Method and device for scheduling target CDN (Content Delivery Network) element and electronic equipment

Abstract

The embodiment of the invention provides a method and device for scheduling a target CDN network element and electronic equipment. The method comprises the steps that whether an abnormal fault CDN network element exists or not is detected in a CDN network; under the condition that the abnormal fault CDN network element is detected, scheduling a target CDN network element from the CDN network to replace the abnormal fault CDN network element based on a CDN network element scheduling model; wherein the CDN network element scheduling model is used for scheduling the target CDN network element from the CDN network.

Description

Technical Field

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

[0002] In the modern Internet era, CDN has developed into a key infrastructure to meet the growing demand for network traffic and improve user experience. CDN caches content to the geographical location closest to the user through distributed edge servers, thereby reducing latency and increasing access speed. However, as the scale and complexity of CDN networks grow, how to select the best target CDN network element to ensure user service quality in the event of abnormal failure of CDN network elements has become a major challenge. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a method, device and electronic device for scheduling a target CDN network element, which can solve the problem that a better target CDN network element cannot be selected to ensure user service quality during the scheduling of an abnormal fault CDN network element.

[0004] To solve the above technical problems, the embodiments of the present application are implemented through the following aspects.

[0005] In a first aspect, an embodiment of the present application provides a method for scheduling a target CDN network element, including: detecting whether there is an abnormal faulty CDN network element in the CDN network; in the case of detecting an abnormal faulty CDN network element, scheduling a target CDN network element from the CDN network to replace the abnormal faulty CDN network element based on a CDN network element scheduling model; wherein the CDN network element scheduling model is used to schedule the target CDN network element from the CDN network.

[0006] In a second aspect, an embodiment of the present application provides a device for scheduling a target CDN network element, comprising: a detection module, used to detect whether there is an abnormal faulty CDN network element in the CDN network; a scheduling module, used to schedule a target CDN network element from the CDN network to replace the abnormal faulty CDN network element based on a CDN network element scheduling model when an abnormal faulty CDN network element is detected; wherein the CDN network element scheduling model is used to schedule the target CDN network element from the CDN network.

[0007] 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.

[0008] 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.

[0009] In the embodiment of the present application, when an abnormal faulty CDN network element is detected in the CDN network, a target CDN network element is scheduled from the CDN network based on a CDN network element scheduling model to replace the abnormal faulty CDN network element, thereby ensuring user service quality and improving the service quality of the CDN and user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] 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.

[0011] Figure 1 A schematic diagram showing a flow chart of a method for scheduling a target CDN network element provided in an embodiment of the present application;

[0012] Figure 2 A system block diagram showing a method for scheduling a target CDN network element provided by an embodiment of the present application;

[0013] Figure 3 A schematic diagram showing the structure of a device for scheduling a target CDN network element provided in an embodiment of the present application;

[0014] Figure 4 A schematic diagram of the hardware structure of an electronic device for executing the method for scheduling a target CDN network element provided in an embodiment of the present application. DETAILED DESCRIPTION

[0015] 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.

[0016] Figure 1A flow chart of a method for scheduling a target CDN network element 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 the terminal device or the 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.

[0017] S10: Detect whether there is any abnormal faulty CDN network element in the CDN network.

[0018] Optionally, this embodiment also includes the following steps: receiving topology perception parameters, the topology perception parameters including the status monitoring threshold of the CDN network and the detection frequency of abnormal faulty CDN network elements; wherein, in this step, detecting whether there are abnormal faulty CDN network elements in the CDN network includes: based on the topology perception parameters, detecting whether there are abnormal faulty CDN network elements in the CDN network.

[0019] S20: When an abnormal faulty CDN network element is detected, based on a CDN network element scheduling model, a target CDN network element is scheduled from the CDN network to replace the abnormal faulty CDN network element; wherein the CDN network element scheduling model is used to schedule the target CDN network element from the CDN network.

