Intelligent traffic scheduling strategy optimization method and device, electronic equipment and medium

Abstract

The invention relates to the technical field of computer networks and communication, in particular to an intelligent traffic scheduling strategy optimization method and device, electronic equipment and a medium, and the method comprises the steps: determining a target traffic scheduling strategy of target equipment according to an actual network state, an actual equipment type and a current traffic demand of the target equipment, based on the target priority strategy node, performing node processing on the target traffic scheduling request to obtain a processed traffic request; and updating a target traffic scheduling strategy by using the processed traffic request, and generating a target traffic scheduling management instance based on the new traffic scheduling strategy by using the actual network state, the actual equipment type and the current traffic demand so as to perform intelligent traffic scheduling on the target equipment. Therefore, the problems that in the related technology, flow scheduling mainly depends on static rules and algorithms, self-adaptive adjustment cannot be conducted according to the real-time network condition, flow distribution is uneven, and the flexibility and expandability of flow scheduling are reduced are solved.

Description

Technical Field

[0001] The present application relates to the field of computer networks and communication technologies, and particularly to an intelligent traffic scheduling strategy optimization method, device, electronic device and medium. Background Art

[0002] With the rapid development of application scenarios such as the Internet of Things, the Internet of Vehicles, and smart homes, the communication traffic between devices and platforms has been increasing continuously. Traditional traffic scheduling methods cannot meet the requirements of high concurrency and high availability. Especially in multiple protocol and dynamic network environments, the selection of traffic scheduling strategies has become the key to improving system efficiency, reducing network congestion, optimizing bandwidth usage, and reducing latency.

[0003] In related technologies, traffic scheduling methods usually rely on a single protocol or device type, and traffic scheduling mainly depends on static rules and algorithms, that is, traffic scheduling mostly depends on static strategies. Secondly, traffic scheduling instructions are usually generated based on access traffic and preset policy routing, and then the traffic scheduling instructions are sent to the target node device for traffic scheduling.

[0004] However, the traffic scheduling in related technologies mainly depends on static rules and algorithms, which cannot meet the traffic allocation under multiple traffic types, device requirements, and network states, resulting in uneven traffic allocation and inability to adaptively adjust according to the real-time network conditions, reducing the flexibility and scalability of traffic scheduling, and urgent solutions are needed. Summary of the Invention

[0005] The present application provides an intelligent traffic scheduling strategy optimization method, device, electronic device and medium to solve the problems that the traffic scheduling in related technologies mainly depends on static rules and algorithms, cannot meet the traffic allocation under multiple traffic types, device requirements, and network states, resulting in uneven traffic allocation, inability to adaptively adjust according to the real-time network conditions, and reducing the flexibility and scalability of traffic scheduling.

[0006] The first aspect of the present application provides an intelligent traffic scheduling strategy optimization method, including the following steps: Based on the target traffic scheduling request of the target device, obtain the actual network status, actual device type, and current traffic demand of the target device; Determine the target traffic scheduling strategy of the target device according to the actual network status, the actual device type, and the current traffic demand, and perform node processing on the target traffic scheduling request based on the target priority policy node to obtain the processed traffic request; Use the processed traffic request to update the target traffic scheduling strategy to determine a new traffic scheduling strategy, and based on the new traffic scheduling strategy, use the actual network status, the actual device type, and the current traffic demand to generate a target traffic scheduling management instance to perform intelligent traffic scheduling on the target device according to the target traffic scheduling management instance.

[0007] Optionally, in an embodiment of the present application, the determining the target traffic scheduling strategy of the target device according to the actual network status, the actual device type, and the current traffic demand includes: Extract the bandwidth, delay, and load in the actual network status; Match the bandwidth priority policy, delay optimization policy, or load balancing policy in the target traffic scheduling strategy according to the bandwidth, the delay, the load, the actual device type, and the current traffic demand.

