment coefficient corresponding to each server.

[0035] Furthermore, the CDN network screens the target edge node corresponding to the user according to the file resource upload parameters of the user, and determines the target edge node corresponding to the user, including:

[0036] Extracting operation parameter data of each edge node in the CDN network, wherein the operation parameter data includes network status parameters and node status parameters;

[0037] Obtaining a first evaluation factor using the network status parameter;

[0038] Obtaining a second evaluation factor using the node state parameter;

[0039] The edge node evaluation parameter is obtained by using the first evaluation factor and the second evaluation factor; wherein the edge node evaluation parameter is obtained by the following formula:

[0040]

[0041] Where K represents the edge node evaluation parameter; K 01 Represents the first evaluation factor; K 02 represents the second evaluation factor;

[0042] The edge node corresponding to the maximum value of the edge node evaluation parameter is used as the target edge node corresponding to the user.

[0043] Further, obtaining a first evaluation factor using the network status parameter includes:

[0044] Extracting network status parameters corresponding to each edge node in the CDN network; wherein the network status parameters include network delay and communication channel utilization;

[0045] The first evaluation factor is obtained by using the network delay and the communication channel utilization, wherein the first evaluation factor is obtained by the following formula:

[0046]

[0047] Among them, K 01 represents the first evaluation factor; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P yi represents the network delay rate of data transmission corresponding to the i-th unit time of the edge node; P xi represents the communication channel utilization rate of data transmission occurring in the i-th unit time; P ymax Indicates the maximum value of the network delay rate corresponding to the edge node; P xmax represents the maximum utilization rate of the communication channel corresponding to the edge node; P xymax represents the network delay rate corresponding to the network delay when the communication channel utilization rate is at the maximum value; p represents the first evaluation adjustment parameter, and the first evaluation adjustment parameter is obtained by the following formula:

[0048]

[0049] Wherein, p represents the first evaluation adjustment parameter; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P xi represents the communication channel utilization rate of data transmission occurring in the i-th unit time; P xi-1 Represents the communication channel utilization rate of the data transmission occurring in the i-1th unit time.

[0050] Further, obtaining a second evaluation factor using the node state parameter includes:

[0051] Extracting node status parameters corresponding to each edge node in the CDN network; wherein the node status parameters include the number of requests processed, bandwidth usage, and the number of server connections;

[0052] The second evaluation factor is obtained by using the number of request processing, bandwidth usage and number of server connections, wherein the second evaluation factor is obtained by the following formula:

[0053]

[0054] Among them, K 02 represents the second evaluation factor; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P birepresents the bandwidth usage rate of data transmission occurring in the i-th unit time; M i represents the number of requests processed by the edge node corresponding to the i-th unit time; M 0i represents the number of server connections corresponding to the i-th unit time of the edge node; M k represents the evaluation adjustment coefficient, and the evaluation adjustment coefficient is obtained by the following formula:

[0055]

[0056] Among them, M k represents the evaluation adjustment coefficient; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; M i represents the number of requests processed by the edge node corresponding to the i-th unit time; M 0i Indicates the number of server connections corresponding to the i-th unit time of the edge node.

[0057] Furthermore, the CDN network sends and caches the real-time uploaded content information of the file resource to the target edge node within the CDN network, and the target edge node shares the data with other edge nodes of the CDN network, including:

[0058] Sorting all edge nodes in the CDN network in descending order according to edge node evaluation parameters to obtain an edge node set;

[0059] The CDN network sends and caches the real-time uploaded content information of the file resource to the target edge node corresponding to the user inside the CDN network;

[0060] The target edge node shares data according to the order of precedence in the edge node set.

[0061] Furthermore, the operation parameters of the CDN network are monitored in real time, and an abnormality alarm is issued when an abnormality occurs in the CDN network, including:

[0062] Extracting edge node evaluation parameters corresponding to each edge node of the CDN network;

[0063] Comparing the edge node evaluation parameter with a preset evaluation parameter threshold;

[0064] The edge nodes whose edge node evaluation parameters exceed the evaluation parameter threshold are taken as the first edge node group;

[0065] The edge nodes whose edge node evaluation parameters do not exceed the evaluation parameter threshold are taken as the second edge node group;

[0066] The first comprehensive node evaluation parameter is obtained by using the edge node evaluation parameter corresponding to the edge node included in the first edge node group, wherein the first comprehensive node evaluation parameter is obtained by the following formula:

[0067]

[0068] Among them, Q 01 represents the first comprehensive node evaluation parameter; a represents the number of edge nodes included in the first edge node group; K i represents the edge node evaluation parameter corresponding to the i-th edge node in the first edge node group; K amax and K amin represent the maximum value and the minimum value of the edge node evaluation parameter corresponding to the first edge node group respectively; K bmax Indicates the maximum value of the edge node evaluation parameter corresponding to the second edge node group;

[0069] The second comprehensive node evaluation parameter is obtained by using the edge node evaluation parameter corresponding to the edge node included in the second edge node group, wherein the second comprehensive node evaluation parameter is obtained by the following formula:

[0070]

[0071] Among them, Q 02 represents the second comprehensive node evaluation parameter; b represents the number of edge nodes included in the second edge node group; K j represents the edge node evaluation parameter corresponding to the jth edge node in the second edge node group; K bmax and K bmin represent the maximum value and the minimum value of the edge node evaluation parameter corresponding to the second edge node group respectively; K amin Indicates the minimum value of the edge node evaluation parameter corresponding to the first edge node group;

[0072] The first comprehensive node evaluation parameter and the second comprehensive node evaluation parameter are used to obtain a comprehensive node evaluation parameter; wherein the comprehensive node evaluation parameter is obtained by the following formula:

[0073]

[0074] Among them, Q represents the comprehensive node evaluation parameter; Q 01 represents the first comprehensive node evaluation parameter; Q 02 represents the second comprehensive node evaluation parameter;

[0075] When the comprehensive node evaluation parameter is lower than a preset comprehensive evaluation parameter threshold, it is determined that an abnormality exists in the CDN network and an abnormality alarm is issued.

[0076] A file resource management system based on CDN, the file resource management system includes:

[0077] The network configuration module is used to configure the CDN network according to the resource management requirements set by the user;

[0078] A target edge node determination module is used for the CDN network to screen the target edge node corresponding to the user according to the file resource upload parameters of the user, and determine the target edge node corresponding to the user;

[0079] A data sending and sharing module, which is used for the CDN network to send and cache the content information of the real-time upload of the file resources to the target edge node inside the CDN network, and for the target edge node to share the data with other edge nodes of the CDN network;

[0080] The network anomaly monitoring module is used to monitor the operating parameters of the CDN network in real time and issue an anomaly alarm when an anomaly occurs in the CDN network.