[0020] Optionally, this embodiment also includes the following steps: based on the training set data, using a reinforcement learning algorithm to generate or iteratively train the CDN network element scheduling model; wherein the training set data includes: the abnormal faulty CDN network element, CDN network topology information, and the target CDN network element.

[0021] This embodiment uses key technologies such as reinforcement learning and artificial intelligence (i.e., CDN network element scheduling model). Reinforcement learning is a machine learning method that learns the best strategy in continuous interaction with the environment. By using reinforcement learning, an intelligent agent can be established in a real-time environment to continuously optimize its decision-making process by observing and taking actions to achieve more efficient CDN resource scheduling. Secondly, by utilizing advanced AI algorithms, potential rules can be learned and mined from a large amount of historical data, thereby providing strong support for real-time scheduling decisions, which is conducive to obtaining reasonable target CDN network elements, thereby improving the service quality and user experience of the CDN.

[0022] In one example, the CDN network topology information includes a CDN network topology diagram, and the method further includes: based on data visualization technology, displaying the CDN network topology diagram in a graphical manner, and displaying the execution status information of the scheduling task related to the target CDN network element. This example can help CDN operation and maintenance personnel understand the overall architecture and topology of the CDN network, so as to better monitor, manage and maintain the CDN network.

[0023] In an example, 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.

[0024] The methods provided in the above examples further include: obtaining a CDN log file; and generating the CDN network topology information based on the CDN log file.

[0025] The generating the CDN network topology information based on the CDN log file includes: performing streaming analysis and calculation on the CDN log file based on CDN log file analysis technology and network topology perception technology to generate the CDN network topology information.

[0026] This embodiment adopts the streaming log file analysis technology, which is more conducive to obtaining real-time CDN network topology information and improving the accuracy of the obtained CDN network topology information compared to the static log file analysis technology.

[0027] CDN log file analysis technology can obtain key data from CDN log files, analyze and process them to build CDN network topology information and provide interactive operations. Network topology awareness technology is the core technology for building CDN network topology information. It can build CDN network topology information based on the data in CDN log files to show the overall architecture and topology of the CDN network.

[0028] The method for scheduling target CDN network elements provided in the embodiment of the present application, when an abnormal faulty CDN network element is detected in the CDN network, schedules the target CDN network element from the CDN network based on the CDN network element scheduling model to replace the abnormal faulty CDN network element, thereby ensuring the user service quality and improving the service quality and user experience of the CDN.

[0029] The embodiment of the present application is specifically aimed at the CDN network element scheduling problem of abnormal faults in the CDN field. By introducing advanced reinforcement learning and artificial intelligence technologies, efficient and intelligent scheduling decisions are achieved to effectively deal with complex network environments and abnormal faults, thereby ensuring user service quality. By utilizing advanced reinforcement learning technology and artificial intelligence algorithms, scheduling strategies can be adjusted in real time, thereby overcoming the limitations of existing technologies and improving the efficiency and effectiveness of CDN resource scheduling.

[0030] To explain in detail the method for scheduling a target CDN network element provided in an embodiment of the present application, the following will be described in conjunction with specific application scenarios and specific embodiments.

[0031] The application environment of this application is mainly aimed at CDN service providers and their related network infrastructure. In the modern Internet era, network traffic and user demand continue to grow. In order to meet these needs and improve user experience, CDN has become a key infrastructure. The following is a description of the application environment for implementing the embodiment of this application:

[0032] Large-scale CDN service providers: The embodiments of the present application are applicable to CDN service providers of various sizes, especially those with large network infrastructure, a large number of edge servers and massive content. These service providers need to process a large amount of network traffic and provide stable and high-speed access services to users around the world. In this environment, effective resource scheduling strategies are particularly important. The methods of the embodiments of the present application can help them achieve efficient and intelligent scheduling decisions, improve network performance, reduce latency, and ensure user service quality.