[0008] Optionally, in an embodiment of the present application, the performing node processing on the target traffic scheduling request based on the target priority policy node includes: When the bandwidth in the actual network status meets the first preset condition, perform bandwidth priority scheduling processing on the target traffic scheduling request using the bandwidth priority node in the target priority policy node; When the delay in the actual network status meets the second preset condition, perform delay optimization scheduling processing on the target traffic scheduling request using the delay optimization node in the target priority policy node; When the load in the actual network status meets the third preset condition, perform load balancing processing on the target traffic scheduling request using the load balancing node in the target priority policy node.

[0009] Optionally, in an embodiment of the present application, after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, it further includes: When it is detected that the bandwidth, delay, or load in the actual network status meets the preset change condition, obtain the new bandwidth, new delay, and new load in the actual network status; Switch the new traffic scheduling strategy using the new bandwidth, the new delay, and the new load to obtain the switched traffic scheduling strategy, and perform intelligent traffic scheduling on the target device according to the switched traffic scheduling strategy.

[0010] Optionally, in an embodiment of the present application, after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, it further includes: generating an intelligent traffic scheduling report based on the actual network status, the actual device type, the current traffic demand, and the new traffic scheduling policy; sending the intelligent traffic scheduling report to a preset terminal, and displaying the intelligent traffic scheduling report on the preset terminal.

[0011] An embodiment of the second aspect of the present application provides an intelligent traffic scheduling policy optimization device, including: an acquisition module, configured to acquire the actual network status, the actual device type, and the current traffic demand of the target device based on a target traffic scheduling request of the target device; a determination module, configured to determine a target traffic scheduling policy for the target device according to the actual network status, the actual device type, and the current traffic demand, and perform node processing on the target traffic scheduling request based on a target priority policy node to obtain a processed traffic request; a scheduling module, configured to update the target traffic scheduling policy by using the processed traffic request to determine a new traffic scheduling policy, and generate a target traffic scheduling management instance based on the new traffic scheduling policy, the actual network status, the actual device type, and the current traffic demand, so as to perform intelligent traffic scheduling on the target device according to the target traffic scheduling management instance.

[0012] Optionally, in an embodiment of the present application, the determination module includes: an extraction unit, configured to extract the bandwidth, delay, and load in the actual network status; a matching unit, configured to match a bandwidth priority policy, a delay optimization policy, or a load balancing policy in the target traffic scheduling policy according to the bandwidth, the delay, the load, the actual device type, and the current traffic demand.

[0013] Optionally, in an embodiment of the present application, the determination module includes: a first processing unit, configured to perform bandwidth priority scheduling processing on the target traffic scheduling request by using a bandwidth priority node in the target priority policy node when the bandwidth in the actual network status meets a first preset condition; a second processing unit, configured to perform delay optimization scheduling processing on the target traffic scheduling request by using a delay optimization node in the target priority policy node when the delay in the actual network status meets a second preset condition; a third processing unit, configured to perform load balancing processing on the target traffic scheduling request by using a load balancing node in the target priority policy node when the load in the actual network status meets a third preset condition.

[0014] Optionally, in an embodiment of the present application, the device of the embodiment of the present application further includes: an acquisition module, configured to, after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, when detecting that the bandwidth, latency, or load in the actual network state meets a preset change condition, acquire the new bandwidth, new latency, and new load in the actual network state; a processing module, configured to, after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, switch the new traffic scheduling policy by using the new bandwidth, the new latency, and the new load to obtain a switched traffic scheduling policy, and perform intelligent traffic scheduling on the target device according to the switched traffic scheduling policy.

[0015] Optionally, in an embodiment of the present application, the device of the embodiment of the present application further includes: a generation module, configured to generate an intelligent traffic scheduling report according to the actual network state, the actual device type, the current traffic demand, and the new traffic scheduling policy after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance; a sending module, configured to send the intelligent traffic scheduling report to a preset terminal after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, and display the intelligent traffic scheduling report on the preset terminal.