[0081] Beneficial effects of the present invention:

[0082] The CDN-based file resource management system and method proposed in the present invention significantly reduces the access delay of file resources and improves the user experience through the user's nearby access and the efficient data transmission mechanism within the CDN network. Through multi-node deployment and load balancing technology, the access pressure of a single node is effectively dispersed, and the fault tolerance and availability of the system are improved. Intelligent caching strategies and data sharing mechanisms enable the CDN network to utilize storage and bandwidth resources more efficiently and reduce operating costs. Through real-time monitoring and abnormal alarm mechanisms, potential security threats are discovered and responded to in a timely manner, ensuring the safe and reliable management of file resources. The original intention of the CDN network was to cope with the scenario of large-scale concurrent access by users. Through distributed storage and distribution technology, the pressure on the source server is effectively alleviated. BRIEF DESCRIPTION OF THE DRAWINGS

[0083] Figure 1 is a flow chart of the method of the present invention;

[0084] Figure 2 The system block diagram of the system of the present invention. DETAILED DESCRIPTION

[0085] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0086] The embodiment of the present invention proposes a CDN-based file resource management method, such as Figure 1As shown, the file resource management method includes:

[0087] S1. Perform network configuration of the CDN network according to the resource management requirements set by the user;

[0088] S2, the CDN network screens the target edge node corresponding to the user according to the file resource upload parameters of the user, and determines the target edge node corresponding to the user;

[0089] S3, the CDN network sends and caches the real-time uploaded content information of the file resource to the target edge node inside the CDN network, and the target edge node shares the data with other edge nodes of the CDN network;

[0090] S4. Monitor the operating parameters of the CDN network in real time, and issue an abnormality alarm when an abnormality occurs in the CDN network.

[0091] The working principle of the above technical solution is to customize the CDN network according to the specific needs of users (such as geographical distribution, traffic prediction, security requirements, etc.). This includes selecting a suitable CDN service provider, configuring the network topology, setting cache strategies, etc., to ensure that the CDN network can meet the user's resource management needs.

[0092] When a user uploads a file resource, the CDN network will intelligently select the target edge node that is most suitable for storing and distributing the file resource based on the upload parameters provided by the user (such as geographic location, network conditions, file type, etc.). This step is designed to enable users to access the nearest location and reduce network latency.

[0093] Once the target edge node is determined, the CDN network will quickly send and cache the real-time content information of the file resources uploaded by the user to the node. Subsequently, the target edge node will share data with other edge nodes, and through the fast transmission mechanism within the CDN network, ensure that the file resources can quickly cover each node in the CDN network, and achieve wide distribution of content.

[0094] The CDN network will monitor its own operating parameters in real time, including but not limited to node load, cache hit rate, network latency, bandwidth usage, etc. Once an abnormal situation is found (such as node failure, network congestion, cache failure, etc.), the system will immediately trigger an alarm mechanism to notify the administrator or automatically take countermeasures to ensure the stable operation of the CDN network and the safe and reliable management of file resources.

[0095] The effects of the above technical solutions are: through the user's nearby access and the efficient data transmission mechanism within the CDN network, the access delay of file resources is significantly reduced, and the user experience is improved. Through multi-node deployment and load balancing technology, the access pressure of a single node is effectively dispersed, and the fault tolerance and availability of the system are improved. Intelligent caching strategies and data sharing mechanisms enable the CDN network to use storage and bandwidth resources more efficiently and reduce operating costs. Through real-time monitoring and abnormal alarm mechanisms, potential security threats can be discovered and responded to in a timely manner, ensuring the safe and reliable management of file resources. The original intention of the CDN network was to cope with the scenario of large-scale concurrent access by users. Through distributed storage and distribution technology, the pressure on the source server is effectively alleviated.

[0096] In summary, through a series of innovative designs and implementations, this technical solution has brought significant technical effects to file resource management, improved user experience, reduced operating costs, and enhanced system stability and security.

[0097] In one embodiment of the present invention, the network configuration of the CDN network is performed according to the resource management requirements set by the user, including:

[0098] S101, real-time monitoring to determine whether resource management demand information input by a user is received;

[0099] S102, determining a CDN network corresponding to the resource management demand information of the user according to the resource management demand information input by the user;

[0100] S103, performing CDN network configuration for the determined CDN network corresponding to the user's resource management requirement information;

[0101] The resource management demand information includes file resource types, file resource access volume and user data user group distribution area; the file resource types include pictures, videos and documents.

[0102] The working principle of the above technical solution is: the system continuously monitors whether there is resource management demand information input by the user. This is usually received through a user interface (such as a web page, API interface, etc.). Once the demand information submitted by the user is received, the system enters the next processing flow.

[0103] The system parses the received user input and extracts key resource management demand information, including file resource types (such as pictures, videos, documents, etc.), estimated file resource access volume, and user data user group distribution area.

[0104] Based on this information, the system further determines the CDN network that best suits the user's needs. This involves selecting a CDN service provider or network architecture with appropriate node distribution, bandwidth resources, caching strategies, and other characteristics.

[0105] After the CDN network is determined, the system configures the CDN network accordingly based on the user's resource management requirements. This includes setting cache policies (such as cache time, cache space size, etc.), load balancing policies, security policies, etc., to ensure that the CDN network can efficiently meet the user's file resource management needs.

[0106] The effect of the above technical solution is: this technical solution allows users to customize the configuration of the CDN network according to their actual needs (such as file resource type, access volume, user group distribution, etc.), thereby realizing a personalized resource management solution. This helps to improve user experience while reducing operating costs. By real-time monitoring and dynamic adjustment of the configuration of the CDN network, the system can more efficiently utilize network resources, including bandwidth, storage and computing resources. This helps to improve the performance and stability of the CDN network while reducing costs caused by idle or overused resources. Configuring the CDN network based on the user's data user group distribution area can ensure that users can access the required file resources nearby, thereby significantly reducing access latency and improving user experience. The design of this technical solution allows users to dynamically adjust the configuration of the CDN network according to changes in actual needs. This makes the system have good scalability and can easily cope with possible business growth or changes in the future.

[0107] During CDN network configuration, multiple security policies (such as data encryption, access control, etc.) can be integrated to ensure the security of file resources during transmission and storage. This helps protect user data privacy and business security.

[0108] In summary, this technical solution not only improves the user experience and system performance stability, but also enhances the scalability and security of the system by realizing personalized configuration of the CDN network according to the resource management requirements set by the user.

[0109] In one embodiment of the present invention, CDN network configuration is performed for a determined CDN network corresponding to resource management requirement information of a user, including:

[0110] S1031. Configure basic operation parameters of the CDN network according to the user's resource management demand information, wherein the basic operation parameters of the CDN network include downstream traffic, acceleration area, acceleration package size, etc.;

[0111] S1032. Automatically add a domain name corresponding to the acceleration zone on the CDN console corresponding to the CDN network according to the acceleration zone corresponding to the user, and perform domain name ownership verification;

[0112] S1033. After the domain name ownership verification is completed, configure the basic parameters of the server group corresponding to the user; wherein the server group corresponding to the user is the source station corresponding to the user, and the basic parameters include the domain name, the back-to-source address, the back-to-source protocol and the source station weight; meanwhile, the source station weight refers to the initial weight parameter corresponding to each server included in the server group;

[0113] S1034, the CDN network performs initial traffic allocation when the CDN node returns to the source according to the initial weight parameters corresponding to each server;

[0114] S1035. Adjust the weight according to the file resource upload amount of each server in the server group corresponding to the user, and adjust the traffic distribution when the CDN node returns to the source in the next operation monitoring cycle according to the adjusted weight parameter.