[0033] Complex and ever-changing network environment: The embodiments of the present application are suitable for handling complex and ever-changing network environments, including constantly changing network conditions, abnormal failures, bandwidth limitations, etc. In these environments, traditional scheduling strategies may not meet the requirements of real-time and flexibility, while the method based on reinforcement learning AI technology in the embodiments of the present application can adaptively adjust the scheduling strategy and respond to network changes in real time, thereby ensuring user service quality.

[0034] Peak hours and emergencies: During peak network traffic hours or emergencies (such as large events, breaking news, etc.), CDN service providers need to handle a large number of concurrent requests, and network load may increase rapidly. The method of the embodiment of the present application can cope with these challenges by intelligently scheduling resource allocation, alleviating network pressure, and ensuring user experience.

[0035] Different types of content: The embodiments of the present application are applicable to processing various types of network content, including static resources (such as pictures, documents, etc.), dynamic resources (such as videos, audio, etc.) and real-time interactive applications (such as online games, live broadcasts, etc.). For different types of content, the method of the embodiments of the present application can perform refined scheduling strategies to meet the needs of various scenarios.

[0036] Cross-region and multi-network access: In cross-region and multi-network access application environments, the method of the embodiment of the present application can make full use of the advantages of reinforcement learning AI technology to implement intelligent and adaptive scheduling strategies. By considering factors such as geographical location and network conditions, the method of the embodiment of the present application can provide users with the best edge server selection to ensure service quality and access speed.

[0037] Integration with other infrastructure: The embodiments of the present application can also be integrated with other network infrastructures (such as cloud computing, the Internet of Things, etc.) to provide end-to-end optimization solutions. By realizing intelligent and adaptive resource scheduling, the embodiments of the present application contribute to the efficient operation of the entire network system and improve overall performance.

[0038] Private and hybrid CDN deployment: The method of the embodiment of the present application is applicable to private CDN and hybrid CDN deployment scenarios. For private CDN within an enterprise, the embodiment of the present application can achieve flexible and efficient resource scheduling and reduce operating costs. For hybrid CDN, the embodiment of the present application can coordinate resource allocation of different service providers and improve the overall service level.

[0039] Real-time monitoring and reporting: The embodiments of the present application can be combined with existing real-time monitoring and reporting systems to provide real-time network status and scheduling strategy information to operation and maintenance personnel. With this information, operation and maintenance personnel can better understand the network status, discover and solve problems in a timely manner, and improve operation and maintenance efficiency.

[0040] In summary, the application environment of the embodiments of the present application is wide, covering CDN service providers of various sizes, complex and changeable network environments, peak hours and emergencies, different types of network content, cross-regional and multi-network access scenarios, etc. In these application environments, the CDN optimal scheduling target grouping recommendation method based on reinforcement learning AI technology in the embodiments of the present application can achieve efficient and intelligent resource scheduling and improve user service quality and experience.

[0041] 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.

[0042] 1) Main control layer: Contains the main control module, which is responsible for coordinating and managing the operation of the entire system.

[0043] 2) Application layer: It includes the optimal scheduling target reinforcement learning training module, which is used to train the reinforcement learning model (i.e., CDN network element scheduling model); the optimal scheduling target recommendation module, which recommends the optimal target grouping (i.e., target CDN network element) for the scheduling system based on the trained model; and the task execution module, which is responsible for the actual execution of the scheduling task.

[0044] 3) Data layer: Contains data collection interface, used to collect data such as network status and fault information; data delivery interface, used to deliver scheduling tasks to actual devices; data task management module, responsible for managing data storage and access; log file system, used to record system operation logs.

[0045] 4) Hardware layer: includes CDN network element equipment, such as edge servers, central nodes, etc.; data probes, which are used to monitor network status and equipment operation in real time.

[0046] Main control module: The main control module is connected to the optimal scheduling target reinforcement learning training module and the data acquisition interface. The main control module is responsible for coordinating and managing the operation of the entire system, including setting scheduling strategies, monitoring network conditions, and communicating with other modules. Through the main control module, operation and maintenance personnel can understand the system status in real time, adjust scheduling strategies, and monitor task execution.