[0016] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the intelligent traffic scheduling policy optimization method as described in the above embodiment.

[0017] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, where the computer-readable storage medium stores a computer program, and when the program is executed by a processor, it implements the intelligent traffic scheduling policy optimization method as described above.

[0018] An embodiment of the fifth aspect of the present application provides a computer program product, including a computer program, where when the computer program is executed, it is used to implement the intelligent traffic scheduling policy optimization method as described above.

[0019] Embodiments of the present application can determine the target traffic scheduling strategy for the target device according to the actual network status, actual device type, and current traffic demand of the target device, perform node processing on the target traffic scheduling request based on the target priority policy node, update the target traffic scheduling strategy using the processed traffic request, and generate a target traffic scheduling management instance based on the new traffic scheduling strategy using the actual network status, actual device type, and current traffic demand to perform intelligent traffic scheduling on the target device, effectively improving the flexibility and scalability of traffic scheduling. Thus, it solves the problems in the related art that traffic scheduling mainly relies on static rules and algorithms and cannot be adaptively adjusted according to real-time network conditions, resulting in uneven traffic distribution and reducing the flexibility and scalability of traffic scheduling.

[0020] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and be readily understood from the following description of the embodiments in conjunction with the accompanying drawings, where:

[0022] Figure 1 FIG. [X] is a schematic diagram of an intelligent traffic scheduling strategy optimization system provided according to an embodiment of the present application;

[0023] Figure 2 FIG. [X] is a flowchart of an intelligent traffic scheduling strategy optimization method provided according to an embodiment of the present application;

[0024] Figure 3 FIG. [X] is a schematic diagram of the strategy pattern of a specific embodiment of the present application;

[0025] Figure 4 FIG. [X] is a specific implementation class diagram of the strategy pattern of a specific embodiment of the present application;

[0026] Figure 5 FIG. [X] is a schematic diagram of the responsibility chain pattern of a specific embodiment of the present application;

[0027] Figure 6 FIG. [X] is a specific implementation class diagram of the responsibility chain pattern of a specific embodiment of the present application;

[0028] Figure 7 FIG. [X] is a schematic diagram of the factory pattern of a specific embodiment of the present application;

[0029] Figure 8 FIG. [X] is a specific implementation class diagram of the factory pattern of a specific embodiment of the present application;

[0030] Figure 9 FIG. [X] is a schematic diagram of the state pattern of a specific embodiment of the present application;

[0031] Figure 10 Class diagram for the specific implementation of the status mode in a specific embodiment of this application;

[0032] Figure 11 Schematic structural diagram of an intelligent traffic scheduling strategy optimization device provided according to an embodiment of this application;

[0033] Figure 12 Schematic structural diagram of a vehicle provided according to an embodiment of this application. Detailed implementation manners

[0034] The embodiments of the present application will be described in detail below. Examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0035] The intelligent traffic scheduling strategy optimization method, device, electronic device and medium according to the embodiments of the present application will be described below with reference to the accompanying drawings. In view of the problems in the related art mentioned in the above background technology that traffic scheduling mainly relies on static rules and algorithms, cannot meet the traffic allocation under various traffic types, device requirements and network states, resulting in uneven traffic allocation, inability to perform adaptive adjustment according to the real-time network condition, and reduction of the flexibility and scalability of traffic scheduling, the present application provides an intelligent traffic scheduling strategy optimization method. In this method, the target traffic scheduling strategy of the target device can be determined according to the actual network state, actual device type and current traffic demand of the target device, and the target traffic scheduling request is processed by nodes based on the target priority policy node, the target traffic scheduling strategy is updated by using the processed traffic request, and based on the new traffic scheduling strategy, a target traffic scheduling management instance is generated by using the actual network state, actual device type and current traffic demand to perform intelligent traffic scheduling on the target device, effectively improving the flexibility and scalability of traffic scheduling. Thus, the problems in the related art that traffic scheduling mainly relies on static rules and algorithms, cannot perform adaptive adjustment according to the real-time network condition, resulting in uneven traffic allocation and reduction of the flexibility and scalability of traffic scheduling are solved.

