CDN (Content Delivery Network) route finding method and device and electronic equipment

Abstract

The embodiment of the invention provides a CDN routing way-finding method and device and electronic equipment, and the method comprises the steps: obtaining CDN way-finding request information which is used for requesting a routing result; based on the CDN path-finding request information, the CDN network topology information and a CDN routing selection model, obtaining a routing result matched with the CDN path-finding request; wherein the CDN routing selection model is used for outputting a routing result.

Description

Technical Field

[0001] The present application relates to the technical field of content delivery network (CDN), and in particular to a CDN routing method, device and electronic device. Background Art

[0002] In Internet applications, CDN technology is an important means to ensure the quality of Internet services. CDN technology improves the speed and quality of users' access to Internet services by distributing and caching content globally. However, due to the complexity and dynamics of CDN network topology, CDN's routing strategy may not be able to maximize user experience, causing inconvenience and poor experience to users.

[0003] In the related art, CDN routing strategy is mainly based on artificially pre-set rules and static CDN network topology. This method often cannot adapt to the changes in CDN network topology and the diversity of user requests. For example, in some cases, a routing result may be optimal, but in other cases, it may cause bottlenecks and delays. Therefore, how to find a better routing result is an important issue facing current CDN technology. Summary of the invention

[0004] The purpose of the embodiments of the present application is to provide a CDN routing method, device and electronic device, which can solve the problem of not being able to obtain better routing results in the CDN network.

[0005] To solve the above technical problems, the embodiments of the present application are implemented through the following aspects.

[0006] In a first aspect, an embodiment of the present application provides a CDN routing method, comprising: obtaining CDN routing request information, wherein the CDN routing request information is used to request a routing result; based on the CDN routing request information, CDN network topology information and a CDN routing selection model, obtaining a routing result that matches the CDN routing request; wherein the CDN routing selection model is used to output a routing result.

[0007] In a second aspect, an embodiment of the present application provides a CDN routing pathfinding device, comprising: an acquisition module, used to obtain CDN pathfinding request information, wherein the CDN pathfinding request information is used to request a routing result; an output module, used to obtain a routing result matching the CDN pathfinding request based on the CDN pathfinding request information, CDN network topology information and a CDN routing selection model; wherein the CDN routing selection model is used to output the routing result.

[0008] In a third aspect, an embodiment of the present application provides an electronic device comprising: a memory, a processor, and computer executable instructions stored in the memory and executable on the processor, wherein the computer executable instructions, when executed by the processor, implement the steps of executing the method described in the first aspect.

[0009] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which is used to store computer-executable instructions. When the computer-executable instructions are executed by a processor, the steps of the method described in the first aspect are implemented.

[0010] In the embodiment of the present application, routing results are obtained based on CDN network topology information and a CDN routing selection model. The CDN routing selection model can automatically learn and optimize routing selection strategies. Through continuous trial and error and learning, the routing selection strategies can be gradually optimized, thereby improving the service quality and user experience of the CDN. Routing results are obtained based on real-time CDN network topology information, which is conducive to obtaining the optimal routing results and improving the service quality and user experience of the CDN. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0012] Figure 1 A schematic diagram showing a flow chart of a CDN routing method provided by an embodiment of the present application;

[0013] Figure 2 A schematic diagram of a system block diagram of a CDN routing method provided by an embodiment of the present application is shown;

[0014] Figure 3 A schematic diagram showing the structure of a CDN routing pathfinding device provided in an embodiment of the present application is shown;

[0015] Figure 4 A schematic diagram of the hardware structure of an electronic device for executing the CDN routing method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0017] Figure 1 A flow chart of a CDN routing method provided by an embodiment of the present application is shown, and the method can be executed by an electronic device, such as a terminal device or a server device. In other words, the method can be executed by software or hardware installed in a terminal device or a server device. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. Figure 1 As shown, the method may include the following steps.

[0018] Step S10: Obtain CDN routing request information, where the CDN routing request information is used to request a routing result.