[0115] The working principle of the above technical solution is to set the basic operating parameters of the CDN network according to the user's resource management demand information (such as expected access volume, content type, etc.). These parameters include but are not limited to downstream traffic restrictions, selection of acceleration areas (i.e., determining the geographical locations where CDN nodes will be deployed to optimize access speed), and acceleration package size (affecting cache efficiency and response speed).

[0116] On the CDN console, the corresponding domain name is automatically added according to the acceleration zone specified by the user. This step is to ensure that the CDN network can correctly identify and process the user's domain name request. Subsequently, the domain name ownership is verified to ensure that only the legitimate domain name owner can perform subsequent configurations, thereby enhancing the security of the system.

[0117] After the domain name ownership is verified, the basic parameters of the server group (i.e., the source station) corresponding to the user are configured. These parameters include the domain name (used for communication between the CDN node and the source station), the back-to-source address (the specific IP address or domain name of the source station), the back-to-source protocol (such as HTTP / HTTPS), and the source station weight. The source station weight is used to determine the traffic distribution ratio between different source stations when the CDN node is back-to-source.

[0118] The CDN network performs initial traffic distribution when the CDN node returns to the source based on the initial weight parameters corresponding to each server. This means that when the CDN network starts working, it will distribute user requests to different source stations based on the preset weights to balance the load and ensure service stability.

[0119] As time goes by, the system will dynamically adjust the weight of the source station based on the actual operation data such as the file resource upload volume of each server in the server group corresponding to the user. This is to reflect the current load capacity and performance of the server and ensure that the CDN network can efficiently use resources. The adjusted weight parameters will be used for the traffic distribution when the CDN node returns to the source in the next operation monitoring cycle, thereby further optimizing the user experience and the performance of the CDN network.

[0120] The effect of the above technical solution is: the technical solution allows the configuration to be dynamically adjusted according to the actual needs of users and the operation of the CDN network, so that the CDN network can flexibly respond to various changes and improve the adaptability and stability of the system. Through reasonable weight distribution and traffic redistribution mechanism, balanced management of the source station load can be achieved to avoid the situation where a single source station is overloaded while other source stations are idle, thereby improving resource utilization and the overall performance of the system. By optimizing the configuration and traffic distribution strategy of the CDN network, the delay time for users to access file resources can be significantly reduced, the response speed and access success rate can be improved, thereby improving the user experience. Security measures such as domain name ownership verification enhance the security of the CDN network and prevent the access of illegal domain names and potential security threats. Reasonable CDN network configuration and traffic distribution strategy help reduce the waste of resources such as bandwidth and storage, thereby reducing operating costs. At the same time, by improving the performance and stability of the system, downtime and maintenance costs caused by failures can also be reduced.

[0121] In one embodiment of the present invention, weight adjustment is performed according to the file resource upload amount of each server in the server group corresponding to the user, including:

[0122] Step 1: extract the time length corresponding to the preset operation monitoring cycle;

[0123] Step 2: monitor the file resource upload volume of each server in the server group within each operation monitoring period in real time according to the time length corresponding to the preset operation monitoring period;

[0124] Step 3: Obtain a weight adjustment coefficient corresponding to each server using the file resource upload amount of each server; wherein the weight adjustment coefficient is obtained by the following formula:

[0125]

[0126] Where s represents the weight adjustment coefficient corresponding to each server; n represents the number of file resource uploads of the server in the currently completed operation monitoring cycle; C i Indicates the uploaded data volume corresponding to the i-th file resource upload in the currently completed operation monitoring cycle; C max and C minIndicates the maximum and minimum values ​​of the uploaded data volume corresponding to the i-th file resource upload in the currently completed operation monitoring cycle; C px Indicates the average uploaded data volume of the file resources in the previous running monitoring cycle of the currently ended running monitoring cycle; C xmax and C xmin Indicates the maximum and minimum values ​​of the uploaded data volume of the file resource in the previous running monitoring cycle of the currently ended running monitoring cycle; c 01 and c 02 denote the first coefficient and the second coefficient respectively, and the first coefficient is obtained by the following formula:

[0127]

[0128] Among them, c 01 represents the first coefficient; m represents the number of times the file resource was uploaded in the previous operation monitoring cycle of the currently ended operation monitoring cycle; C j Indicates the data volume corresponding to the j-th file resource upload in the previous running monitoring cycle of the currently ended running monitoring cycle; C z01 Indicates the median value of the data volume corresponding to the file resource upload in the previous running monitoring cycle of the currently ended running monitoring cycle;

[0129] And, the second coefficient is obtained by the following formula:

[0130]

[0131] Among them, c 02 represents the second coefficient; n represents the number of times the file resource is uploaded to the server in the currently completed operation monitoring cycle; C j Indicates the uploaded data volume corresponding to the i-th file resource upload in the currently completed operation monitoring cycle; C z02 Indicates the median value of the data volume corresponding to the file resource upload of the currently completed running monitoring cycle;

[0132] Step 4: extract the initial weight parameters corresponding to each server;

[0133] Step 5: Obtain an adjusted weight parameter corresponding to each server using the weight adjustment coefficient corresponding to each server and the initial weight parameter corresponding to each server, wherein the adjusted weight parameter is obtained by the following formula:

[0134] w t =(1+s)·w c

[0135] Among them, w t represents the adjusted weight parameter corresponding to each server; wc represents the initial weight parameter corresponding to each server; s represents the weight adjustment coefficient corresponding to each server.

[0136] The working principle of the above technical solution is as follows: First, the system sets a preset operation monitoring cycle, which defines the time range for data collection and weight adjustment. In each operation monitoring cycle, the system monitors and records the file resource upload volume of each server in the server group in real time. This includes the number of uploads and the amount of data uploaded each time. Using the collected data, the system calculates the weight adjustment coefficient corresponding to each server. This coefficient is based on the comparison of the upload data volume of the current operation monitoring cycle with the previous operation monitoring cycle, and is obtained through complex mathematical formulas (including median, average upload data volume, number of uploads, etc.). The purpose is to reflect the load capacity and performance of the server in the current cycle. The system extracts the initial weight parameters set for each server when the CDN network is configured. These parameters are set when the CDN network starts working and are used to preliminarily allocate the traffic when the CDN node returns to the source. The weight adjustment coefficient of each server is combined with the initial weight parameter, and the adjusted weight parameter of each server is calculated by formula. This adjusted weight will be used for traffic allocation when the CDN node returns to the source in the next operation monitoring cycle.

[0137] The effect of the above technical solution is: by real-time monitoring and dynamic adjustment of server weights, the system can ensure a more balanced distribution of traffic when CDN nodes go back to the source. This helps to avoid situations where some servers are overloaded while other servers are idle, and improves the overall performance and stability of the system. The weight adjustment mechanism enables the CDN network to utilize server resources more efficiently. Servers with good performance will take on more back-to-source tasks, while servers with poor performance will have their workloads reduced accordingly, thereby optimizing resource allocation. This technical solution can dynamically adjust weights based on the actual operation of the servers, enhancing the adaptability and flexibility of the system. Regardless of how the server load changes, the system can maintain the stable operation of the CDN network by adjusting the weights.