[0036] As Figure 1 shown, an intelligent traffic scheduling strategy optimization system is established in the embodiments of the present application. The system includes: a policy selection module, a responsibility chain processing module, a factory generation module and a network state management module.

[0037] Among them, the policy selection module: Based on the policy pattern, selects the most suitable traffic scheduling policy according to the current network status, device type, and traffic demand; among them, the policy pattern: The system defines multiple traffic scheduling policies, and each policy is responsible for a specific type of traffic scheduling (such as bandwidth optimization, latency optimization, load balancing, etc.).

[0038] The responsibility chain processing module: Based on the responsibility chain pattern, the traffic request is passed among multiple policy nodes, gradually processed, and finally the scheduling is completed; among them, the responsibility chain pattern: The traffic scheduling process is split into multiple stages, and each stage processes a specific type of traffic request. If a certain stage cannot process the request, the traffic will be passed to the next stage for processing.

[0039] The policy factory module: Based on the factory pattern, dynamically generates a suitable traffic scheduling management instance according to the device type, current network status, and traffic demand; among them, the factory pattern: Responsible for generating a traffic scheduling management instance according to the network status and device type.

[0040] The network status management module: Based on the state pattern, determines the current state of the system according to the real-time network status (such as bandwidth, latency, load, etc.), and switches to the traffic scheduling policy suitable for the current state; among them, the state pattern: The system manages the network status through the state pattern, and each network status corresponds to a different scheduling mode. When the network status changes, the system will automatically switch to the scheduling policy suitable for the current state, so as to ensure that the traffic scheduling can adapt to the network changes.

[0041] Specifically,[[]]END]] Figure 2 It is a schematic flowchart of an intelligent traffic scheduling policy optimization method provided by an embodiment of the present application.

[0042] As Figure 2 shown, the intelligent traffic scheduling policy optimization method includes the following steps:

[0043] In step S201, based on the target traffic scheduling request of the target device, obtain the actual network status, actual device type, and current traffic demand of the target device.

[0044] In the embodiment of the present application, the target device is the device currently performing traffic scheduling, and can be a device in a large-scale distributed system such as the Internet of Things or the Internet of Vehicles.

[0045] It can be understood that the traffic scheduling policy optimization system in the embodiments of the present application can receive traffic scheduling requests from the current device and the platform. Then, based on the traffic scheduling requests, the embodiments of the present application obtain the actual network status of the device (such as bandwidth, latency, load, etc.), the actual device type (such as mobile phone, computer, etc.), and the current traffic demand, so as to dynamically select and apply the optimal traffic scheduling policy, such as bandwidth - priority policy, latency - optimization policy, and load - balancing policy, etc., effectively improving the executability of intelligent traffic scheduling policy optimization.

[0046] In step S202, determine the target traffic scheduling policy for the target device according to the actual network status, actual device type, and current traffic demand, and perform node processing on the target traffic scheduling request based on the target - priority policy node to obtain the processed traffic request.

[0047] In the embodiments of the present application, the target traffic scheduling policy can be a bandwidth - priority policy, a latency - optimization policy, and a load - balancing policy.

[0048] It can be understood that the embodiments of the present application can determine the traffic scheduling policy of the device according to the actual network status, actual device type, and current traffic demand, and based on the target - priority policy node. For example, through the responsibility - chain pattern in the above - mentioned system, the traffic scheduling process is split into multiple stages, and each stage processes different types of traffic. In each stage, the traffic is judged according to the rules in the following steps. If the current traffic optimization policy does not meet the conditions, the traffic scheduling request will be passed to the next processing node to obtain the processed traffic request. That is to say, the present application can pass the traffic request among multiple policy nodes through the responsibility - chain pattern in the above - mentioned system, and it is gradually processed and finally completed for scheduling, so that the traffic optimization policy can be adaptively adjusted according to the real - time network conditions, improving the flexibility of traffic scheduling.