[0019] Optionally, the CDN routing request information may include at least one of the following: user IP address, MD5 data of user requested content, and user special request file information.

[0020] Step S20: Based on the CDN routing request information, the CDN network topology information and the CDN routing selection model, a routing result matching the CDN routing request is obtained; wherein the CDN routing selection model is used to output the routing result.

[0021] Before executing this embodiment, the CDN routing selection model can be trained based on CDN routing request information, real-time CDN network topology information, and the best routing result matching the CDN routing request. The CDN routing selection model can automatically learn and optimize the routing selection strategy. Through continuous trial and error and learning, the routing selection strategy can be gradually optimized, thereby improving the service quality and user experience of the CDN.

[0022] Optionally, the CDN network topology information (or CDN network topology map) may include at least one of the following: CDN network element service operation information, CDN network link information, and CDN network element hardware indicator information. The CDN network topology information in this embodiment can be obtained in real time, and compared with the solution of obtaining routing results based on a static CDN network topology structure in the related art, the optimal routing result can be obtained, thereby improving the service quality and user experience of the CDN.

[0023] This embodiment may also include at least one of the following steps: 1) obtaining the CDN network element operation log from the CDN network's log file system, and obtaining the CDN network element service operation information based on the CDN network element operation log; 2) obtaining the CDN network link status information based on the CDN network's network link status log; 3) obtaining the CDN network element operation parameters from the CDN network's data collection interface, and obtaining the CDN network element hardware indicator information based on the CDN network element operation parameters. This embodiment is conducive to obtaining real-time CDN network topology information, and further conducive to obtaining the optimal routing results, improving the CDN service quality and user experience.

[0024] Optionally, in this embodiment, the routing result that matches the CDN routing request includes one of the following: 1) the best routing result that matches the user IP address and the MD5 data of the user requested content, and the best routing result may have the lowest delay, the highest reliability quality, etc.; 2) the routing result that matches the file information of the user's special requirements, and the routing result may be the best routing result that meets the user's special requirements.

[0025] Optionally, in various embodiments of the present application, after obtaining a routing result that matches the CDN routing request, the method further includes: passing the routing result to a CDN control network element, so that the CDN control network element realizes the routing path arrival of the CDN by redirecting the user service.

[0026] The embodiments of the present application provide a CDN routing method, which obtains routing results based on CDN network topology information and a CDN routing selection model. The CDN routing selection model can automatically learn and optimize routing selection strategies. Through continuous trial and error and learning, the routing selection strategies can be gradually optimized, thereby improving the service quality and user experience of the CDN. Routing results are obtained based on real-time CDN network topology information, which is conducive to obtaining the optimal routing results and improving the service quality and user experience of the CDN.

[0027] Optionally, Figure 1 The CDN network topology information in the illustrated embodiment includes a CDN network topology diagram, Figure 1 The illustrated embodiment also includes the following steps: obtaining a CDN log file; and generating a CDN network topology diagram based on the CDN log file.

[0028] This embodiment generates a CDN network topology structure diagram based on the CDN log file, including: performing streaming analysis and calculation on the CDN log file based on CDN log file analysis technology and network topology perception technology to generate a CDN network topology structure diagram.

[0029] This embodiment adopts streaming log file analysis technology, which is conducive to obtaining real-time CDN network topology information, which is conducive to obtaining the best routing results and improving the service quality and user experience of the CDN.

[0030] Optionally, each of the above embodiments may further include the following steps: based on the training set data, using a reinforcement learning algorithm to generate or iteratively train the CDN routing selection model; wherein the training set data includes: the CDN routing request information, the CDN network topology information, and the routing results matching the CDN routing request.