[0047] Optimal Scheduling Target Reinforcement Learning Training Module: This module is connected to the main control module and the optimal scheduling target recommendation module. It is responsible for training the reinforcement learning model based on the collected network status, fault information and other data to provide the best target grouping recommendation for the scheduling system. By continuously learning and updating the model, this module can achieve intelligent scheduling in a constantly changing network environment.

[0048] Optimal Scheduling Target Recommendation Module: This module is connected to the Optimal Scheduling Target Reinforcement Learning Training Module and the Task Execution Module. It recommends the best target grouping for the scheduling system based on the trained reinforcement learning model to achieve optimal allocation of network resources. By recommending the best target grouping, this module helps improve user service quality and access speed.

[0049] Task execution module: The task execution module is connected to the optimal scheduling target recommendation module and the data delivery interface. It is responsible for executing the actual scheduling tasks according to the recommended optimal target group, including delivering the scheduling tasks to the actual devices and monitoring the task execution status. By effectively executing the scheduling tasks, this module helps to improve the operating efficiency of the entire CDN system.

[0050] Data collection interface: The data collection interface is connected to the main control module and CDN network element equipment. It is responsible for collecting data such as network status and fault information to provide training data for the reinforcement learning model. By collecting network data in real time, this interface helps to achieve real-time monitoring of network status and rapid fault location.

[0051] Data delivery interface: The data delivery interface is connected to the task execution module and CDN network element devices. It is responsible for delivering scheduling tasks to actual devices to achieve optimal allocation of network resources. By efficiently delivering scheduling tasks, this interface helps improve the operating efficiency of the entire CDN system.

[0052] Data task management module: This module is connected to the optimal scheduling target recommendation module and the log file system. It is responsible for managing the storage and access of data, including the storage, retrieval and update of data such as network status and fault information. By effectively managing data, this module helps to improve the system's data processing capabilities and real-time performance, thereby improving the operating efficiency of the entire CDN system.

[0053] Log file system: The log file system is connected to the data task management module. It is responsible for recording system operation logs, including task execution status, network status changes, fault handling process and other information. Through detailed log records, operation and maintenance personnel can quickly understand the system operation status, conduct fault analysis and processing, and ensure the stable operation of the CDN system.

[0054] CDN network element equipment: CDN network element equipment is connected to the data collection interface, data delivery interface and data probe. It includes edge servers, central nodes and other equipment, which are responsible for actual content distribution, caching and load balancing. By optimizing resource allocation and scheduling strategies, CDN network element equipment can provide higher quality services and improve user access speed.

[0055] Data probe: Data probe is connected to CDN network element equipment. It is responsible for real-time monitoring of network status and equipment operation, and collecting key performance indicators and fault information. By real-time monitoring of network status, data probe can provide accurate and timely information to operation and maintenance personnel, which helps to quickly locate and solve network problems and improve the stability and performance of the entire CDN system.

[0056] The following end-to-end example will show how to set topology awareness parameters through the main control module so that the UI can be presented on the main control module. The following are the detailed steps:

[0057] Step 1: The operation and maintenance personnel set the topology perception parameters through the main control module, such as network status monitoring threshold, fault detection frequency, etc. These parameters will be used to control the accuracy and real-time performance of network topology perception.

[0058] Step 2: The main control module sends a data collection request to the CDN network element device through the data collection interface according to the set parameters. The data probe collects the required network status and fault information and returns this data to the data collection interface.

[0059] Step 3: The data collection interface passes the collected data to the optimal scheduling target reinforcement learning training module. This module uses this data to train and update the reinforcement learning model to provide the best target grouping recommendation for the scheduling system.

[0060] Step 4: The optimal scheduling target recommendation module recommends the optimal target grouping for the scheduling system based on the trained reinforcement learning model. The recommendation results will be passed to the task execution module, which is responsible for actually executing the scheduling task.