[0049] Among them, in an embodiment of the present application, determining the target traffic scheduling policy for the target device according to the actual network status, actual device type, and current traffic demand includes: extracting the bandwidth, latency, and load in the actual network status; matching the bandwidth - priority policy, latency - optimization policy, or load - balancing policy in the target traffic scheduling policy according to the bandwidth, latency, load, actual device type, and current traffic demand.

[0050] In the actual execution process, the embodiments of the present application can extract the bandwidth, latency, and load in the actual network status, so as to match the bandwidth - priority policy, latency - optimization policy, or load - balancing policy in the traffic scheduling policy according to the bandwidth, latency, load, actual device type, and current traffic demand for traffic scheduling, improving the flexibility of traffic scheduling.

[0051] For example, Figure 3As shown, each traffic scheduling policy selects different policies for optimization according to different traffic demands and network states. For example, if the bandwidth is <100Mbps, the bandwidth priority policy is selected. Specifically, the above system can implement the policy pattern according to the following steps:

[0052] First, for the definition of the policy interface, a unified traffic scheduling policy interface is defined, and all specific scheduling policies implement this interface. This interface contains a core method scheduleTraffic(), which is used to execute the specific logic of traffic scheduling. Among them, TrafficRequest represents a traffic request, which contains information such as the type, size, and priority of the traffic; NetworkStatus represents the current network status, including bandwidth, latency, load, etc.

[0053] Next, Figure 4 For the implementation of specific policies, each specific scheduling policy class inherits the TrafficSchedulingStrategy interface and provides specific scheduling behaviors for different network states or traffic demands. For example, bandwidth priority policy, latency optimization policy, load balancing policy, etc.

[0054] Finally, when the above system receives a traffic scheduling request, the system will select a suitable policy according to the real-time network status. Through the policy pattern, the selection of the scheduling policy is no longer fixed, but dynamically adjusted. The traffic scheduling request will determine the best traffic scheduling policy according to information such as network bandwidth, latency, and load.

[0055] Among them, in an embodiment of the present application, based on the target priority policy node, the target traffic scheduling request is processed by nodes, including: when the bandwidth in the actual network status meets the first preset condition, using the bandwidth priority node in the target priority policy node to perform bandwidth priority scheduling processing on the target traffic scheduling request; when the latency in the actual network status meets the second preset condition, using the latency optimization node in the target priority policy node to perform latency optimization scheduling processing on the target traffic scheduling request; when the load in the actual network status meets the third preset condition, using the load balancing node in the target priority policy node to perform load balancing processing on the target traffic scheduling request.

[0056] In some embodiments, the responsibility chain pattern in the above system splits the traffic scheduling process into multiple stages, and each stage processes different types of traffic. In each stage, the traffic is judged according to certain rules. If the current traffic scheduling policy does not meet the conditions, the traffic request will be passed to the next processing node. For example, Figure 5As shown, at the bandwidth - first node: if the bandwidth is sufficient, bandwidth - first scheduling is performed; otherwise, it is passed to the next node for processing. At the latency - optimization node: if the response time is insufficient, latency - optimization scheduling is performed; otherwise, it is passed to the next node for processing. At the load - balancing node: if the load condition is not met, load - balancing processing is performed; otherwise, it is passed to the next node for processing. Finally, if all traffic - scheduling policies are unable to handle it, the traffic - scheduling request is discarded, ensuring that traffic is processed in order of priority, avoiding resource conflicts and excessive loads, and improving the stability of the system.