[0031] Reinforcement learning is a method in the field of machine learning that learns behavioral strategies through a trial-and-error process to achieve the purpose of maximizing rewards. This embodiment uses a reinforcement learning algorithm to establish a CDN routing model, and can also use a reinforcement learning algorithm to iteratively train the CDN routing model. Through continuous trial and error and learning, the CDN routing model can automatically learn and optimize routing strategies. Specifically, CDN routing request information and CDN network topology information can be used as input to design a state and action space suitable for CDN, and then the established CDN routing model is used to find the best routing result and output the best routing result. Through continuous trial and error and learning, the CDN routing model can gradually optimize the routing strategy, thereby improving the service quality and user experience of CDN.

[0032] In the technical field, this embodiment uses reinforcement learning AI technology to achieve intelligent routing selection, thereby improving the service quality and user experience of CDN. At the same time, this embodiment can also provide more decision support and management tools for CDN operation and maintenance personnel, thereby improving the management efficiency and operation stability of CDN. In practical applications, this embodiment can help CDN providers achieve a more efficient content distribution network, improve the speed and quality of users' access to content, and thus enhance user stickiness and satisfaction.

[0033] In addition, this embodiment also has a wide range of application prospects and can be applied to Internet application fields such as live video broadcasting, online games, music, and social networking. With the continuous popularization and development of Internet services, the application prospects of CDN technology are becoming more and more broad. At the same time, with the continuous changes in network topology and service quality, the demand for intelligent routing selection is becoming more and more urgent. Therefore, the application prospects of this embodiment are very broad and have very high commercial value and social significance.

[0034] This embodiment can solve the problem of intelligent routing selection in the existing CDN technology and has obvious practical application and economic benefits.

[0035] Compared with the traditional CDN routing selection method, this embodiment has at least the following advantages:

[0036] 1. Intelligence: This embodiment uses a reinforcement learning algorithm to establish a CDN routing model, which is intelligent and can automatically learn and optimize routing strategies to adapt to the dynamic CDN network topology and the diversity of user requests.

[0037] 2. Adaptability: This embodiment is based on CDN network topology information (such as CDN network topology structure and performance indicators), and is designed to be suitable for the state and action space of CDN. It has the characteristics of adaptability and can select the best routing strategy according to different situations to improve user experience.

[0038] 3. Improve service quality and user experience: The ultimate goal of this embodiment is to improve the service quality and user experience of the CDN. Through intelligent routing selection and optimization strategies, the user experience and satisfaction can be effectively improved.

[0039] In summary, the technical solution of this embodiment has obvious practical value and economic benefits, and can solve the problem of intelligent routing selection in existing CDN technology, thereby improving the service quality and user experience of CDN. This technical solution can provide CDN providers with a more efficient content distribution network, enhance user stickiness and satisfaction, and has very broad commercial application prospects and social significance.

[0040] This embodiment can provide more ideas and solutions for the development and application of CDN technology. By using reinforcement learning AI technology to establish an intelligent CDN routing model, the service quality and user experience of CDN can be effectively improved. This solution not only has high technical content and innovation, but also has broad application prospects and commercial value in practical applications.

[0041] To explain in detail the CDN routing method provided in the embodiment of the present application, the following will be described in combination with a specific application scenario and a specific embodiment.

[0042] The application environment of the embodiment of the present application is mainly Internet service providers, especially enterprises that provide CDN services. Due to the continuous popularization and development of Internet services, the application of CDN technology is becoming more and more extensive, involving Internet application fields such as live video, online games, music and social networking. In these application scenarios, it is very important to provide fast, reliable and high-quality content distribution services, so the role of CDN technology is becoming more and more prominent.

[0043] CDN providers are the main application objects of the embodiments of this application. These companies need to provide efficient and reliable CDN services to meet users' access needs for content. In this case, the CDN user request optimal routing pathfinding method based on reinforcement learning AI technology provided by the embodiments of this application can better optimize the routing selection strategy and improve the quality and performance of CDN services.

[0044] In addition, the embodiments of the present application are also applicable to the internal networks of Internet service providers, especially for enterprises with complex and dynamic network topologies. The routing selection method provided by the embodiments of the present application can effectively solve the routing selection problem in the internal network and improve network performance and operation stability.

