Method and apparatus for scheduling target CDN network element, and electronic device

Abstract

Provided in the embodiments of the present application are a method and apparatus for scheduling a target CDN network element, and an electronic device. The method comprises: detecting whether there is an abnormal faulty CDN network element in a CDN; and when the abnormal faulty CDN network element is detected, on the basis of a CDN network element scheduling model, scheduling a target CDN network element from the CDN, so as to replace the abnormal faulty CDN network element, wherein the CDN network element scheduling model is used for scheduling the target CDN network element from the CDN.

Description

Method, device and electronic device for scheduling target CDN network element

[0001] Cross-references

[0002] This application claims priority to a Chinese patent application filed with the Patent Office of China on June 26, 2023, with application number 2023107607090 and invention name “Method, device and electronic device for scheduling target CDN network elements”. The entire contents of the application are incorporated by reference into this application. Technical Field

[0003] 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

[0004] In the modern internet era, CDNs have become critical infrastructure to meet ever-increasing network traffic demands and enhance user experience. CDNs use distributed edge servers to cache content in locations closest to users, reducing latency and improving access speeds. However, as CDN networks grow in size and complexity, selecting the optimal target CDN element to ensure quality of service in the event of an unexpected CDN element failure becomes a major challenge.

[0005] Summary of the Invention

[0006] 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 of being unable to select a better target CDN network element to ensure user service quality during the scheduling process of an abnormal CDN network element.

[0007] To solve the above technical problems, the embodiments of the present application are implemented through the following aspects.

[0008] 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; when an abnormal faulty CDN network element is detected, 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.

[0009] In a second aspect, an embodiment of the present application provides a device for scheduling a target CDN network element, including: a detection module for detecting whether there is an abnormal faulty CDN network element in the CDN network; a scheduling module for 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 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.

[0010] In a third aspect, an embodiment of the present application provides an electronic device comprising: a memory, a processor, and computer-executable instructions stored on the memory and executable on the processor, wherein the computer-executable instructions, when executed by the processor, implement the steps of the method described in the first aspect.

[0011] 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are only some embodiments recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0013] FIG1 is a flow chart showing a method for scheduling a target CDN network element according to an embodiment of the present application;

[0014] FIG2 shows a system block diagram of a method for scheduling a target CDN network element according to an embodiment of the present application;

[0015] FIG3 is a schematic structural diagram of an apparatus for scheduling a target CDN network element according to an embodiment of the present application;

[0016] FIG4 is a schematic diagram of the hardware structure of an electronic device that executes the method for scheduling a target CDN network element provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] 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 the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0018] Figure 1 illustrates a flow chart of a method for scheduling a target CDN network element, as provided in an embodiment of the present application. This method can be performed by an electronic device, such as a terminal device or a server device. In other words, the method can be performed by software or hardware installed on the terminal device or server device. The server device includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. As shown in Figure 1, the method may include the following steps.

[0019] S10: Detect whether there is any abnormal faulty CDN network element in the CDN network.

[0020] This embodiment may further include the following steps: receiving topology awareness parameters, wherein the topology awareness parameters include a status monitoring threshold of the CDN network and a 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: detecting whether there are abnormal faulty CDN network elements in the CDN network based on the topology awareness parameters.

[0021] S20: When an abnormal faulty CDN network element is detected, a target CDN network element is scheduled 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.

[0022] This embodiment may further include 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 fault CDN network element, CDN network topology information, and the target CDN network element.

[0023] 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 through continuous interaction with the environment. By using reinforcement learning, an intelligent agent can be established in a real-time environment. By observing and taking actions, it continuously optimizes its decision-making process to achieve more efficient CDN resource scheduling. Secondly, by utilizing advanced AI algorithms, it is possible to learn and mine potential patterns from a large amount of historical data, thereby providing strong support for real-time scheduling decisions, facilitating the selection of reasonable target CDN network elements, and thus improving the CDN service quality and user experience.

[0024] In one example, the CDN network topology information includes a CDN network topology diagram, and the method further includes: graphically displaying the CDN network topology diagram based on data visualization technology, and displaying information about the execution status 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, thereby better monitoring, managing, and maintaining the CDN network.

[0025] In one 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.