[0057] Specifically, in the above - mentioned traffic - scheduling policy optimization system, each responsibility - chain node represents a specific scheduling policy. For example, bandwidth - first, latency - optimization, etc. Each responsibility - chain node implements an interface that contains a processing method handleRequest() to determine whether to process the current request. If it cannot process, the request is passed to the next node.

[0058] As Figure 6 shown, the specific implementation of the responsibility - chain node will determine whether to process the current traffic request according to different network states. If the current policy cannot handle the traffic request, the request is passed to the next policy node.

[0059] Among them, the creation and processing of the responsibility chain are completed through the setNextHandler method of TrafficSchedulingHandler. Each responsibility - chain node will judge whether to process the traffic request according to its own rules. If it cannot process, the request is passed to the next node. Thus, the above - mentioned system can optimize traffic scheduling step by step, ensuring that traffic is optimally processed according to priority and resource conditions.

[0060] In step S203, the target traffic - scheduling policy is updated using the processed traffic request to determine a new traffic - scheduling policy. Based on the new traffic - scheduling policy, a target traffic - scheduling management instance is generated using the actual network state, actual device type, and current traffic demand to perform intelligent traffic scheduling on the target device according to the target traffic - scheduling management instance.

[0061] It can be understood that the embodiments of the present application can update the traffic scheduling policy by using the processed traffic requests. For example, according to the real-time network status, a suitable policy can be selected, so that the selection of the traffic scheduling policy is no longer fixed but dynamically adjusted. Thus, a new traffic scheduling policy, that is, the finally adjusted traffic scheduling policy, can be determined. Then, based on the new traffic scheduling policy and based on the factory pattern of the above system, a traffic scheduling management instance is generated by using the actual network status, the actual device type, and the current traffic demand, so as to perform intelligent traffic scheduling on the device according to the traffic scheduling management instance. Therefore, the traffic scheduling policy can be adaptively adjusted according to the real-time network condition, improving the flexibility and scalability of the traffic scheduling.

[0062] For example, as Figure 7 shown, the factory pattern in the above system is used to dynamically generate traffic scheduling management objects. Through the factory pattern, the system can generate appropriate traffic scheduling policy objects according to the current network status or device requirements, avoiding hard coding and enhancing the flexibility of the system.

[0063] First, a traffic scheduling management factory interface is defined, providing a factory method createSchedulingStrategy() for generating a suitable scheduling management object according to the network status and traffic requests. Then, according to different network statuses, specific factory classes will generate different scheduling management objects. As Figure 8 shown, in a network environment with bandwidth priority, the factory will generate a bandwidth-priority scheduling object; in an environment with latency optimization, the factory will generate a latency-optimization scheduling object. Finally, the above system creates a scheduling management object suitable for the current network status through the factory class, avoiding manual selection of the management object and improving the automation and intelligence of the system.

[0064] Optionally, in an embodiment of the present application, after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, it further includes: when it is detected that the bandwidth, latency, or load in the actual network status meets the preset change condition, obtaining the new bandwidth, new latency, and new load in the actual network status; using the new bandwidth, new latency, and new load to switch to a new traffic scheduling policy to obtain the switched traffic scheduling policy, and performing intelligent traffic scheduling on the target device according to the switched traffic scheduling policy.

[0065] In some embodiments, the above system manages the network status through the state pattern, and each network status corresponds to a different scheduling pattern. When the network status changes, the system will automatically switch to the scheduling policy suitable for the current state, thus ensuring that the traffic scheduling can adapt to network changes.

[0066] Among them, the network state management module: Based on the state pattern, it determines the current state of the system according to the real-time network state (such as bandwidth, latency, load, etc.), and switches to the traffic scheduling strategy that adapts to the current state. For example, Figure 9 As shown, the state pattern is used to dynamically manage the switching of network states and traffic scheduling strategies. The above system switches to different states according to the real-time state of the network (such as bandwidth, latency, etc.), and then selects the most appropriate traffic scheduling strategy.