[0045] In summary, the application environment of the embodiments of the present application is mainly Internet service providers, especially CDN providers and enterprises with complex and dynamic network topologies. These enterprises need to provide efficient and reliable CDN services and network services to meet the needs of users and internal employees. The CDN routing method based on reinforcement learning AI technology provided in the embodiments of the present application can effectively improve the quality and performance of CDN services and network services, and has broad practical application value and commercial significance.

[0046] The system block diagram of the embodiment of the present application is as follows Figure 2 As shown in the figure, it consists of four layers, namely hardware layer, data layer, application layer, and main control layer. Next, we will explain the functions of these four layers and the functions of the modules within the layers one by one.

[0047] Hardware Layer

[0048] The hardware layer mainly consists of existing network element devices and data probes in the CDN network, which serve as the underlying hardware resource devices for mobilization.

[0049] CDN network element device (abbreviated as network element device): network element concepts such as sub-servers and sub-virtual groups. It is mainly used as the source device of the calculated indicators (including physical devices and virtual devices).

[0050] Data probe: This module is connected to the network element devices at the hardware layer and the data collection interface and data delivery interface at the data layer. The data probes distributed in the sub-servers and sub-virtual groups are used to collect the indicator data generated in the network element devices that need to be used for calculation. After receiving the data collection task sent by the data delivery interface, this module collects the corresponding indicator data in the network element devices and uploads it to the data collection interface to complete the data collection operation.

[0051] Data Layer

[0052] The data layer includes data collection interface, data delivery interface, data task management module and log file system. The main function of this layer is to transmit and verify data. At the same time, for newly added data collection tasks, register and add data collection task queues; for newly removed data collection tasks, register and remove data collection task queues; for newly modified data collection tasks, register and modify data collection task queues; query data collection tasks in the queue.

[0053] Data collection interface: This interface is connected to the data probe of the hardware layer and the data task management module of the data side. After collecting and acquiring data, the data probe uploads the data to the data collection interface. After receiving the data collected by the data probe, the interface aggregates the data into the data task management module and uniformly marks the timestamp and manages it in a unified manner, so that it can be called by various business processing units at the application layer.

[0054] Data sending interface: This interface is connected to the data probe of the hardware layer and the data task management module of the data layer. After receiving the task signal in the current data collection task queue in the data task management module, the module sends the task collection signal to the data probe to drive the data probe to collect the corresponding indicator data.

[0055] Data task management module: This module is connected to the data collection interface, data delivery interface and optimal routing reinforcement learning training module in the application layer. The main functions of this module include: 1) providing a persistent database to store the historical data of various indicators of each network element; 2) maintaining the data task queue and sending each signal in the data task queue to the corresponding module to drive the module to run, that is, for newly added data collection tasks, register and add the data collection task queue; for newly removed data collection tasks, register and remove the data collection task queue; for newly modified data collection tasks, register and modify the data collection task queue; query the data collection tasks in the queue. At the same time, for the data collected by the data collection interface, this module aggregates this type of data and marks the time uniformly for calling by each business processing unit in the application layer.

[0056] Log file system: This system is a centralized log file management system in the CDN network system, which contains log file information, alarm information, fault information, network transmission log information between each CDN network element device and the connected CDN network element device, etc. When performing optimal routing training, this embodiment uses the network transmission log information in this system and the constraints manually input through the main control module to calculate the optimal routing path, and uses the optimal routing path as part of the training set to perform optimal routing reinforcement learning algorithm training.

[0057] Application Layer

[0058] The application layer mainly consists of various business processing units, including the best routing reinforcement learning training module, the best routing output module and the task execution module. This layer is the main business layer of this system, which includes most of the functions of this system. This layer will be explained in detail below.

