CDN-based file resource management system and method

Abstract

The present invention proposes a CDN-based file resource management system and method. The file resource management method includes: configuring the CDN network according to resource management requirements set by the user; the CDN network screening the target edge node corresponding to the user based on the user's file resource upload parameters to determine the target edge node corresponding to the user; the CDN network sending and caching the real-time uploaded content information of the file resource to the target edge node within the CDN network, and the target edge node sharing the data with other edge nodes of the CDN network; real-time monitoring of the operating parameters of the CDN network and issuing an abnormality alarm when an abnormality occurs in the CDN network. The system includes modules corresponding to the steps of the method.

Description

Technical Field

[0001] The present invention provides 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. When faced with large-scale concurrent access by users, this approach often leads to problems such as excessive server load, increased network latency, and decreased user experience. These problems are particularly prominent in cross-regional and cross-operator network environments. In traditional management, all user requests need 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 access. 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-mentioned technical problems in the prior art. The technical solutions adopted are as follows:

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

[0005] Perform CDN network configuration based on user-set resource management requirements;

[0006] The CDN network screens the target edge node corresponding to the user according to the user's file resource upload parameters 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 resources 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 CDN network is configured according to the resource management requirements set by the user, including:

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

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

[0012] Perform CDN network configuration based on the determined CDN network corresponding to the user's resource management requirements;

[0013] The resource management demand information includes file resource types, file resource access volume, and user data user group distribution areas; 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 user's resource management requirement information, including:

[0015] Configuring the basic operating parameters of the CDN network based on the user's resource management requirements, including downlink traffic, acceleration regions, and acceleration packet size;

[0016] Automatically add the 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;

[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, back-to-source address, back-to-source protocol and source station weight; at the same time, 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 of the CDN node back to the source in the next operation monitoring cycle is adjusted according to the adjusted weight parameter.

[0020] Furthermore, weight adjustment is performed according to the file resource upload volume 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 to the server in the currently completed operation monitoring cycle; C i Indicates the amount of uploaded data corresponding to the i-th file resource upload in the currently completed running monitoring cycle; C max and C min Indicates the maximum and minimum 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 resource in the previous monitoring cycle of the currently completed 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 completed 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 completed running monitoring cycle; C z01 Indicates the median data volume corresponding to the file resource upload in the previous running monitoring cycle before the currently completed running monitoring cycle;

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

[0029]

[0030] 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 amount of uploaded data corresponding to the i-th file resource upload in the currently completed running monitoring cycle; C z02 Indicates the median data volume corresponding to the file resource uploads during the currently completed 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 Indicates 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 user's file resource upload parameters, and determines the target edge node corresponding to the user, including:

[0036] Extracting operating parameter data of each edge node in the CDN network, wherein the operating 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 first evaluation factor and the second evaluation factor are used to obtain an edge node evaluation parameter; wherein the edge node evaluation parameter is obtained by the following formula:

[0040]

[0041] Among them, 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] Furthermore, 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] A 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 P represents the communication channel utilization rate of data transmission occurring in the i-th unit time; 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 is at its maximum; 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 P represents the communication channel utilization rate of data transmission occurring in the i-th unit time; xi-1 Indicates the communication channel utilization rate of the data transmission occurring in the i-1th unit time.

[0050] Furthermore, 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] A second evaluation factor is obtained 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 of data transmission occurring in the i-th unit time; M i Indicates 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 Indicates 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 within 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 within the CDN network;

[0060] The target edge nodes share data according to the order in the edge node set.

[0061] Furthermore, 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, 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 a 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 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:

[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 They represent the maximum value and 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 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:

[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 K represents the edge node evaluation parameter corresponding to the j-th edge node in the second edge node group; bmax and K bmin They represent the maximum value and 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 the preset comprehensive evaluation parameter threshold, it is determined that there is an abnormality in the CDN network and an abnormality alarm is issued.

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

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

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

[0079] The data transmission and sharing module is used for the CDN network to transmit and cache the content information of the real-time uploaded file resources to the target edge node within the CDN network, and 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 nearest 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. The intelligent caching strategy and data sharing mechanism enable the CDN network to utilize storage and bandwidth resources more efficiently, reducing 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 design of the CDN network is 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 A flow chart of the method of the present invention;

[0084] Figure 2 This is a system block diagram of the system of the present invention. DETAILED DESCRIPTION

[0085] The preferred embodiments of the present invention are described below with reference to 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 file resource management method based on CDN, such as Figure 1As shown, the file resource management method includes:

[0087] S1. Configure 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 user's file resource upload parameters 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 within 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 generate 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 configuration according to the user's specific needs (such as geographical distribution, traffic forecast, security requirements, etc.). This includes selecting the appropriate CDN service provider, configuring the network topology, setting caching strategies, etc. to ensure that the CDN network can meet the user's resource management needs.