[0067] Specifically, this application can define a NetworkState interface, and each specific network state class implements this interface, responsible for selecting and applying different traffic scheduling strategies according to the network state. The specific state class determines the traffic scheduling strategy according to conditions such as the current network bandwidth and latency. For example, as Figure 10 shown, when the bandwidth is sufficient, the bandwidth-first strategy is selected, and when the latency is high, the latency-optimization strategy is selected. Thus, according to the real-time network conditions, the system can dynamically switch network states and execute corresponding traffic scheduling strategies to ensure that the traffic scheduling can adapt to network changes. The traffic scheduling strategy optimization system can also flexibly expand new traffic scheduling strategies according to actual needs, enhancing the scalability and maintainability of the system.

[0068] It should be noted that the preset change conditions are set by those skilled in the art according to the actual situation and are not specifically limited herein.

[0069] Optionally, in an embodiment of this application, after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, it further includes: generating an intelligent traffic scheduling report according to the actual network state, actual device type, current traffic demand, and new traffic scheduling strategy; sending the intelligent traffic scheduling report to a preset terminal, and displaying the intelligent traffic scheduling report on the preset terminal.

[0070] For example, the embodiment of this application can generate an intelligent traffic scheduling report according to the actual network state, actual device type, current traffic demand, and new traffic scheduling strategy, and send the intelligent traffic scheduling report to the computer terminal of relevant technical personnel, and display the intelligent traffic scheduling report on the computer terminal, so that relevant technical personnel can view the traffic scheduling situation in a timely manner, effectively improving the interactivity and intelligence.

[0071] It should be noted that the preset terminal is set by those skilled in the art according to the actual situation and is not specifically limited herein.

[0072] The intelligent traffic scheduling strategy optimization method proposed according to the embodiments of the present application can determine the target traffic scheduling strategy of the target device based on the actual network status, actual device type, and current traffic demand of the target device, and perform node processing on the target traffic scheduling request based on the target priority policy node, update the target traffic scheduling strategy using the processed traffic request, and generate a target traffic scheduling management instance based on the new traffic scheduling strategy using the actual network status, actual device type, and current traffic demand to perform intelligent traffic scheduling on the target device, effectively improving the flexibility and scalability of traffic scheduling. Thus, it solves the problems in the related art that traffic scheduling mainly relies on static rules and algorithms and cannot be adaptively adjusted according to real-time network conditions, resulting in uneven traffic distribution and reducing the flexibility and scalability of traffic scheduling.

[0073] Next, refer to the drawings to describe the intelligent traffic scheduling strategy optimization device proposed according to the embodiments of the present application.

[0074] Figure 11 It is a block diagram of the intelligent traffic scheduling strategy optimization device according to the embodiments of the present application.

[0075] As Figure 11 shown, the intelligent traffic scheduling strategy optimization device 10 includes: an acquisition module 100, a determination module 200, and a scheduling module 300.

[0076] Specifically, the acquisition module 100 is configured to obtain the actual network status, actual device type, and current traffic demand of the target device based on the target traffic scheduling request of the target device.

[0077] The determination module 200 is configured to determine the target traffic scheduling strategy of the target device according to the actual network status, actual device type, and current traffic demand, and perform node processing on the target traffic scheduling request based on the target priority policy node to obtain the processed traffic request.

[0078] The scheduling module 300 is configured to update the target traffic scheduling strategy using the processed traffic request to determine a new traffic scheduling strategy, and generate a target traffic scheduling management instance based on the new traffic scheduling strategy using the actual network status, actual device type, and current traffic demand to perform intelligent traffic scheduling on the target device according to the target traffic scheduling management instance.

[0079] Optionally, in an embodiment of the present application, the determination module 200 includes: an extraction unit and a matching unit.

[0080] Among them, the extraction unit is configured to extract the bandwidth, latency, and load in the actual network status.