[0138] By optimizing the traffic distribution strategy of the CDN network, the system can reduce the delay time for users to access file resources, improve the response speed and access success rate, and thus enhance the user experience. A reasonable weight adjustment mechanism helps reduce downtime and maintenance costs caused by server overload. At the same time, by improving the performance and stability of the system, it can also reduce the waste of bandwidth and storage resources caused by failures. This technical solution introduces complex mathematical formulas and algorithms to calculate the weight adjustment coefficient, realizing the intelligent management of CDN network traffic distribution. This not only improves the accuracy and efficiency of management, but also reduces the need for human intervention.

[0139] In one embodiment of the present invention, the CDN network screens the target edge node corresponding to the user according to the file resource upload parameters of the user, and determines the target edge node corresponding to the user, including:

[0140] S201. Extracting operation parameter data of each edge node in the CDN network, wherein the operation parameter data includes network status parameters and node status parameters;

[0141] S202, obtaining a first evaluation factor using the network status parameter;

[0142] S203, obtaining a second evaluation factor using the node state parameter;

[0143] S204: Obtain an edge node evaluation parameter using the first evaluation factor and the second evaluation factor; wherein the edge node evaluation parameter is obtained by the following formula:

[0144]

[0145] Where K represents the edge node evaluation parameter; K 01 Represents the first evaluation factor; K 02 represents the second evaluation factor;

[0146] S205: Use the edge node corresponding to the maximum value of the edge node evaluation parameter as the target edge node corresponding to the user.

[0147] The working principle of the above technical solution is as follows: First, the system extracts the operating parameter data of each edge node in the CDN network. These data include network status parameters (such as network delay, bandwidth utilization, packet loss rate, etc.) and node status parameters (such as node load, cache hit rate, failure rate, etc.).

[0148] Using the extracted network status parameters, the system calculates the first evaluation factor (K01). This factor reflects the performance of the edge node at the network level, such as the reliability and efficiency of data transmission.

[0149] Similarly, using the node status parameters, the system calculates the second evaluation factor (K02), which reflects the performance of the edge node at the node level, such as processing power and stability.

[0150] By combining the first evaluation factor and the second evaluation factor, the system calculates the evaluation parameter (K) of each edge node. This parameter is a comprehensive indicator used to evaluate the overall performance of the edge node when processing user file resource upload requests.

[0151] Finally, the system compares the evaluation parameters of all edge nodes and determines the edge node corresponding to the maximum evaluation parameter as the target edge node corresponding to the user. This means that the node has the best performance at both the network and node levels and is most suitable for processing the user's file resource upload request.

[0152] The effect of the above technical solution is: by selecting the edge node with the best performance as the target node, the system can ensure that the user's file resource upload request is processed quickly and stably. This helps to reduce upload delays and improve upload success rates, thereby optimizing user experience. The introduction of edge node evaluation parameters enables the CDN network to allocate resources more reasonably. Edge nodes with poor performance will have fewer opportunities to process user requests, while nodes with better performance will take on more tasks. This helps to improve resource utilization and the overall performance of the system. By dynamically screening target edge nodes, the system can promptly discover and avoid using nodes that may fail or have degraded performance. This helps to enhance the stability and reliability of the system and reduce the risk of service interruption and data loss caused by node failure. The technical solution realizes intelligent management of edge nodes in the CDN network. By comprehensively evaluating multiple performance indicators of edge nodes, the system can automatically select the optimal node to process user requests, reducing the need for human intervention and the error rate. The technical solution has strong adaptability. As the scale of the CDN network expands and user needs change, the system can flexibly adjust the weights and calculation methods of the evaluation factors to adapt to different application scenarios and needs.

[0153] On the other hand, the above technical solution combines two different evaluation factors together, which can make a more comprehensive evaluation of edge nodes. The first evaluation factor reflects a certain network state of the node, and the second evaluation factor reflects other characteristics of the node (such as the stability or load of the node). By combining these two evaluation factors, the formula can more accurately determine which edge node is more suitable as the user's target node. The logarithmic function and weighting coefficient in the formula make it possible to adjust these parameters in actual use to affect the relative importance of the two evaluation factors, thereby dynamically adjusting the node selection strategy according to the actual network environment. For example, when a certain evaluation factor becomes more important, its weight can be increased so that the factor occupies a larger proportion in the final evaluation. Through this weighted evaluation method, the relationship between performance (such as response time, throughput) and reliability (such as node availability, failure rate) can be weighed when selecting nodes, so as to select the edge node that best suits user needs under current conditions. The formula in this technical solution enhances the flexibility and accuracy of the CDN network in selecting edge nodes by comprehensively considering different node evaluation factors, thereby improving the overall performance of the system.

[0154] In one embodiment of the present invention, obtaining a first evaluation factor using the network status parameter includes:

[0155] S2021. Extracting network status parameters corresponding to each edge node in the CDN network; wherein the network status parameters include network delay and communication channel utilization;

[0156] S2022. Obtain a first evaluation factor using the network delay and the communication channel utilization rate, wherein the first evaluation factor is obtained by the following formula:

[0157]

[0158] Among them, K 01 represents the first evaluation factor; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P yi represents the network delay rate of data transmission corresponding to the i-th unit time of the edge node; P xi represents the communication channel utilization rate of data transmission occurring in the i-th unit time; P ymax Indicates the maximum value of the network delay rate corresponding to the edge node; P xmax represents the maximum utilization rate of the communication channel corresponding to the edge node; P xymax represents the network delay rate corresponding to the network delay when the communication channel utilization rate is at the maximum value; p represents the first evaluation adjustment parameter, and the first evaluation adjustment parameter is obtained by the following formula:

[0159]

[0160] Wherein, p represents the first evaluation adjustment parameter; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P xi represents the communication channel utilization rate of data transmission occurring in the i-th unit time; P xi-1 Represents the communication channel utilization rate of the data transmission occurring in the i-1th unit time.

[0161] The working principle of the above technical solution is as follows: the system first extracts the network status parameters corresponding to each edge node in the CDN network, mainly including network delay (Pyi) and communication channel utilization (Pxi). These data are collected in real time or periodically, reflecting the real-time performance of edge nodes at the network level.

[0162] For each edge node, the system calculates its data transmission network delay rate (Pyi) and communication channel utilization rate (Pxi) per unit time (such as 1 second). These indicators are used to quantify the performance of edge nodes in network transmission.

[0163] The system also needs to determine the maximum network delay rate (Pymax) and the maximum communication channel utilization rate (Pxmax) corresponding to the edge node, as well as the network delay rate (Pxymax) corresponding to the network delay when the communication channel utilization rate is maximum. These values ​​are used for subsequent calculations and comparisons.

[0164] Using the above data, the system calculates the first evaluation adjustment parameter (p). This parameter takes into account the change in communication channel utilization and evaluates the stability of the edge node when the network load changes by comparing the communication channel utilization in adjacent unit time.

[0165] Finally, the system uses the network delay rate, communication channel utilization, maximum network delay rate, maximum communication channel utilization, network delay rate under specific conditions, and the first evaluation adjustment parameter to calculate the first evaluation factor (K01) through a given formula. This factor combines the performance of the edge node in terms of network delay and communication channel utilization, and takes into account the stability of network load changes.