[0059] Optimal routing reinforcement learning training module: This module obtains the network transmission log information between CDN network element devices in the log file system: 1) The network transmission log information between CDN network element devices - obtains the status of the network path between CDN network element devices, and obtains the CDN network element service operation status information based on the CDN network element operation status log; 2) Network element indicator information - obtains CDN network element operation parameters from the data acquisition interface of the CDN network, and obtains the CDN network element hardware indicator information based on the CDN network element operation parameters to avoid routing to abnormal network elements; 3) Restrictions input by the main control module; 4) Obtain the CDN network link status information based on the network link status log of the CDN network. The best routing path is calculated by the traversal algorithm, and the best routing path is used as part of the training set to train the reinforcement learning model, and finally obtain (in the absence of an initial model) or iterate (in the case of an initial model that already exists) the CDN routing selection model.

[0060] Optimal route output module: This module mainly uses the information in the log file: 1) Network transmission log information between CDN network element devices - obtain the status of the network path between CDN network element devices, and obtain the CDN network element service operation information based on the CDN network element operation log; 2) Network element indicator information - obtain CDN network element operation parameters from the CDN network data acquisition interface, and obtain the CDN network element hardware indicator information based on the CDN network element operation parameters to avoid routing to abnormal network elements; 3) Restrictions input by the main control module; 4) Obtain the CDN network link status information based on the CDN network network link status log. Input into the CDN routing selection model to obtain the optimal routing path.

[0061] Task execution module: This module is mainly controlled by the main control module. There are two types of tasks that can be executed: 1) training and generating CDN routing selection model; 2) iterative training of CDN routing selection model. For specific examples, see the implementation examples in the next chapter.

[0062] Main control layer

[0063] This layer is mainly used to control system training or iteration of CDN routing selection model, as well as input artificial restriction conditions. In some cases, customers may not choose the best routing path when considering business needs, but choose a routing method that meets their business needs. This type of information is input into the main control module, thereby changing the best routing path output by the best routing reinforcement learning model. This will be explained in conjunction with a specific embodiment below.

[0064] Example 1

[0065] This embodiment is a training process embodiment, which includes the following steps:

[0066] The main control module sends training instructions and also sends user-specific file information (such as CDN network element restrictions, content restrictions, etc.).

[0067] The task execution module receives the training instructions and special requirement file information, adds the training task to the task queue, and waits for the task queue to start executing after the training task is reached.

[0068] The optimal routing reinforcement learning module obtains the corresponding CDN network element operation log (whether the business operation status is good) and CDN network link status log from the log file system; obtains the operation parameters of each CDN network element from the data acquisition interface to calculate the hardware indicator information of each CDN network element (whether the hardware operation status is normal).

[0069] The best route reinforcement learning module calculates the best route link through the above data and combines it with the user's special requirements file information to form a training set. The training set is: [CDN network element business operation status, CDN network link status, CDN network element hardware indicators, user special requirements; and the best route result].

[0070] The optimal routing reinforcement learning module is trained based on the above training set through a reinforcement learning algorithm to obtain a CDN routing selection model.

[0071] Example 2

[0072] This embodiment is an iterative process embodiment, including the following steps:

[0073] The main control module sends out iteration instructions and also sends out user-specific requirement file information (network element restrictions, content restrictions, etc.).

[0074] The task execution module receives iteration instructions and special requirement file information, adds the training task to the task queue, and waits for the task queue to start executing after the training task is reached.

[0075] The optimal routing reinforcement learning module obtains the corresponding CDN network element operation log (whether the business operation status is good) and CDN network link status log from the log file system; obtains the operation parameters of each CDN network element from the data acquisition interface to calculate the hardware indicator information of each CDN network element (whether the hardware operation status is normal).

[0076] The best route reinforcement learning module calculates the best route link through the above data and combines it with the user's special requirements file information to form a training set. The training set is: [CDN network element business operation status, CDN network link status, CDN network element hardware indicators, user special requirements; and the best route result].

[0077] The optimal routing reinforcement learning module iteratively trains the CDN routing selection model based on the above training set through a reinforcement learning algorithm, and iteratively obtains the optimal CDN routing selection model.