[0092] When a user uploads a file, the CDN network intelligently selects the most suitable edge node for storing and distributing the file based on the user's upload parameters (such as geographic location, network conditions, and file type). This step aims to provide users with access to the nearest node and reduce network latency.

[0093] Once the target edge node is identified, the CDN network will quickly send and cache the real-time content information of the user's uploaded file resources to that node. The target edge node will then share data with other edge nodes. Through the CDN network's internal fast transmission mechanism, the file resources can be quickly covered by various nodes in the CDN network, achieving widespread content distribution.

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

[0095] The effects of the above technical solutions are as follows: through user proximity access and efficient data transmission mechanisms within the CDN network, access latency to file resources is significantly reduced, improving the user experience. Through multi-node deployment and load balancing technology, the access pressure on a single node is effectively dispersed, improving the system's fault tolerance and availability. Intelligent caching strategies and data sharing mechanisms enable the CDN network to more efficiently utilize storage and bandwidth resources, reducing operating costs. Through real-time monitoring and abnormal alarm mechanisms, potential security threats are promptly discovered and responded to, ensuring the safe and reliable management of file resources. The CDN network was originally designed to cope with scenarios of large-scale concurrent user access. Through distributed storage and distribution technologies, it effectively alleviates the pressure on the source server.

[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 CDN network configuration 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 the user is received;

[0099] S102: Determine a CDN network corresponding to the resource management requirement information of the user according to the resource management requirement information input by the user;

[0100] S103: Perform 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 areas; the file resource types include pictures, videos, and documents.

[0102] The working principle of the above technical solution is that the system continuously monitors whether there is any resource management request information input by the user. This is usually received through a user interface (such as a web page, API interface, etc.). Once the user submits the request information, the system proceeds to the next step of the processing flow.

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

[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 determining the CDN network, the system will configure it accordingly based on the user's resource management requirements. This includes setting cache policies (such as cache time and cache space size), load balancing policies, and security policies 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 highly scalable and can easily cope with possible business growth or changes in the future.

[0107] During CDN network configuration, you can integrate multiple security policies (such as data encryption and access control) 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 implementing personalized configuration of the CDN network based on 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 a user's resource management requirement information, including:

[0110] S1031. Configuring basic operating parameters of the CDN network based on the user's resource management requirements, wherein the basic operating parameters of the CDN network include downstream traffic, acceleration regions, and acceleration packet size;

[0111] S1032. Automatically add the 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, back-to-source address, back-to-source protocol, and source station weight; at the same time, 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 distribution 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 of the CDN node back to the source in the next operation monitoring cycle according to the adjusted weight parameter.

[0115] The above technical solution works by setting the basic operating parameters of the CDN network based on the user's resource management requirements (such as expected traffic volume and content type). These parameters include, but are not limited to, downstream traffic limits, acceleration region selection (which determines the geographical locations where CDN nodes will be deployed to optimize access speed), and acceleration packet size (which affects cache efficiency and response speed).

[0116] On the CDN console, the corresponding domain name is automatically added based on the user's specified acceleration zone. This step ensures that the CDN network can correctly identify and process requests from the user's domain name. Domain ownership verification is then performed to ensure that only legitimate domain owners can perform subsequent configurations, enhancing system security.

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

[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 origin servers based on the preset weights to balance the load and ensure service stability.

[0119] Over time, the system dynamically adjusts the weight of the origin server based on actual operational data, such as the file resource upload volume of each server in the corresponding server cluster. This is to reflect the server's current load capacity and performance, ensuring efficient resource utilization within the CDN network. The adjusted weight parameters will be used to distribute traffic back to the origin during the next monitoring cycle, further optimizing the user experience and CDN network performance.