[0026] The method provided in any of the above examples further includes: obtaining a CDN log file; and generating the CDN network topology information based on the CDN log file.

[0027] 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.

[0028] 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.

[0029] CDN log file analysis technology can extract key data from CDN log files, analyze and process it to construct CDN network topology information and provide interactive operations. Network topology awareness technology is the core technology for constructing CDN network topology information. It can construct CDN network topology information based on the data in CDN log files to display the overall architecture and topology of the CDN network.

[0030] The method for scheduling a target CDN network element provided in an embodiment of the present application, when an abnormal faulty CDN network element is detected in the CDN network, schedules a target CDN network element 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 and user experience of the CDN.

[0031] The embodiments of this application specifically address the issue of scheduling CDN network elements in the face of abnormal failures in the CDN field. By introducing advanced reinforcement learning and artificial intelligence technologies, efficient and intelligent scheduling decisions are achieved to effectively cope with complex network environments and abnormal failures, thereby ensuring user service quality. By utilizing advanced reinforcement learning technology and artificial intelligence algorithms, scheduling strategies can be adjusted in real time, thus overcoming the limitations of existing technologies and improving the efficiency and effectiveness of CDN resource scheduling.

[0032] To illustrate in detail the method for scheduling a target CDN network element provided by an embodiment of the present application, the following will be described in conjunction with specific application scenarios and specific embodiments.

[0033] 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 embodiments of this application:

[0034] Large-Scale CDN Service Providers: The embodiments of this application are applicable to CDN service providers of all sizes, particularly those with extensive network infrastructure, numerous edge servers, and vast amounts of content. These service providers must process large volumes of network traffic and provide stable, high-speed access services to users worldwide. In this environment, effective resource scheduling strategies are crucial. The methods of the embodiments of this application can help them implement efficient and intelligent scheduling decisions, improve network performance, reduce latency, and ensure user service quality.

[0035] Complex and volatile network environments: The embodiments of this application are suitable for handling complex and volatile network environments, including those with constantly changing network conditions, abnormal failures, bandwidth limitations, and other issues. In these environments, traditional scheduling strategies may not meet the requirements of real-time and flexibility. However, the reinforcement learning AI technology-based approach of the embodiments of this application can adaptively adjust scheduling strategies to respond to network changes in real time, thereby ensuring user service quality.

[0036] Peak hours and emergencies: During peak network traffic periods or emergencies (such as large-scale events or breaking news), CDN service providers need to handle a large number of concurrent requests, and network load may increase rapidly. The method of the embodiments of the present application can address these challenges by intelligently scheduling resource allocation, alleviating network pressure, and ensuring user experience.

[0037] For different types of content: The embodiments of this application are applicable to processing various types of network content, including static resources (such as images, documents, etc.), dynamic resources (such as video, audio, etc.), and real-time interactive applications (such as online games, live broadcasts, etc.). For different types of content, the methods of the embodiments of this application can implement refined scheduling strategies to meet the needs of various scenarios.

[0038] Cross-region and multi-network access: In cross-region and multi-network access applications, the methods of the present invention can fully leverage the advantages of reinforcement learning AI technology to implement intelligent and adaptive scheduling strategies. By considering factors such as geographic location and network conditions, the methods of the present invention can provide users with the best edge server selection, ensuring service quality and access speed.

[0039] Integration with other infrastructure: This embodiment of the application can also be integrated with other network infrastructure (such as cloud computing and the Internet of Things) to provide an end-to-end optimization solution. By implementing intelligent and adaptive resource scheduling, this embodiment of the application helps the entire network system operate efficiently and improves overall performance.

[0040] Private and Hybrid CDN Deployment: The methods of the present invention are applicable to both private and hybrid CDN deployment scenarios. For private CDNs within an enterprise, the present invention can achieve flexible and efficient resource scheduling, reducing operating costs. For hybrid CDNs, the present invention can coordinate resource allocation across different service providers, improving overall service levels.

[0041] Real-time monitoring and reporting: This embodiment of the present invention can be integrated with existing real-time monitoring and reporting systems to provide operations and maintenance personnel with real-time network status and scheduling policy information. With this information, operations and maintenance personnel can better understand network conditions, identify and resolve problems in a timely manner, and improve operations and maintenance efficiency.