[0078] Example 3

[0079] This embodiment is an optimal routing application embodiment, which includes the following steps:

[0080] The main control module sends a path-finding request, the request content includes the user's IP address, the MD5 data of the request content, and the user's special request file information.

[0081] The task execution module receives the pathfinding request and adds the task to the queue.

[0082] The optimal route output module calls the log file system to obtain the corresponding CDN network element operation log (whether the business operation status is good) and CDN network link status log; obtains the operation parameters of each CDN network element from the data collection interface to calculate the hardware indicator information of each CDN network element (whether the hardware operation status is normal), and constitutes [CDN network element business operation status, CDN network link status, CDN network element hardware indicators, user special requirements] data, and obtains the optimal routing path through the CDN routing selection model calculation.

[0083] The main control module passes the optimal routing path to the CDN control network element, and by redirecting user services, the optimal routing path of the CDN is achieved, so that users can obtain the best and fastest content services.

[0084] The embodiment of the present application is based on the CDN user request optimal routing pathfinding method of reinforcement learning AI technology, which solves the problems of intelligent routing selection in the existing CDN technology, including the complexity and dynamics of the network topology, the diversity of user requests, node failures and network congestion. The innovation points and improved features are mainly in the following aspects:

[0085] 1. Application of reinforcement learning algorithm: The reinforcement learning algorithm is used to establish a CDN routing selection model, which is intelligent and adaptive. Through continuous trial and error and learning, the CDN routing selection model can gradually optimize the routing selection strategy and improve the CDN service quality and user experience.

[0086] 2. Design based on CDN network topology information (such as CDN network topology structure and performance indicators): Taking CDN network topology information as input, design a state and action space suitable for CDN, which has the characteristics of adaptability and can select the best routing strategy according to different situations to improve user experience.

[0087] 3. Improve service quality and user experience: The ultimate goal is to improve the service quality and user experience of CDN. Through intelligent routing selection and optimization strategies, user experience and satisfaction can be effectively improved.

[0088] Compared with the traditional CDN routing selection method, this embodiment has at least the following advantages:

[0089] 1. Intelligence: This embodiment uses a reinforcement learning algorithm to establish a CDN routing model, which is intelligent and can automatically learn and optimize routing strategies to adapt to the dynamic CDN network topology and the diversity of user requests.

[0090] 2. Adaptability: This embodiment is based on CDN network topology information (such as CDN network topology structure and performance indicators), and is designed to be suitable for the state and action space of CDN. It has the characteristics of adaptability and can select the best routing strategy according to different situations to improve user experience.

[0091] 3. Improve service quality and user experience: The ultimate goal of this embodiment is to improve the service quality and user experience of the CDN. Through intelligent routing selection and optimization strategies, the user experience and satisfaction can be effectively improved.

[0092] In summary, the technical solution of this embodiment has obvious practical value and economic benefits, and can solve the problem of intelligent routing selection in existing CDN technology, thereby improving the service quality and user experience of CDN. This technical solution can provide CDN providers with a more efficient content distribution network, enhance user stickiness and satisfaction, and has very broad commercial application prospects and social significance.

[0093] This embodiment can provide more ideas and solutions for the development and application of CDN technology. By using reinforcement learning AI technology to establish an intelligent CDN routing model, the service quality and user experience of CDN can be effectively improved. This solution not only has high technical content and innovation, but also has broad application prospects and commercial value in practical applications.

[0094] Figure 3 A schematic diagram of the structure of a CDN routing pathfinding device provided in an embodiment of the present application is shown, and the device 300 includes the following modules.

[0095] The acquisition module 302 is used to acquire CDN routing request information, where the CDN routing request information is used to request a routing result.

[0096] The output module 304 is used to obtain a routing result matching the CDN routing request based on the CDN routing request information, the CDN network topology information and the CDN routing selection model; wherein the CDN routing selection model is used to output the routing result.