[0120] The effect of the above technical solution is as follows: this technical solution allows for dynamic configuration adjustment based on the actual needs of users and the operating conditions of the CDN network, enabling the CDN network to flexibly respond to various changes and improve the adaptability and stability of the system. Through reasonable weight distribution and traffic redistribution mechanisms, balanced management of the source station load can be achieved, avoiding 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 enhancing the user experience. Security measures such as domain name ownership verification enhance the security of the CDN network, preventing the access of illegal domain names and potential security threats. Reasonable CDN network configuration and traffic distribution strategies 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 based on the file resource upload volume 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: monitoring the file resource upload volume of each server in the server group in 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 volume 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 to the server in the currently completed operation monitoring cycle; C i Indicates the amount of uploaded data corresponding to the i-th file resource upload in the currently completed running monitoring cycle; C max and C minIndicates the maximum and minimum 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 resource in the previous monitoring cycle of the currently completed 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 completed 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 completed running monitoring cycle; C z01 Indicates the median data volume corresponding to the file resource upload in the previous running monitoring cycle before the currently completed running monitoring cycle;

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

[0130]

[0131] 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 amount of uploaded data corresponding to the i-th file resource upload in the currently completed running monitoring cycle; C z02 Indicates the median data volume corresponding to the file resource uploads during the currently completed 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 Indicates 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. During each operation monitoring cycle, the system monitors and records the file resource upload volume of each server in the server cluster in real time. This includes the number of uploads and the amount of data uploaded each time. Using this collected data, the system calculates a corresponding weight adjustment coefficient for each server. This coefficient is derived based on a comparison of the upload data volume between the current operation monitoring cycle and the previous operation monitoring cycle, using a complex mathematical formula (including median value, average upload data volume, number of uploads, etc.). Its purpose is to reflect the server's load capacity and performance during the current cycle. The system extracts the initial weight parameters set for each server during CDN network configuration. These parameters are set at the start of the CDN network and are used to initially distribute traffic back to the CDN node during back-to-origin traffic. The system combines each server's weight adjustment coefficient with the initial weight parameters to calculate the adjusted weight parameter for each server through a formula. This adjusted weight will be used to distribute traffic back to the CDN node during the next operation monitoring cycle.

[0137] The effect of the above technical solution is that by real-time monitoring and dynamic adjustment of server weights, the system can ensure more balanced traffic distribution during back-to-origin traffic between CDN nodes. This helps avoid situations where some servers are overloaded while others are idle, improving the overall performance and stability of the system. The weight adjustment mechanism enables the CDN network to more efficiently utilize server resources. Servers with better performance will take on more back-to-origin tasks, while servers with lower performance will have their workload reduced, thereby optimizing resource allocation. This technical solution dynamically adjusts weights based on the actual operating conditions of the servers, enhancing the system's adaptability and flexibility. Regardless of server load fluctuations, the system can maintain stable operation of the CDN network by adjusting weights.

[0138] By optimizing the CDN network's traffic distribution strategy, the system can reduce latency for users accessing file resources, improve response speed and access success rates, and thus enhance the user experience. A rational weight adjustment mechanism helps reduce downtime and maintenance costs caused by server overload. Furthermore, by improving system performance and stability, it can also reduce bandwidth and storage resource waste caused by failures. This technical solution incorporates complex mathematical formulas and algorithms to calculate weight adjustment coefficients, enabling intelligent management of CDN network traffic distribution. This not only improves management accuracy and efficiency, 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 based on the user's file resource upload parameters, and determines the target edge node corresponding to the user, including:

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

[0141] S202: Obtain 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] Among them, 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. This data includes network status parameters (such as network latency, 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), which 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 and second evaluation factors, the system calculates the evaluation parameter (K) for 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 identifies the edge node with the maximum evaluation parameter as the target edge node for the user. This means that this node exhibits 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 above technical solution achieves the following: By selecting the edge node with the best performance as the target node, the system ensures that user file resource upload requests are processed quickly and stably. This helps reduce upload latency and improve upload success rates, thereby optimizing the user experience. The introduction of edge node evaluation parameters enables a more rational resource allocation within the CDN network. Poorly performing edge nodes will have fewer opportunities to process user requests, while higher-performing nodes will take on more tasks. This helps improve resource utilization and overall system performance. By dynamically screening target edge nodes, the system can promptly identify and avoid nodes that may be experiencing failures or performance degradation. This helps enhance system stability and reliability, reducing the risk of service interruptions and data loss caused by node failures. This technical solution implements intelligent management of edge nodes within the CDN network. By comprehensively evaluating multiple performance indicators of edge nodes, the system automatically selects the optimal node to process user requests, reducing the need for manual intervention and error rates. This technical solution is highly adaptable. As the CDN network scales and user needs change, the system can flexibly adjust the weighting of evaluation factors and calculation methods to adapt to different application scenarios and needs.