[0166] The effect of the above technical solution is: by comprehensively considering the two key indicators of network delay and communication channel utilization, the system can more accurately evaluate the network performance of each edge node in the CDN network. This helps to identify performance bottlenecks and potential problem areas. The introduction of the first evaluation adjustment parameter enables the evaluation process to take into account changes in network load. By comparing the communication channel utilization in adjacent unit times, the system can evaluate the stability and responsiveness of edge nodes when network load fluctuates. Based on the evaluation results of the first evaluation factor, the system can more reasonably allocate resources in the CDN network. Edge nodes with better performance will bear more user requests, thereby improving the overall service quality and user experience. By evaluating the network performance of edge nodes regularly or in real time, the system can promptly discover and solve potential network problems. This helps to reduce the risk of service interruption and data loss caused by network failures and improve the stability and reliability of the system. This technical solution provides strong support for the intelligent management of CDN networks. Through the automated evaluation and adjustment process, the system can automatically optimize resource allocation and service quality, reducing the need for human intervention and the error rate.

[0167] On the other hand, by considering two key network status parameters, network delay and communication channel utilization, the performance of edge nodes in the data transmission process can be quantitatively reflected. Specifically, nodes with smaller network delay and higher communication channel utilization will obtain a better first evaluation factor, indicating that they have better service capabilities under current conditions.

[0168] The above technical solution is used to adjust the evaluation process of the network status. By calculating the changes in the utilization of the communication channel in each unit time, it can capture the fluctuations of the network status over time. If the network delay or channel utilization changes significantly, the value of p will appropriately adjust the first evaluation factor, thereby dynamically reflecting the changes in the network status, thereby affecting the evaluation results of the node. The above formula combines multiple network performance indicators, so that the evaluation of the node not only considers the single network delay or channel utilization, but combines the two to form a multi-dimensional node evaluation method. This evaluation method can more accurately select nodes that are suitable as user target edge nodes, thereby improving the user experience. Through the detailed quantification of the network status, K 01 The calculation results of and p can provide a more accurate and targeted evaluation basis for subsequent node selection. This improvement in accuracy can effectively avoid node selection errors caused by fluctuations or anomalies in a single indicator, ensuring the overall stability and reliability of the system.

[0169] In summary, these formulas achieve comprehensive, dynamic, and accurate performance evaluation of edge nodes through precise evaluation and adjustment of network latency and channel utilization, thus providing strong technical support for node selection in CDN networks.

[0170] In one embodiment of the present invention, obtaining a second evaluation factor using the node state parameter includes:

[0171] S2031. Extracting node status parameters corresponding to each edge node in the CDN network; wherein the node status parameters include the number of request processing, bandwidth usage, and the number of server connections;

[0172] S2032. Obtain a second evaluation factor using the number of request processing, bandwidth usage, and number of server connections, wherein the second evaluation factor is obtained by the following formula:

[0173]

[0174] Among them, K 02 represents the second evaluation factor; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P bi represents the bandwidth usage rate of data transmission occurring in the i-th unit time; M i represents the number of requests processed by the edge node corresponding to the i-th unit time; M 0i represents the number of server connections corresponding to the i-th unit time of the edge node; M k represents the evaluation adjustment coefficient, and the evaluation adjustment coefficient is obtained by the following formula:

[0175]

[0176] Among them, M k represents the evaluation adjustment coefficient; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; M i represents the number of requests processed by the edge node corresponding to the i-th unit time; M 0i Indicates the number of server connections corresponding to the i-th unit time of the edge node.

[0177] The working principle of the above technical solution is as follows: the system first extracts the node status parameters corresponding to each edge node in the CDN network, mainly including the number of requests processed (Mi), bandwidth utilization (although not directly used in this step, it is usually one of the important indicators for evaluating node status) and the number of server connections (M0i). These data reflect the ability of edge nodes in processing user requests and maintaining server connections.

[0178] For each edge node, the system calculates the number of requests processed (Mi) and the number of server connections (M0i) in each unit time (such as 1 second). These two indicators are used to quantify the processing capacity and connection status of the edge node respectively.

[0179] The system uses the number of requests processed (Mi) and the number of server connections (M0i) to calculate the evaluation adjustment coefficient (Mk). This coefficient reflects the proportional relationship between the number of requests processed and the number of server connections, and how this relationship changes over time. The calculation of the evaluation adjustment coefficient takes into account the change in the number of requests processed per unit time to evaluate the stability and efficiency of the edge node in processing requests.

[0180] Finally, the system uses the number of request processing (Mi), the number of server connections (M0i) and the evaluation adjustment coefficient (Mk) to calculate the second evaluation factor (K02) through a given formula. This factor combines the performance of edge nodes in processing requests and maintaining connections, and takes into account the balance between request processing and connection status.

[0181] The effect of the above technical solution is that by considering the number of request processing, the number of server connections and the proportional relationship between them, the system can more comprehensively evaluate the performance of each edge node in the CDN network. This helps to identify nodes with insufficient processing capacity or poor connection status. The introduction of the evaluation adjustment coefficient enables the evaluation process to take into account the changes in the number of request processing. By dynamically adjusting the evaluation factor, the system can motivate the edge nodes to remain efficient and stable when processing requests, thereby improving the overall request processing efficiency. The calculation of the second evaluation factor takes into account the balance between the number of request processing and the number of server connections. This helps to avoid the situation where resources are over-concentrated on certain nodes and other nodes are insufficient, thereby optimizing resource allocation and utilization in the CDN network. By evaluating the node status parameters of edge nodes regularly or in real time, the system can promptly discover and solve potential performance problems. This helps to reduce the risk of service interruption and data loss caused by node failure or performance bottleneck, and improve the stability and reliability of the system. The technical solution provides strong support for the intelligent management of CDN networks. Through the automated evaluation and adjustment process, the system can automatically optimize node performance and service quality, reducing the need for human intervention and the error rate.

[0182] On the other hand, the second evaluation factor combines multiple key network status parameters such as the number of request processing, bandwidth utilization, and the number of server connections, thereby evaluating the performance of the node in multiple dimensions. This evaluation method helps to comprehensively measure the service capabilities of the node under different network conditions and ensure that the selected node can efficiently meet user needs. At the same time, Mi and M0i in the second evaluation factor represent the request processing volume and the number of server connections, respectively. These parameters reflect the processing capacity and resource utilization of the edge node in actual operation. By comparing these parameters, the formula can evaluate whether the node maintains an efficient operating state under high load or low load conditions. At the same time, the second evaluation factor dynamically adjusts the size of the evaluation factor by comparing the minimum and maximum processing volumes in each time period. This adjustment mechanism can flexibly adapt to changes in network status according to actual operating conditions, ensuring the accuracy and real-time nature of the evaluation results. At the same time, the second evaluation factor can consider the balance between the node load and its service capacity when calculating the node evaluation factor. This balancing mechanism avoids the evaluation deviation caused by a single parameter that is too high or too low, thereby more reasonably selecting nodes suitable as target edge nodes. The above technical solution can better select edge nodes with excellent performance and balanced load, thereby improving the overall service quality of the CDN network and reducing user access delays and network congestion problems.