[0042] In summary, the application environments of the embodiments of this application are broad, covering CDN service providers of various sizes, complex and changing 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 this application can achieve efficient and intelligent resource scheduling, improving user service quality and experience.

[0043] The system block diagram of the embodiment of the present application is shown in Figure 2, which consists of four layers: hardware layer, data layer, application layer, and main control layer. The following will explain the functions of each layer and the functions of the modules within each layer.

[0044] 1) Main control layer: Contains the main control module, responsible for coordinating and managing the operation of the entire system.

[0045] 2) Application layer: This layer includes an optimal scheduling target reinforcement learning training module, which is used to train the reinforcement learning model (i.e., the CDN network element scheduling model); an optimal scheduling target recommendation module, which recommends the optimal target grouping (i.e., the target CDN network element) for the scheduling system based on the trained model; and a task execution module, which is responsible for actually executing the scheduling task.

[0046] 3) Data layer: Contains a data acquisition interface for collecting data such as network status and fault information; a data delivery interface for delivering scheduling tasks to actual devices; a data task management module for managing data storage and access; and a log file system for recording system operation logs.

[0047] 4) Hardware layer: includes CDN network element devices, such as edge servers and central nodes; data probes, which are used to monitor network status and device operation in real time.

[0048] 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 policies, monitoring network conditions, and communicating with other modules. Through the main control module, operations and maintenance personnel can obtain real-time system status, adjust scheduling policies, and monitor task execution.

[0049] 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 collected data such as network status and fault information to provide optimal target grouping recommendations for the scheduling system. By continuously learning and updating the model, this module enables intelligent scheduling in an ever-changing network environment.

[0050] Optimal Scheduling Target Recommendation Module: This module is connected to the Optimal Scheduling Target Reinforcement Learning Training Module and the Task Execution Module. Based on the trained reinforcement learning model, it recommends optimal target groupings for the scheduling system to optimize the allocation of network resources. By recommending optimal target groupings, this module helps improve user service quality and access speed.

[0051] Task Execution Module: This module is connected to the optimal scheduling target recommendation module and the data delivery interface. It is responsible for executing the actual scheduling tasks based on the recommended optimal target group, including delivering the scheduled tasks to the actual devices and monitoring task execution. By effectively executing scheduling tasks, this module helps improve the operational efficiency of the entire CDN system.

[0052] Data Collection Interface: This interface connects to the main control module and CDN network elements. It collects 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 facilitates real-time monitoring of network status and rapid fault location.

[0053] Data Distribution Interface: This interface connects the task execution module and CDN network elements. It distributes scheduled tasks to the actual devices to optimize network resource allocation. By efficiently distributing scheduled tasks, this interface helps improve the overall efficiency of the CDN system.

[0054] Data Task Management Module: This module is connected to the Optimal Scheduling Target Recommendation Module and the Log File System. It manages data storage and access, including the storage, retrieval, and update of data such as network status and fault information. By effectively managing data, this module helps improve the system's data processing capabilities and real-time performance, thereby enhancing the operational efficiency of the entire CDN system.

[0055] Log File System: The log file system is connected to the data task management module. It records system operation logs, including task execution status, network status changes, and troubleshooting procedures. This detailed log allows operations and maintenance personnel to quickly understand system operation status, analyze and troubleshoot faults, and ensure stable CDN system operation.

[0056] CDN network elements: These devices connect to data collection interfaces, data distribution interfaces, and data probes. They include edge servers, central nodes, and other devices responsible for actual content distribution, caching, and load balancing. By optimizing resource allocation and scheduling strategies, CDN network elements can provide higher-quality services and improve user access speeds.

[0057] Data Probes: Data probes are connected to CDN network elements. They monitor network status and device operation in real time, collecting key performance indicators and fault information. By monitoring network conditions in real time, data probes provide operations and maintenance personnel with accurate and timely information, helping to quickly locate and resolve network issues and improve the stability and performance of the entire CDN system.

[0058] The following end-to-end example demonstrates how to set topology awareness parameters through the master control module so that the UI can be displayed on the master control module. The following are the detailed steps:

[0059] Step 1: Operations personnel use the main control module to set topology perception parameters, such as network status monitoring thresholds, fault detection frequency, etc. These parameters will be used to control the accuracy and real-time performance of network topology perception.