[0153] On the other hand, the above technical solution combines two different evaluation factors, enabling a more comprehensive assessment of edge nodes. The first evaluation factor reflects a specific node's network status, while the second reflects other characteristics (such as node stability or load). By integrating these two evaluation factors, the formula can more accurately determine which edge node is most suitable as a user's target node. The logarithmic function and weighting coefficients in the formula allow for the relative importance of the two evaluation factors to be adjusted in practice, thereby dynamically adjusting the node selection strategy based on the actual network environment. For example, when a certain evaluation factor becomes more important, its weight can be increased, giving it a greater weight in the final evaluation. This weighted evaluation method balances performance (such as response time and throughput) with reliability (such as node availability and failure rate) when selecting nodes, thereby selecting the edge node that best meets user needs under current conditions. By comprehensively considering different node evaluation factors, the formula in this technical solution enhances the flexibility and accuracy of the CDN network when selecting edge nodes, 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. Extract 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, 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 P represents the communication channel utilization rate of data transmission occurring in the i-th unit time; 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 is at its maximum; 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 P represents the communication channel utilization rate of data transmission occurring in the i-th unit time; xi-1 Indicates 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 latency (Pyi) and communication channel utilization (Pxi). This data is collected in real time or periodically, reflecting the real-time performance of the edge node 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 (e.g., 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 is maximum. These values are used for subsequent calculations and comparisons.

[0164] Using this data, the system calculates the first evaluation adjustment parameter (p). This parameter takes into account the changes in communication channel utilization and evaluates the stability of edge nodes when network load changes by comparing communication channel utilization within 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) using a given formula. This factor integrates 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 above technical solution achieves the following: By comprehensively considering two key metrics: network latency and communication channel utilization, the system can more accurately assess the network performance of each edge node in the CDN network. This helps identify performance bottlenecks and potential problem areas. The introduction of the first evaluation adjustment parameter allows the evaluation process to account for network load fluctuations. By comparing communication channel utilization within adjacent time units, the system can assess the stability and responsiveness of edge nodes in the face of network load fluctuations. Based on the evaluation results of the first evaluation factor, the system can more rationally allocate resources within the CDN network. Edge nodes with better performance will handle more user requests, thereby improving overall service quality and user experience. By regularly or in real time evaluating the network performance of edge nodes, the system can promptly identify and resolve potential network issues. This helps reduce the risk of service interruptions and data loss caused by network failures, and improves system stability and reliability. This technical solution provides strong support for intelligent management of CDN networks. Through automated evaluation and adjustment processes, the system can automatically optimize resource allocation and service quality, reducing the need for human intervention and the risk of errors.

[0167] Furthermore, by considering two key network status parameters, network latency and communication channel utilization, we can quantitatively reflect the performance of edge nodes during data transmission. Specifically, nodes with lower network latency and higher communication channel utilization receive a higher first evaluation factor, indicating 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 a 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 α and p can provide a more accurate and targeted evaluation basis for subsequent node selection. This improved 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 assessment 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. Extract 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;

[0172] S2032. Obtain a second evaluation factor using the number of processed requests, 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 of data transmission occurring in the i-th unit time; M i Indicates 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 Indicates 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 above technical solution works as follows: the system first extracts 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 generally an important indicator for evaluating node status), and the number of server connections (M0i). This data reflects the edge node's ability to process user requests and maintain server connections.

[0178] For each edge node, the system calculates the number of requests processed (Mi) and the number of server connections (M0i) per unit time (e.g., 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 assess the stability and efficiency of edge nodes in processing requests.

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

[0181] The above technical solution provides a comprehensive performance assessment of each edge node in the CDN network by considering the number of requests processed, the number of server connections, and the proportional relationship between them. This helps identify nodes with insufficient processing capacity or poor connectivity. The introduction of an evaluation adjustment factor allows the evaluation process to account for fluctuations in the number of requests processed. By dynamically adjusting the evaluation factor, the system incentivizes edge nodes to maintain high efficiency and stability in request processing, thereby improving overall request processing efficiency. The calculation of the second evaluation factor considers the balance between the number of requests processed and the number of server connections. This helps avoid excessive resource concentration in certain nodes, leaving others with insufficient resources, thereby optimizing resource allocation and utilization within the CDN network. By regularly or in real time evaluating edge node status parameters, the system can promptly identify and resolve potential performance issues. This helps reduce the risk of service interruptions and data loss caused by node failures or performance bottlenecks, thereby improving system stability and reliability. This technical solution provides strong support for intelligent management of CDN networks. Through automated evaluation and adjustment processes, the system can automatically optimize node performance and service quality, reducing the need for human intervention and the risk of errors.