[0097] In the embodiment of the present application, routing results are obtained based on CDN network topology information and a CDN routing selection model. The CDN routing selection model can automatically learn and optimize routing selection strategies. Through continuous trial and error and learning, the routing selection strategies can be gradually optimized, thereby improving the service quality and user experience of the CDN. Routing results are obtained based on real-time CDN network topology information, which is conducive to obtaining the optimal routing results and improving the service quality and user experience of the CDN.

[0098] Optionally, as an embodiment, the device also includes a model training module, which is used to generate or iteratively train the CDN routing selection model based on training set data using a reinforcement learning algorithm; wherein the training set data includes: the CDN routing request information, the CDN network topology information, and the routing results matching the CDN routing request.

[0099] Optionally, as an embodiment, the CDN routing request information includes at least one of the following: user IP address, MD5 data of user requested content, user special requirement file information; or the CDN network topology information includes at least one of the following: CDN network element service operation status information, CDN network link status information, CDN network element hardware indicator information.

[0100] Optionally, as an embodiment, the acquisition module 302 is also used for at least one of the following: 1) obtaining the CDN network element operation status log from the log file system of the CDN network, and obtaining the CDN network element business operation status information based on the CDN network element operation status log; 2) obtaining the CDN network link status information based on the network link status log of the CDN network; 3) obtaining the CDN network element operation parameters from the data acquisition interface of the CDN network, and obtaining the CDN network element hardware indicator information based on the CDN network element operation parameters.

[0101] Optionally, as an embodiment, the routing result that matches the CDN routing request includes one of the following: 1) the best routing result that matches the user IP address and the MD5 data of the user requested content; 2) the routing result that matches the user's special required file information.

[0102] Optionally, as an embodiment, the device further includes a transmission module, which is used to transmit the routing result to the CDN control network element, so that the CDN control network element realizes the routing path arrival of the CDN by redirecting the user service.

[0103] Optionally, as an embodiment, the acquisition module 302 is further used to acquire a CDN log file; and generate a CDN network topology diagram based on the CDN log file.

[0104] Optionally, as an embodiment, the acquisition module 302 is specifically used to perform streaming analysis and calculation on the CDN log file based on CDN log file analysis technology and network topology awareness technology to generate a CDN network topology structure diagram.

[0105] The device 300 provided in the embodiment of the present application can execute the various methods described in the foregoing method embodiments, and realize the functions and beneficial effects of the various methods described in the foregoing method embodiments, which will not be repeated here.

[0106] Figure 4 A schematic diagram of the hardware structure of an electronic device that implements the embodiment of the present application is shown. Referring to the figure, at the hardware level, the electronic device includes a processor, and optionally, an internal bus, a network interface, and a memory. Among them, the memory may include a memory, such as a high-speed random access memory (Random-Access Memory, RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage, etc. Of course, the electronic device may also include hardware required for other services.

[0107] The processor, the network interface and the memory may be interconnected through an internal bus, which may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one bidirectional arrow is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0108] The memory is used to store the program. Specifically, the program may include a program code, and the program code includes a computer operation instruction. The memory may include a memory and a non-volatile memory, and provides instructions and data to the processor.

[0109] The processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it, forming a device for locating the target user at the logical level. The processor executes the program stored in the memory and is specifically used to execute: Figure 1-2 The method disclosed in the illustrated embodiment realizes the functions and beneficial effects of each method described in the foregoing method embodiments, which will not be described in detail here.

[0110] The above application Figure 1-2 The method disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor or an instruction in the form of software. The above processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The methods, steps and logic block diagrams disclosed in the embodiments of the present application can be implemented or executed. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc. The steps of the method disclosed in the embodiments of the present application can be directly embodied as a hardware decoding processor for execution, or a combination of hardware and software modules in the decoding processor for execution. The software module can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory, and the processor reads the information in the memory and completes the steps of the above method in combination with its hardware.