[0060] Step 2: The master control module sends a data collection request to the CDN network element device through the data collection interface based on the set parameters. The data probe collects the required network status and fault information and returns this data to the data collection interface.

[0061] 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 optimal target grouping recommendation for the scheduling system.

[0062] 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 are passed to the task execution module, which is responsible for actually executing the scheduling task.

[0063] Step 5: The task execution module sends the scheduled task to the CDN network element device through the data delivery interface. The network element device adjusts resource allocation and load balancing strategies based on the scheduled task to improve user service quality and access speed.

[0064] 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. Operations and maintenance personnel can obtain system operation status through the data task management module.

[0065] The log file system records system operation logs, including task execution status, network status changes, and fault handling processes. Operations and maintenance personnel can use the log file system to quickly understand the system operation status and conduct fault analysis and handling.

[0066] Finally, the UI is displayed on the main control module. The UI includes information such as the network topology, CDN network element status, and scheduling task execution status, allowing operation and maintenance personnel to understand the system operation status in real time and identify and resolve problems in a timely manner.

[0067] This embodiment of the application proposes a method for scheduling target CDN network elements based on reinforcement learning AI technology, which achieves innovative improvements to the shortcomings and unresolved technical problems in the existing technology. The inventive points of this embodiment of the application are mainly reflected in the following aspects:

[0068] Introducing reinforcement learning technology: Compared to existing static load balancing strategies, scheduling strategies based on monitoring data, and traditional machine learning methods, the embodiments of this application introduce reinforcement learning technology to achieve a dynamic and intelligent scheduling strategy. Reinforcement learning, a machine learning method that can learn optimal strategies through interaction with the environment, can adjust scheduling strategies in real time to cope with complex network environments and abnormal failure situations.

[0069] Integrating AI technology: By integrating advanced AI algorithms into CDN scheduling strategies, the present embodiment can learn and discover underlying patterns from extensive historical data, providing strong support for real-time scheduling decisions. The method of the present embodiment is more intelligent and adaptive when handling abnormal faults, enabling optimal decisions to be made in a short period of time, ensuring user service quality.

[0070] Adaptive Scheduling Strategy Adjustment: This embodiment leverages reinforcement learning and artificial intelligence technologies to adaptively adjust scheduling strategies to meet evolving network environments and user needs. This innovation transcends the limitations of static, fixed scheduling strategies in existing technologies, bringing greater flexibility and efficiency to CDN resource scheduling.

[0071] Optimal Scheduling Target Group Recommendation: This embodiment addresses the issue of scheduling abnormal CDN network elements and proposes a method for selecting optimal scheduling target CDN network elements. By leveraging reinforcement learning and artificial intelligence technologies, this embodiment can recommend optimal target groups to the scheduling system in real time, enabling more efficient and intelligent scheduling decisions and ensuring user service quality.

[0072] In summary, the method for scheduling target CDN network elements proposed in the embodiments of the present application achieves innovative improvements based on existing technologies and has significant technical effects. The embodiments of the present application fully leverage the advantages of reinforcement learning and artificial intelligence technologies to improve the intelligence level of CDN resource scheduling and enhance the response speed and accuracy of the scheduling system. Furthermore, the method of the embodiments of the present application is highly adaptable and can effectively cope with complex and changing network environments and abnormal fault conditions, ensuring that user service quality is guaranteed.

[0073] In practical applications, the embodiments of this application can be widely applied to CDN service providers of various sizes, helping them achieve more efficient and stable resource scheduling, improving service quality and user experience. Furthermore, the methods of the embodiments of this application are also highly versatile and scalable, and can be applied to resource scheduling problems in other fields, such as cloud computing and the Internet of Things.

[0074] In summary, the method for scheduling target CDN network elements in the embodiment of the present application has significant technical effects. By implementing a dynamic and intelligent scheduling strategy, it brings important technological progress to the field of CDN resource scheduling and has broad application prospects and practical value.

[0075] FIG3 shows a schematic structural diagram of an apparatus for scheduling a target CDN network element according to an embodiment of the present application. The apparatus 300 includes the following modules.

[0076] The detection module 302 is used to detect whether there is any abnormal fault CDN network element in the CDN network.

[0077] 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 the 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.