[0061] Step 5: The task execution module sends the scheduling task to the CDN network element device through the data sending interface. The network element device adjusts the resource allocation and load balancing strategy according to the scheduling task to improve the user service quality and access speed.

[0062] The data task management module is responsible for storing, retrieving and updating information such as task execution status, network status changes, and fault handling processes. Operation and maintenance personnel can obtain system operation status through the data task management module.

[0063] The log file system records the system operation log, including task execution status, network status changes, fault handling process, etc. Operation and maintenance personnel can quickly understand the system operation status through the log file system and conduct fault analysis and processing.

[0064] Finally, the UI is presented on the main control module. The UI includes information such as network topology, CDN network element status, and scheduling task execution status, so that operation and maintenance personnel can understand the system operation status in real time and discover and handle problems in a timely manner.

[0065] The embodiment of the present application proposes a method for scheduling target CDN network elements based on reinforcement learning AI technology, which achieves innovative improvements in view of the deficiencies in the prior art and unresolved technical problems. The inventive points of the embodiment of the present application are mainly reflected in the following aspects:

[0066] Introducing reinforcement learning technology: Compared with the existing static load balancing strategy, the scheduling strategy based on monitoring data and the traditional machine learning method, the embodiment of the present application introduces reinforcement learning technology to achieve a dynamic and intelligent scheduling strategy. Reinforcement learning is a machine learning method that can learn the best strategy in the process of interacting with the environment. It can adjust the scheduling strategy in real time to cope with complex network environments and abnormal fault conditions.

[0067] Combining artificial intelligence technology: By integrating advanced artificial intelligence algorithms into CDN scheduling strategies, the embodiments of the present application can learn and mine potential rules from a large amount of historical data, thereby providing strong support for real-time scheduling decisions. The method of the embodiments of the present application has stronger intelligence and adaptability when dealing with abnormal fault conditions, and can make the best decision in a short time to ensure user service quality.

[0068] Adaptive adjustment of scheduling strategy: The embodiment of the present application uses reinforcement learning and artificial intelligence technology to realize adaptive adjustment of scheduling strategy to meet the ever-changing network environment and user needs. This invention breaks through the limitations of static and fixed scheduling strategies in the prior art and brings higher flexibility and efficiency to CDN resource scheduling.

[0069] Recommendation of optimal scheduling target grouping: This embodiment of the application proposes a method for selecting the optimal scheduling target CDN network element in response to the problem of scheduling abnormal fault CDN network elements. Through reinforcement learning and artificial intelligence technology, this embodiment of the application can recommend the best target grouping for the scheduling system in real time, thereby achieving more efficient and intelligent scheduling decisions and ensuring user service quality.

[0070] In summary, the method for scheduling target CDN network elements proposed in the embodiment of the present application has achieved innovative improvements on the basis of the existing technology and has obvious technical effects. The embodiment of the present application fully utilizes the advantages of reinforcement learning and artificial intelligence technology to improve the intelligence level of CDN resource scheduling and improve the response speed and accuracy of the scheduling system. In addition, the method of the embodiment of the present application is also highly adaptable and can effectively cope with complex and changeable network environments and abnormal fault conditions to ensure that the quality of user service is guaranteed.

[0071] In practical applications, the embodiments of the present application can be widely used in CDN service providers of various sizes to help them achieve more efficient and stable resource scheduling and improve service quality and user experience. At the same time, the method of the embodiments of the present application also has good versatility and scalability, and can be applied to resource scheduling problems in other fields, such as cloud computing, the Internet of Things, etc.

[0072] In summary, the method for scheduling target CDN network elements in the embodiment of the present application has obvious technical effects. By implementing dynamic and intelligent scheduling strategies, it brings important technical progress to the field of CDN resource scheduling and has broad application prospects and practical value.

[0073] Figure 3 A schematic diagram of the structure of a device for scheduling a target CDN network element provided in an embodiment of the present application is shown. The device 300 includes the following modules.