[0183] In summary, the above technical solution achieves accurate evaluation of edge node performance through multi-dimensional parameter evaluation and dynamic adjustment, thereby optimizing the node selection process of the CDN network and improving network resource utilization and user experience.

[0184] In one embodiment of the present invention, the CDN network sends and caches content information of real-time upload of file resources to a target edge node within the CDN network, and the target edge node shares data with other edge nodes of the CDN network, including:

[0185] S301, sorting all edge nodes in the CDN network in descending order according to edge node evaluation parameters to obtain an edge node set;

[0186] S302, the CDN network sends and caches the real-time uploaded content information of the file resource to the target edge node corresponding to the user inside the CDN network;

[0187] S303: The target edge node shares data according to the order of precedence in the edge node set.

[0188] The working principle of the above technical solution is as follows: First, the CDN network will sort all edge nodes in descending order according to the edge node evaluation parameters (such as K value) calculated previously. This sorting process generates an edge node set, in which the node with the highest evaluation parameter is at the front of the set.

[0189] Next, the CDN network sends and caches the file resource content information uploaded by the user in real time to the target edge node corresponding to the user. This target edge node is determined based on the previous screening and evaluation process and has the best network and node performance.

[0190] Once the content information is cached in the target edge node, the node will share data with other edge nodes in the CDN network in the order of the edge node set previously sorted. This means that data will be shared with the node with the second highest evaluation parameter first, and then in turn, until all edge nodes that need to share data have received the data.

[0191] The effect of the above technical solution is that the CDN network can optimize the efficiency of data distribution by sorting edge nodes according to evaluation parameters and sharing data with nodes with better performance first. This helps to reduce the time delay of data transmission and improve the user access speed. The technical solution supports the CDN network to dynamically expand edge nodes when needed. When new nodes join the network, the system can recalculate the edge node evaluation parameters and sort them, thereby ensuring the efficiency and accuracy of the data sharing process. Since data sharing is based on edge node evaluation parameters, even if some nodes in the network fail or performance degrades, the system can automatically adjust the data sharing strategy to avoid data loss or service interruption. Through a reasonable data sharing strategy, the CDN network can reduce unnecessary data transmission and repeated caching, thereby reducing bandwidth costs and storage costs. End users will benefit from the efficient data distribution and caching mechanism of the CDN network. No matter from which geographical location the user accesses the file resources in the CDN network, they can get a fast and stable access experience.

[0192] In one embodiment of the present invention, real-time monitoring of the operating parameters of the CDN network and abnormal alarming when an abnormality occurs in the CDN network include:

[0193] S401, extracting edge node evaluation parameters corresponding to each edge node of the CDN network;

[0194] S402, comparing the edge node evaluation parameter with a preset evaluation parameter threshold;

[0195] S403, taking the edge nodes whose edge node evaluation parameters exceed the evaluation parameter threshold as the first edge node group;

[0196] S404, taking the edge nodes whose edge node evaluation parameters do not exceed the evaluation parameter threshold as the second edge node group;

[0197] S405: Obtain a first comprehensive node evaluation parameter using the edge node evaluation parameters corresponding to the edge nodes included in the first edge node group, wherein the first comprehensive node evaluation parameter is obtained by the following formula:

[0198]

[0199] Among them, Q 01 represents the first comprehensive node evaluation parameter; a represents the number of edge nodes included in the first edge node group; K i represents the edge node evaluation parameter corresponding to the i-th edge node in the first edge node group; K amax and K aminrepresent the maximum value and the minimum value of the edge node evaluation parameter corresponding to the first edge node group respectively; K bmax Indicates the maximum value of the edge node evaluation parameter corresponding to the second edge node group;

[0200] S406: Obtain a second comprehensive node evaluation parameter using the edge node evaluation parameters corresponding to the edge nodes included in the second edge node group, wherein the second comprehensive node evaluation parameter is obtained by the following formula:

[0201]

[0202] Among them, Q 02 represents the second comprehensive node evaluation parameter; b represents the number of edge nodes included in the second edge node group; K j represents the edge node evaluation parameter corresponding to the jth edge node in the second edge node group; K bmax and K bmin represent the maximum value and the minimum value of the edge node evaluation parameter corresponding to the second edge node group respectively; K amin Indicates the minimum value of the edge node evaluation parameter corresponding to the first edge node group;

[0203] S407: Obtain a comprehensive node evaluation parameter using the first comprehensive node evaluation parameter and the second comprehensive node evaluation parameter; wherein the comprehensive node evaluation parameter is obtained by the following formula:

[0204]

[0205] Among them, Q represents the comprehensive node evaluation parameter; Q 01 represents the first comprehensive node evaluation parameter; Q 02 represents the second comprehensive node evaluation parameter;

[0206] S408: When the comprehensive node evaluation parameter is lower than a preset comprehensive evaluation parameter threshold, it is determined that an abnormality exists in the CDN network, and an abnormality alarm is issued.

[0207] The working principle of the above technical solution is: the system first extracts the edge node evaluation parameters corresponding to each edge node in the CDN network (such as the K value calculated by different evaluation factors before). These evaluation parameters reflect the performance and status of the edge node.

[0208] Next, the system compares the evaluation parameters of each edge node with the preset evaluation parameter threshold, which is set according to the normal operation standards and performance requirements of the CDN network.

[0209] According to the comparison result, the system divides the edge nodes into two groups: a first edge node group (nodes whose evaluation parameters exceed the threshold) and a second edge node group (nodes whose evaluation parameters do not exceed the threshold).

[0210] For the two groups of edge nodes, the system calculates their first comprehensive node evaluation parameter (Q01) and second comprehensive node evaluation parameter (Q02) respectively. These two parameters take into account the distribution of evaluation parameters of edge nodes in their respective groups, and are weighted and normalized by specific formulas.

[0211] Then, the system uses these two comprehensive node evaluation parameters to further calculate the comprehensive node evaluation parameter (Q). This parameter combines the performance of the two groups of edge nodes and is used to evaluate the health status of the entire CDN network.

[0212] Finally, the system compares the calculated comprehensive node evaluation parameter with the preset comprehensive evaluation parameter threshold. If the comprehensive node evaluation parameter is lower than the threshold, the system determines that there is an abnormality in the CDN network and triggers the abnormality alarm mechanism.

[0213] The effect of the above technical solution is: by real-time monitoring of the operating parameters of the CDN network, the system can timely discover potential performance problems or abnormal situations, thereby ensuring the stability and reliability of the network. By using the group processing and comprehensive evaluation parameter calculation method, the system can more accurately evaluate the health status of the CDN network. This helps to reduce false positives and false negatives and improve the accuracy of anomaly detection. The system supports adjusting the evaluation parameter threshold and the comprehensive evaluation parameter threshold according to the actual situation of the CDN network. This enables the system to adapt to different network environments and performance requirements, and improve flexibility and scalability. The entire process realizes automated monitoring and alarming, reducing the need for manual intervention. This helps to reduce operation and maintenance costs and improve operation and maintenance efficiency. The system records the evaluation parameters and grouping of edge nodes, as well as the calculation process of comprehensive node evaluation parameters. This provides strong support for subsequent troubleshooting and problem location. By timely discovering and handling abnormal situations in the CDN network, the system can ensure the stability and speed of user access to content, thereby improving the user's access experience.