[0111] The electronic device can also execute the methods described in the foregoing method embodiments, and realize the functions and beneficial effects of the methods described in the foregoing method embodiments, which will not be repeated here.

[0112] Of course, in addition to software implementation methods, the electronic device of the present application does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc. That is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0113] The embodiment of the present application also provides a computer-readable storage medium, wherein the computer-readable medium stores one or more programs, and when the one or more programs are executed by an electronic device including multiple application programs, the electronic device executes Figure 1-2 The method disclosed in the illustrated embodiment realizes the functions and beneficial effects of each method described in the foregoing method embodiments, which will not be described in detail here.

[0114] The computer-readable storage medium includes a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0115] Furthermore, an embodiment of the present application also provides a computer program product, the computer program product comprising a computer program stored on a non-transitory computer-readable storage medium, the computer program comprising program instructions, and when the program instructions are executed by a computer, the following process is implemented: Figure 1-2 The method disclosed in the illustrated embodiment realizes the functions and beneficial effects of each method described in the foregoing method embodiments, which will not be described in detail here.

[0116] In short, the above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

[0117] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer may be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0118] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0119] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0120] Each embodiment in this specification is described in a progressive manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

Claims

1. A CDN routing method, comprising: Obtain CDN routing request information, where the CDN routing request information is used to request a routing result; Based on the CDN routing request information, CDN network topology information and a CDN routing selection model, a routing result matching the CDN routing request is obtained; wherein the CDN routing selection model is used to output the routing result.

2. The method according to claim 1, wherein: The method further comprises: Based on the training set data, a reinforcement learning algorithm is used to generate or iteratively train the CDN routing selection model; The training set data includes: the CDN routing request information, the CDN network topology information, and the routing result matching the CDN routing request.

3. The method according to claim 1 or 2, wherein: The CDN routing request information includes at least one of the following: user IP address, MD5 data of user requested content, user special request file information; or The CDN network topology information includes at least one of the following: CDN network element service operation status information, CDN network link status information, and CDN network element hardware indicator information.

4. The method according to claim 3, wherein: The method further comprises at least one of the following: Obtaining a CDN network element operation status log from a log file system of the CDN network, and obtaining the CDN network element service operation status information based on the CDN network element operation status log; Obtaining the CDN network link status information based on the network link status log of the CDN network; Obtain CDN network element operation parameters from a data collection interface of the CDN network, and obtain the CDN network element hardware indicator information based on the CDN network element operation parameters.

5. The method according to claim 3, wherein: The routing result matching the CDN routing request includes one of the following: The best routing result that matches the user IP address and the MD5 data of the user request content; The routing result that matches the user's special requirement file information.

6. The method according to any one of claims 1 to 5, wherein: After obtaining the routing result matching the CDN routing request, the method further includes: The routing result is passed to the CDN control network element, so that the CDN control network element realizes the routing path arrival of the CDN by redirecting the user service.

7. The method according to claim 1, wherein: The CDN network topology information includes a CDN network topology structure diagram, and the method further includes: Get CDN log files; A CDN network topology diagram is generated based on the CDN log file.

8. The method according to claim 7, wherein: Generating a CDN network topology diagram based on the CDN log file includes: Based on CDN log file analysis technology and network topology perception technology, streaming analysis and calculation are performed on the CDN log file to generate a CDN network topology structure diagram.

9. A CDN routing device, comprising: An acquisition module, used to acquire CDN pathfinding request information, wherein the CDN pathfinding request information is used to request a routing result; An output module is used to obtain a routing result matching the CDN routing request based on the CDN routing request information, the CDN network topology information and the CDN routing selection model; wherein the CDN routing selection model is used to output the routing result.

10. An electronic device comprising: processor; as well as A memory arranged to store computer executable instructions which, when executed, cause the processor to perform the steps of the method of any one of claims 1 to 8.

11. A computer-readable medium storing one or more programs, which, when executed by an electronic device including a plurality of application programs, causes the electronic device to execute the steps of the method according to any one of claims 1 to 8.

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