[0078] In an 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 the CDN network element scheduling model to replace the abnormal faulty CDN network element, thereby ensuring user service quality and improving the service quality and user experience of the CDN.

[0079] In one embodiment, the device also includes a model training module for generating or iteratively training the CDN network element scheduling model based on training set data using a reinforcement learning algorithm; wherein the training set data includes: the abnormal fault CDN network element, CDN network topology information, and the target CDN network element.

[0080] In one embodiment, the apparatus further comprises 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 task related to the target CDN network element.

[0081] In one 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.

[0082] In one embodiment, the device further includes a generating module configured to obtain a CDN log file; and generate the CDN network topology information based on the CDN log file.

[0083] In one embodiment, the generating module is configured 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.

[0084] In one embodiment, the device also includes a receiving module for receiving topology perception parameters, wherein the topology perception parameters include the status monitoring threshold of the CDN network and the detection frequency of the abnormal faulty CDN network element; wherein the detection module 302 is used to detect whether there is an abnormal faulty CDN network element in the CDN network based on the topology perception parameters.

[0085] The device 300 provided in the embodiment of the present application can execute the various methods described in the above method embodiments and realize the functions and beneficial effects of the various methods described in the above method embodiments, which will not be repeated here.

[0086] FIG4 shows a schematic diagram of the hardware structure of an electronic device that implements an embodiment of the present application. 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. The memory may include a memory, such as a high-speed random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage device. Of course, the electronic device may also include hardware required for other services.

[0087] The processor, network interface, and memory can be interconnected via an internal bus, such as an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. These buses can be classified as address buses, data buses, and control buses. For ease of illustration, the figure uses only one bidirectional arrow, but this does not imply that there is only one bus or only one type of bus.

[0088] The memory is used to store programs. Specifically, the program may include program code, which includes computer operating instructions. The memory may include internal memory and non-volatile memory, and provides instructions and data to the processor.

[0089] The processor reads the corresponding computer program from the non-volatile memory into the internal memory and then runs it, logically forming a device for locating the target user. The processor executes the program stored in the memory and is specifically configured to perform the methods disclosed in the embodiments shown in Figures 1-2 and to achieve the functions and benefits of the various methods described in the preceding method embodiments, which will not be further described here.

[0090] The methods disclosed in the embodiments shown in Figures 1-2 of the present application can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by hardware integrated logic circuits in the processor or software instructions. 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 gate or transistor logic devices, or discrete hardware components. The various 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 any conventional processor. The steps of the methods disclosed in the embodiments of the present application can be directly embodied as being executed by a hardware decoding processor, or can be executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a storage medium well-known in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The storage medium is located in the memory, and the processor reads the information in the memory and, in conjunction with its hardware, completes the steps of the above method.

[0091] The electronic device can also execute the methods described in the above method embodiments and realize the functions and beneficial effects of the methods described in the above method embodiments, which will not be described in detail here.

[0092] Of course, in addition to software implementation, the electronic device of this 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.

[0093] An embodiment of the present application also proposes a computer-readable storage medium, which stores one or more programs. When the one or more programs are executed by an electronic device including multiple applications, the electronic device executes the method disclosed in the embodiment shown in Figures 1-2 and realizes the functions and beneficial effects of the various methods described in the previous method embodiments, which will not be repeated here.

[0094] The computer-readable storage medium includes a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0095] Furthermore, an embodiment of the present application also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium, and the computer program includes program instructions. When the program instructions are executed by a computer, the following process is implemented: the method disclosed in the embodiment shown in Figures 1-2 and the functions and beneficial effects of the various methods described in the previous method embodiments are implemented, which will not be repeated here.

[0096] In short, the above description is only a preferred embodiment of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

[0097] The systems, devices, modules, or units described in the above embodiments may be implemented by computer chips or entities, or by products having 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 smartphone, 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.

[0098] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. The 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 disc read-only memory (CD-ROM), digital versatile disc (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 transitory computer-readable media (transitory media), such as modulated data signals and carrier waves.

[0099] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

[0100] The various embodiments in this specification are described in a progressive manner. Similar parts between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the system embodiments are generally similar to the method embodiments, so the description is relatively simple. For relevant parts, refer to the description of the method embodiments.

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 CDN log file analysis technology and network topology perception technology, the CDN daily The log file is used for 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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