[0214] The embodiment of the present invention proposes a file resource management system based on CDN, such as Figure 2 As shown, the file resource management system includes:

[0215] The network configuration module is used to configure the CDN network according to the resource management requirements set by the user;

[0216] A target edge node determination module is used for the CDN network to screen the target edge node corresponding to the user according to the file resource upload parameters of the user, and determine the target edge node corresponding to the user;

[0217] A data sending and sharing module, which is used for the CDN network to send and cache the content information of the real-time upload of the file resources to the target edge node inside the CDN network, and for the target edge node to share the data with other edge nodes of the CDN network;

[0218] The network anomaly monitoring module is used to monitor the operating parameters of the CDN network in real time and issue an anomaly alarm when an anomaly occurs in the CDN network.

[0219] The working principle of the above technical solution is to customize the CDN network according to the specific needs of users (such as geographical distribution, traffic prediction, security requirements, etc.). This includes selecting a suitable CDN service provider, configuring the network topology, setting cache strategies, etc., to ensure that the CDN network can meet the user's resource management needs.

[0220] When a user uploads a file resource, the CDN network will intelligently select the target edge node that is most suitable for storing and distributing the file resource based on the upload parameters provided by the user (such as geographic location, network conditions, file type, etc.). This step is designed to enable users to access the nearest location and reduce network latency.

[0221] Once the target edge node is determined, the CDN network will quickly send and cache the real-time content information of the file resources uploaded by the user to the node. Subsequently, the target edge node will share data with other edge nodes, and through the fast transmission mechanism within the CDN network, ensure that the file resources can quickly cover each node in the CDN network, and achieve wide distribution of content.

[0222] The CDN network will monitor its own operating parameters in real time, including but not limited to node load, cache hit rate, network latency, bandwidth usage, etc. Once an abnormal situation is found (such as node failure, network congestion, cache failure, etc.), the system will immediately trigger an alarm mechanism to notify the administrator or automatically take countermeasures to ensure the stable operation of the CDN network and the safe and reliable management of file resources.

[0223] The effects of the above technical solutions are: through the user's nearby access and the efficient data transmission mechanism within the CDN network, the access delay of file resources is significantly reduced, and the user experience is improved. Through multi-node deployment and load balancing technology, the access pressure of a single node is effectively dispersed, and the fault tolerance and availability of the system are improved. Intelligent caching strategies and data sharing mechanisms enable the CDN network to use storage and bandwidth resources more efficiently and reduce operating costs. Through real-time monitoring and abnormal alarm mechanisms, potential security threats can be discovered and responded to in a timely manner, ensuring the safe and reliable management of file resources. The original intention of the CDN network was to cope with the scenario of large-scale concurrent access by users. Through distributed storage and distribution technology, the pressure on the source server is effectively alleviated.

[0224] In summary, through a series of innovative designs and implementations, this technical solution has brought significant technical effects to file resource management, improved user experience, reduced operating costs, and enhanced system stability and security.

[0225] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A CDN-based file resource management method, characterized in that: The file resource management method comprises: Perform network configuration of the CDN network according to the resource management requirements set by the user; The CDN network screens the target edge node corresponding to the user according to the file resource upload parameters of the user, and determines the target edge node corresponding to the user; The CDN network sends and caches the real-time uploaded content information of the file resource to the target edge node within the CDN network, and the target edge node shares the data with other edge nodes of the CDN network; The operating parameters of the CDN network are monitored in real time, and an abnormality alarm is issued when an abnormality occurs in the CDN network.

2. The CDN-based file resource management method according to claim 1, characterized in that: Perform network configuration of the CDN network according to the resource management requirements set by the user, including: Real-time monitoring of whether the resource management demand information input by the user is received; Determining a CDN network corresponding to the resource management demand information of the user according to the resource management demand information input by the user; Perform CDN network configuration for the determined CDN network corresponding to the user's resource management demand information; The resource management demand information includes file resource types, file resource access volume and user data user group distribution area; the file resource types include pictures, videos and documents.

3. The CDN-based file resource management method according to claim 2, characterized in that: Perform CDN network configuration for the determined CDN network corresponding to the user's resource management requirement information, including: Configure the basic operating parameters of the CDN network according to the user's resource management demand information, wherein the basic operating parameters of the CDN network include downstream traffic, acceleration area, and acceleration package size; According to the acceleration zone corresponding to the user, the domain name corresponding to the acceleration zone is automatically added on the CDN console corresponding to the CDN network, and the domain name ownership is verified; After the domain name ownership verification is completed, the basic parameters of the server group corresponding to the user are configured; wherein the server group corresponding to the user is the source station corresponding to the user, and the basic parameters include the domain name, the back-to-source address, the back-to-source protocol and the source station weight; meanwhile, the source station weight refers to the initial weight parameter corresponding to each server included in the server group; The CDN network performs initial traffic distribution when the CDN node returns to the source according to the initial weight parameters corresponding to each server; The weight is adjusted according to the file resource upload amount of each server in the server group corresponding to the user, and the traffic distribution when the CDN node returns to the source in the next operation monitoring cycle is adjusted according to the adjusted weight parameter.

4. The CDN-based file resource management method according to claim 3, characterized in that: The weight adjustment is performed according to the file resource upload amount of each server in the server group corresponding to the user, including: Extract the time length corresponding to the preset operation monitoring cycle; According to the time length corresponding to the preset operation monitoring cycle, real-time monitoring of the file resource upload volume of each server in the server group within each operation monitoring cycle; The weight adjustment coefficient corresponding to each server is obtained by using the file resource upload amount of each server; wherein the weight adjustment coefficient is obtained by the following formula: Where s represents the weight adjustment coefficient corresponding to each server; n represents the number of file resource uploads of the server in the currently completed operation monitoring cycle; C i Indicates the upload data volume corresponding to the i-th file resource upload in the currently completed operation monitoring cycle; C max and C min Indicates the maximum and minimum values ​​of the uploaded data volume corresponding to the i-th file resource upload in the currently completed operation monitoring cycle; C px Indicates the average uploaded data volume of the file resources in the previous running monitoring cycle of the currently ended running monitoring cycle; C xmax and C xmin Indicates the maximum and minimum values ​​of the uploaded data volume of the file resource in the previous running monitoring cycle of the currently ended running monitoring cycle; c 01 and c 02 denote the first coefficient and the second coefficient respectively, and the first coefficient is obtained by the following formula: Among them, c 01 represents the first coefficient; m represents the number of times the file resource was uploaded in the previous operation monitoring cycle of the currently ended operation monitoring cycle; C j Indicates the data volume corresponding to the j-th file resource upload in the previous running monitoring cycle of the currently ended running monitoring cycle; C z01 Indicates the median value of the data volume corresponding to the file resource upload in the previous running monitoring cycle of the currently ended running monitoring cycle; And, the second coefficient is obtained by the following formula: Among them, c 02 represents the second coefficient; n represents the number of file resource uploads to the server in the currently completed operation monitoring cycle; C j Indicates the upload data volume corresponding to the i-th file resource upload in the currently completed operation monitoring cycle; C z02 Indicates the median value of the data volume corresponding to the file resource upload of the currently completed running monitoring cycle; Extracting the initial weight parameters corresponding to each server; The adjusted weight parameter corresponding to each server is obtained by using the weight adjustment coefficient corresponding to each server and the initial weight parameter corresponding to each server, wherein the adjusted weight parameter is obtained by the following formula: w t =(1+s)·w c Among them, w t represents the adjusted weight parameter corresponding to each server; w c represents the initial weight parameter corresponding to each server; s represents the weight adjustment coefficient corresponding to each server.