[0182] The second evaluation factor, on the other hand, integrates multiple key network status parameters, such as the number of requests processed, bandwidth utilization, and the number of server connections, to provide a multi-dimensional assessment of node performance. This evaluation method helps comprehensively measure a node's service capabilities under different network conditions, ensuring that the selected node can effectively meet user needs. Furthermore, Mi and M0i in the second evaluation factor represent the number of requests processed and the number of server connections, respectively. These parameters reflect the edge node's processing capacity and resource utilization in actual operation. By comparing these parameters, the formula can assess whether a node maintains efficient operation under high or low load conditions. Furthermore, the second evaluation factor dynamically adjusts the evaluation factor by comparing the minimum and maximum processing capacity within each time period. This adjustment mechanism flexibly adapts to changes in network conditions based on actual operation, ensuring the accuracy and real-time nature of the evaluation results. Furthermore, the second evaluation factor considers the balance between node load and service capacity when calculating the node evaluation factor. This balancing mechanism avoids evaluation bias caused by either overestimating or underestimating a single parameter, resulting in a more rational selection of suitable target edge nodes. The above technical solution can better select edge nodes with excellent performance and load balancing, 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 uploaded file resources to a target edge node within the CDN network, and the target edge node shares the data with other edge nodes of the CDN network, including:

[0185] S301: Sort all edge nodes within 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 within the CDN network;

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

[0188] The above technical solution works as follows: First, the CDN network sorts all edge nodes in descending order based on the previously calculated edge node evaluation parameter (such as the K value). This sorting process generates a set of edge nodes, with the node with the highest evaluation parameter 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 is cached on the target edge node, it will share the data with other edge nodes in the CDN network in the order of the previously sorted edge node set. This means that data will be shared first with the node with the next highest evaluation parameter, and so on, until all edge nodes that need to share the data have received it.

[0191] The above technical solution optimizes data distribution efficiency by ranking edge nodes according to evaluation parameters and prioritizing data sharing with nodes with better performance. This helps reduce data transmission latency and improve user access speed. This technical solution allows the CDN network to dynamically expand edge nodes when needed. When new nodes join the network, the system recalculates and re-ranks edge node evaluation parameters, ensuring efficient and accurate data sharing. Because data sharing is based on edge node evaluation parameters, the system can automatically adjust data sharing strategies to avoid data loss or service interruptions even if some nodes in the network fail or experience performance degradation. Through a rational data sharing strategy, the CDN network can reduce unnecessary data transmission and duplicate caching, thereby lowering bandwidth and storage costs. End users benefit from the CDN network's efficient data distribution and caching mechanisms. Users will enjoy fast and stable access to file resources in the CDN network regardless of their geographic location.

[0192] In one embodiment of the present invention, real-time monitoring of the operating parameters of the CDN network and generating an abnormality alarm 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: Compare the edge node evaluation parameter with a preset evaluation parameter threshold;

[0195] S403: The edge nodes whose edge node evaluation parameters exceed the evaluation parameter threshold are taken as a first edge node group;

[0196] S404: The edge nodes whose edge node evaluation parameters do not exceed the evaluation parameter threshold are taken as a 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 aminThey represent the maximum value and 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 using 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 K represents the edge node evaluation parameter corresponding to the j-th edge node in the second edge node group; bmax and K bmin They represent the maximum value and 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 as follows: 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). 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. This threshold is set according to the normal operation standards and performance requirements of the CDN network.

[0209] Based on the comparison results, 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). These two parameters take into account the distribution of evaluation parameters of edge nodes in their respective groups and are weighted and normalized using specific formulas.