[0074] The detection module 302 is used to detect whether there is any abnormal fault CDN network element in the CDN network.

[0075] The scheduling module 304 is used to schedule a target CDN network element from the CDN network to replace the abnormally faulty CDN network element based on a CDN network element scheduling model when an abnormally faulty CDN network element is detected; wherein the CDN network element scheduling model is used to schedule the target CDN network element from the CDN network.

[0076] In an embodiment of the present application, when an abnormally faulty CDN network element is detected in the CDN network, a target CDN network element is scheduled from the CDN network based on a CDN network element scheduling model to replace the abnormally faulty CDN network element, thereby ensuring user service quality and improving the service quality and user experience of the CDN.

[0077] Optionally, as an embodiment, the device also includes a model training module, which is used to generate or iteratively train the CDN network element scheduling model based on the training set data using a reinforcement learning algorithm; wherein the training set data includes: the abnormal faulty CDN network element, CDN network topology information, and the target CDN network element.

[0078] Optionally, as an embodiment, the device further includes a display module for displaying the CDN network topology diagram in a graphical manner based on the data visualization technology, and displaying the execution status information of the scheduling tasks related to the target CDN network element.

[0079] Optionally, as an embodiment, 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.

[0080] Optionally, as an embodiment, the device further includes a generation module, which is used to obtain a CDN log file; and generate the CDN network topology information based on the CDN log file.

[0081] Optionally, as an embodiment, the generating module is 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 the CDN network topology information.

[0082] Optionally, as an embodiment, the device also includes a receiving module for receiving topology perception parameters, wherein the topology perception parameters include a status monitoring threshold of the CDN network and a detection frequency of the abnormal faulty CDN network element; wherein the detection module 302 is used to detect whether there are abnormal faulty CDN network elements in the CDN network based on the topology perception parameters.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] The above application Figure 1-2The 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] The computer-readable storage medium includes a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 method for scheduling a target CDN network element, comprising: Detect whether there are abnormal faulty CDN network elements in the CDN network; In the case of detecting an abnormal faulty CDN network element, scheduling a target CDN network element from the CDN network to replace the abnormal faulty CDN network element based on a CDN network element scheduling model; The CDN network element scheduling model is used to schedule the target CDN network element from the CDN network.

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 network element scheduling model; The training set data includes: the abnormal faulty CDN network element, CDN network topology information, and the target CDN network element.

3. The method according to claim 2, wherein: The CDN network topology information includes a CDN network topology structure diagram, and the method further includes: Based on data visualization technology, the CDN network topology diagram is displayed in a graphical manner, and the execution status information of the scheduling tasks related to the target CDN network element is displayed.

4. The method according to claim 2, wherein: 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.

5. The method according to claim 4, wherein: The method further comprises: Get CDN log files; The CDN network topology information is generated based on the CDN log file.

6. The method according to claim 5, wherein: The generating the CDN network topology information based on the CDN log file includes: Based on the CDN log file analysis technology and the network topology perception technology, the CDN log file is subjected to streaming analysis and calculation to generate the CDN network topology information.

7. The method according to claim 1, wherein: The method further comprises: Receiving topology awareness parameters, wherein the topology awareness parameters include a status monitoring threshold of the CDN network and a detection frequency of the abnormal fault CDN network element; The detecting whether there is an abnormal fault CDN network element in the CDN network includes: based on the topology perception parameter, detecting whether there is an abnormal fault CDN network element in the CDN network.

8. A device for scheduling a target CDN network element, comprising: A detection module is used to detect whether there are abnormal fault CDN network elements in the CDN network; A scheduling module, configured to schedule a target CDN network element from the CDN network to replace the abnormally faulty CDN network element based on a CDN network element scheduling model when an abnormally faulty CDN network element is detected; The CDN network element scheduling model is used to schedule the target CDN network element from the CDN network.

9. 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 7.

10. 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 7.

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