5. The CDN-based file resource management method according to claim 1, characterized in that: The CDN network screens the target edge node corresponding to the user according to the file resource upload parameters of the user, and determines the target edge node corresponding to the user, including: Extracting operation parameter data of each edge node in the CDN network, wherein the operation parameter data includes network status parameters and node status parameters; Obtaining a first evaluation factor using the network status parameter; Obtaining a second evaluation factor using the node state parameter; The edge node evaluation parameter is obtained by using the first evaluation factor and the second evaluation factor; wherein the edge node evaluation parameter is obtained by the following formula: Where K represents the edge node evaluation parameter; K 01 Represents the first evaluation factor; K 02 represents the second evaluation factor; The edge node corresponding to the maximum value of the edge node evaluation parameter is used as the target edge node corresponding to the user.

6. The CDN-based file resource management method according to claim 5, characterized in that: Obtaining a first evaluation factor using the network status parameter includes: Extracting network status parameters corresponding to each edge node in the CDN network; wherein the network status parameters include network delay and communication channel utilization; The first evaluation factor is obtained by using the network delay and the communication channel utilization, wherein the first evaluation factor is obtained by the following formula: Among them, K 01 represents the first evaluation factor; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P yi represents the network delay rate of data transmission corresponding to the i-th unit time of the edge node; P xi represents the communication channel utilization rate of data transmission occurring in the i-th unit time; P ymax Indicates the maximum value of the network delay rate corresponding to the edge node; P xmax represents the maximum utilization rate of the communication channel corresponding to the edge node; P xymax represents the network delay rate corresponding to the network delay when the communication channel utilization rate is at the maximum value; p represents the first evaluation adjustment parameter, and the first evaluation adjustment parameter is obtained by the following formula: Wherein, p represents the first evaluation adjustment parameter; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P xi represents the communication channel utilization rate of data transmission occurring in the i-th unit time; P xi-1 Represents the communication channel utilization rate of the data transmission occurring in the i-1th unit time.

7. The CDN-based file resource management method according to claim 5, characterized in that: Obtaining a second evaluation factor using the node state parameter includes: Extracting node status parameters corresponding to each edge node in the CDN network; wherein the node status parameters include the number of requests processed, bandwidth usage, and the number of server connections; The second evaluation factor is obtained by using the number of request processing, bandwidth usage and number of server connections, wherein the second evaluation factor is obtained by the following formula: Among them, K 02 represents the second evaluation factor; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P bi represents the bandwidth usage rate of data transmission occurring in the i-th unit time; M i represents the number of requests processed by the edge node corresponding to the i-th unit time; M 0i represents the number of server connections corresponding to the i-th unit time of the edge node; M k represents the evaluation adjustment coefficient, and the evaluation adjustment coefficient is obtained by the following formula: Among them, M k represents the evaluation adjustment coefficient; k represents the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; M i represents the number of requests processed by the edge node corresponding to the i-th unit time; M 0i Indicates the number of server connections corresponding to the i-th unit time of the edge node.

8. The CDN-based file resource management method according to claim 1, characterized in that: The CDN network sends and caches the real-time uploaded content information of the file resource to the target edge node within the CDN network, and the target edge node shares the data with other edge nodes of the CDN network, including: Sorting all edge nodes in the CDN network in descending order according to edge node evaluation parameters to obtain an edge node set; The CDN network sends and caches the real-time uploaded content information of the file resource to the target edge node corresponding to the user inside the CDN network; The target edge node shares data according to the order of precedence in the edge node set.

9. The CDN-based file resource management method according to claim 1, characterized in that: The operation parameters of the CDN network are monitored in real time, and an abnormality alarm is issued when an abnormality occurs in the CDN network, including: Extracting edge node evaluation parameters corresponding to each edge node of the CDN network; Comparing the edge node evaluation parameter with a preset evaluation parameter threshold; The edge nodes whose edge node evaluation parameters exceed the evaluation parameter threshold are taken as the first edge node group; The edge nodes whose edge node evaluation parameters do not exceed the evaluation parameter threshold are taken as the second edge node group; The first comprehensive node evaluation parameter is obtained by using the edge node evaluation parameter corresponding to the edge node included in the first edge node group, wherein the first comprehensive node evaluation parameter is obtained by the following formula: Among them, Q 01 represents the first comprehensive node evaluation parameter; a represents the number of edge nodes included in the first edge node group; K i represents the edge node evaluation parameter corresponding to the i-th edge node in the first edge node group; K amax and K amin represent the maximum value and the minimum value of the edge node evaluation parameter corresponding to the first edge node group respectively; K bmax Indicates the maximum value of the edge node evaluation parameter corresponding to the second edge node group; The second comprehensive node evaluation parameter is obtained by using the edge node evaluation parameter corresponding to the edge node included in the second edge node group, wherein the second comprehensive node evaluation parameter is obtained by the following formula: Among them, Q 02 represents the second comprehensive node evaluation parameter; b represents the number of edge nodes included in the second edge node group; K j represents the edge node evaluation parameter corresponding to the jth edge node in the second edge node group; K bmax and K bmin represent the maximum value and the minimum value of the edge node evaluation parameter corresponding to the second edge node group respectively; K amin Indicates the minimum value of the edge node evaluation parameter corresponding to the first edge node group; The first comprehensive node evaluation parameter and the second comprehensive node evaluation parameter are used to obtain a comprehensive node evaluation parameter; wherein the comprehensive node evaluation parameter is obtained by the following formula: Among them, Q represents the comprehensive node evaluation parameter; Q 01 represents the first comprehensive node evaluation parameter; Q 02 represents the second comprehensive node evaluation parameter; When the comprehensive node evaluation parameter is lower than a preset comprehensive evaluation parameter threshold, it is determined that an abnormality exists in the CDN network and an abnormality alarm is issued.

10. A CDN-based file resource management system, characterized in that: The file resource management system comprises: The network configuration module is used to configure the CDN network according to the resource management requirements set by the user; A target edge node determination module is used for the CDN network to screen the target edge node corresponding to the user according to the file resource upload parameters of the user, and determine the target edge node corresponding to the user; A data sending and sharing module, which is used for the CDN network to send and cache the real-time uploaded content information of the file resources to the target edge node inside the CDN network, and for the target edge node to share the data with other edge nodes of the CDN network; The network anomaly monitoring module is used to monitor the operating parameters of the CDN network in real time and issue an anomaly alarm when an anomaly occurs in the CDN network.

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