[0211] The system then 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 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 above technical solution achieves the following: By monitoring CDN network operating parameters in real time, the system can promptly identify potential performance issues or anomalies, thereby ensuring network stability and reliability. By utilizing group processing and comprehensive evaluation parameter calculation methods, the system can more accurately assess the health of the CDN network. This helps reduce false positives and false negatives, and improves the accuracy of anomaly detection. The system supports adjusting evaluation parameter thresholds and comprehensive evaluation parameter thresholds based on the actual CDN network conditions. This allows the system to adapt to different network environments and performance requirements, improving flexibility and scalability. The entire process implements automated monitoring and alarming, reducing the need for manual intervention. This helps reduce operation and maintenance costs and improves 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 promptly detecting and addressing CDN network anomalies, the system ensures stable and fast content access for users, thereby improving the user 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] The target edge node determination module is used for the CDN network to screen the target edge node corresponding to the user according to the user's file resource upload parameters and determine the target edge node corresponding to the user;

[0217] The data transmission and sharing module is used for the CDN network to transmit and cache the content information of the real-time uploaded file resources to the target edge node within the CDN network, and 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 configuration according to the user's specific needs (such as geographical distribution, traffic forecast, security requirements, etc.). This includes selecting the appropriate CDN service provider, configuring the network topology, setting caching strategies, etc. to ensure that the CDN network can meet the user's resource management needs.

[0220] When a user uploads a file, the CDN network intelligently selects the most suitable edge node for storing and distributing the file based on the user's upload parameters (such as geographic location, network conditions, and file type). This step aims to provide users with access to the nearest node and reduce network latency.

[0221] Once the target edge node is identified, the CDN network will quickly send and cache the real-time content information of the user's uploaded file resources to that node. The target edge node will then share data with other edge nodes. Through the CDN network's internal fast transmission mechanism, the file resources can be quickly covered by various nodes in the CDN network, achieving widespread content distribution.

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

[0223] The effects of the above technical solutions are as follows: through user proximity access and efficient data transmission mechanisms within the CDN network, access latency to file resources is significantly reduced, improving the user experience. Through multi-node deployment and load balancing technology, the access pressure on a single node is effectively dispersed, improving the system's fault tolerance and availability. Intelligent caching strategies and data sharing mechanisms enable the CDN network to more efficiently utilize storage and bandwidth resources, reducing operating costs. Through real-time monitoring and abnormal alarm mechanisms, potential security threats are promptly discovered and responded to, ensuring the safe and reliable management of file resources. The CDN network was originally designed to cope with scenarios of large-scale concurrent user access. Through distributed storage and distribution technologies, it effectively alleviates the pressure on the source server.

[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 may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if such changes and modifications fall within the scope of the claims and their equivalents, the present invention is intended to include such changes and modifications.

Claims

1. A CDN-based file resource management method, characterized in that: The file resource management method includes: The CDN network is configured according to the resource management requirements set by the user. The CDN network configuration includes adjusting the weight of the file resource upload volume of each server in the server group corresponding to the user, and adjusting the traffic distribution of the CDN node back to the source in the next operation monitoring cycle according to the adjusted weight parameter. The adjusted weight parameter is obtained by the following formula: in, w t Indicates the adjusted weight parameter corresponding to each server; w c Indicates the initial weight parameter corresponding to each server; s Indicates the weight adjustment coefficient corresponding to each server, which is obtained by the following formula: in, s Indicates the weight adjustment coefficient corresponding to each server; n Indicates the number of file resource uploads to the server during the currently completed monitoring cycle; C i Indicates the number of the currently completed monitoring cycle. i The amount of uploaded data corresponding to this file resource upload; C max and C min Indicates the number of the currently completed monitoring cycle. i The maximum and minimum upload data volumes corresponding to this file resource upload; C px Indicates the average uploaded data volume of the file resource in the previous monitoring cycle of the currently completed 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; The CDN network screens the target edge node corresponding to the user according to the user's file resource upload parameters, and determines the target edge node corresponding to the user; wherein the target edge node is the edge node corresponding to the maximum value of the edge node evaluation parameter; wherein the edge node evaluation parameter is obtained by the following formula: in, K represents the evaluation parameters of edge nodes; K 01 represents the first evaluation factor; K 02 represents the second evaluation factor; The CDN network sends and caches the real-time uploaded content information of the file resources 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; Monitor the operating parameters of the CDN network in real time and issue an abnormality alarm 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 a 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 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: in, Q 01 represents the first comprehensive node evaluation parameter; a Indicates the number of edge nodes included in the first edge node group; K i Indicates the first edge node group i Edge node evaluation parameters corresponding to edge nodes; 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 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: in, Q 02 represents the second comprehensive node evaluation parameter; b Indicates the number of edge nodes included in the second edge node group; K j Indicates the first edge node in the second edge node group j Edge node evaluation parameters corresponding to edge nodes; 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: in, Q represents comprehensive node evaluation parameters; 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 the preset comprehensive evaluation parameter threshold, it is determined that there is an abnormality in the CDN network and an abnormality alarm is issued.

2. The CDN-based file resource management method according to claim 1, characterized in that: Perform CDN network configuration based on user-defined resource management requirements, 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 requirement information of the user according to the resource management requirement information input by the user; Perform CDN network configuration based on the determined CDN network corresponding to the user's resource management requirements; The resource management demand information includes file resource types, file resource access volume, and user data user group distribution areas; 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 based on the determined CDN network that corresponds to the user's resource management requirements, including: Configuring the basic operating parameters of the CDN network based on the user's resource management requirements, wherein the basic operating parameters of the CDN network include downstream traffic, acceleration area, and acceleration packet size; Automatically add the 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; 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, back-to-source address, back-to-source protocol and source station weight; at the same time, 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 of the CDN node back 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 is adjusted 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 combining the file resource upload amount of each server with the first coefficient and the second coefficient; wherein the first coefficient is obtained by the following formula: in, c 01 represents the first coefficient; m Indicates the number of times the file resource was uploaded in the previous monitoring cycle before the currently completed monitoring cycle. C j Indicates the number of the previous running monitoring cycle that has ended. j The amount of data corresponding to the file resource upload; C z01 Indicates the median data volume corresponding to the file resource upload in the previous running monitoring cycle before the currently completed running monitoring cycle; Furthermore, the second coefficient is obtained by the following formula: in, c 02 represents the second coefficient; n Indicates the number of file resource uploads to the server during the currently completed monitoring cycle; C j Indicates the number of the currently completed monitoring cycle. i The amount of uploaded data corresponding to this file resource upload; C z02 Indicates the median data volume corresponding to the file resource uploads during the currently completed 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.

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 user's file resource upload parameters, and determines the target edge node corresponding to the user, including: Extracting operating parameter data of each edge node in the CDN network, wherein the operating 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; Obtaining edge node evaluation parameters using the first evaluation factor and 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; A 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: in, K 01 represents the first evaluation factor; k Indicates the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P yi Indicates the edge node corresponding to i The network delay rate of data transmission per unit time; P xi Indicates the i Communication channel utilization rate for data transmission occurring per unit time; P ymax Indicates the maximum value of the network delay rate corresponding to the edge node; P xmax Indicates the maximum utilization rate of the communication channel corresponding to the edge node; P xymax Indicates the network delay rate corresponding to the network delay that occurs when the communication channel utilization is maximum; p represents the first evaluation adjustment parameter, and the first evaluation adjustment parameter is obtained by the following formula: ； in, p represents the first evaluation adjustment parameter; k Indicates the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P xi Indicates the i Communication channel utilization rate for data transmission occurring per unit time; P xi-1 Indicates the i - The communication channel utilization rate for data transmission occurring per 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; A second evaluation factor is obtained using the number of request processing, bandwidth usage, and number of server connections, wherein the second evaluation factor is obtained by the following formula: in, K 02 represents the second evaluation factor; k Indicates the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; P bi Indicates the i Bandwidth usage of data transmission per unit time; M i Indicates the edge node i The number of requests processed per unit time; M 0i Indicates the edge node i The number of server connections per unit time; M k represents the evaluation adjustment coefficient, and the evaluation adjustment coefficient is obtained by the following formula: in, M k represents the evaluation adjustment coefficient; k Indicates the number of unit times contained in the data running time corresponding to each edge node, and the unit time is 1s; M i Indicates the edge node i The number of requests processed per unit time; M 0i Indicates the edge node i The number of server connections per unit time.

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 within 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 within the CDN network; The target edge nodes share data according to the order in the edge node set.

9. A CDN-based file resource management system is used to execute the CDN-based file resource management method according to any one of claims 1 to 8, characterized in that: The file resource management system includes: The network configuration module is used to configure the CDN network according to the resource management requirements set by the user; The target edge node determination module is used for the CDN network to screen the target edge node corresponding to the user according to the user's file resource upload parameters and determine the target edge node corresponding to the user; The data transmission and sharing module is used for the CDN network to transmit and cache the content information of the real-time uploaded file resources to the target edge node within the CDN network, and 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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