[0081] A matching unit, configured to match the bandwidth - priority policy, latency - optimization policy, or load - balancing policy in the target traffic scheduling policy according to bandwidth, latency, load, actual device type, and current traffic demand.

[0082] Optionally, in an embodiment of the present application, the determining module 200 includes: a first processing unit, a second processing unit, and a third processing unit.

[0083] Among them, the first processing unit is configured to perform bandwidth - priority scheduling processing on the target traffic scheduling request by using the bandwidth - priority node in the target priority - policy node when the bandwidth in the actual network state meets the first preset condition.

[0084] The second processing unit is configured to perform latency - optimization scheduling processing on the target traffic scheduling request by using the latency - optimization node in the target priority - policy node when the latency in the actual network state meets the second preset condition.

[0085] The third processing unit is configured to perform load - balancing processing on the target traffic scheduling request by using the load - balancing node in the target priority - policy node when the load in the actual network state meets the third preset condition.

[0086] Optionally, in an embodiment of the present application, the device 10 of the embodiments of the present application further includes: an acquisition module and a processing module.

[0087] Among them, the acquisition module is configured to, after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, when detecting that the bandwidth, latency, or load in the actual network state meets the preset change condition, acquire the new bandwidth, new latency, and new load in the actual network state.

[0088] The processing module is configured to, after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, switch to a new traffic scheduling policy by using the new bandwidth, new latency, and new load to obtain the switched - after traffic scheduling policy, and perform intelligent traffic scheduling on the target device according to the switched - after traffic scheduling policy.

[0089] Optionally, in an embodiment of the present application, the device 10 of the embodiments of the present application further includes: a generation module and a sending module.

[0090] Among them, the generation module is configured to, after performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, generate an intelligent traffic scheduling report according to the actual network state, actual device type, current traffic demand, and new traffic scheduling policy.

[0091] A sending module, configured to send an intelligent traffic scheduling report to a preset terminal after performing intelligent traffic scheduling on a target device according to a target traffic scheduling management instance, and display the intelligent traffic scheduling report on the preset terminal.

[0092] It should be noted that the foregoing explanation of the embodiments of the intelligent traffic scheduling strategy optimization method is also applicable to the intelligent traffic scheduling strategy optimization device of this embodiment, and will not be elaborated here.

[0093] The intelligent traffic scheduling strategy optimization device proposed according to the embodiments of the present application can determine the target traffic scheduling strategy of the target device according to the actual network status, actual device type, and current traffic demand of the target device, perform node processing on the target traffic scheduling request based on the target priority policy node, update the target traffic scheduling strategy by using the processed traffic request, and generate a target traffic scheduling management instance based on the new traffic scheduling strategy by using the actual network status, actual device type, and current traffic demand to perform intelligent traffic scheduling on the target device, effectively improving the flexibility and scalability of traffic scheduling. Thus, it solves the problems in the related art that traffic scheduling mainly relies on static rules and algorithms and cannot be adaptively adjusted according to real-time network conditions, resulting in uneven traffic distribution and reducing the flexibility and scalability of traffic scheduling.

[0094] Figure 12 The structural schematic diagram of the electronic device provided by the embodiments of the present application. The electronic device may include:

[0095] A memory 1201, a processor 1202, and a computer program stored on the memory 1201 and executable on the processor 1202.

[0096] When the processor 1202 executes the program, it implements the intelligent traffic scheduling strategy optimization method provided in the foregoing embodiments.

[0097] Further, the electronic device further includes:

[0098] A communication interface 1203, configured for communication between the memory 1201 and the processor 1202.

[0099] The memory 1201 is used to store a computer program executable on the processor 1202.

[0100] The memory 1201 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0101] If the memory 1201, the processor 1202, and the communication interface 1203 are implemented independently, the communication interface 1203, the memory 1201, and the processor 1202 can be interconnected through a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 12 only a thick line is used in Figure 12 , but it does not mean that there is only one bus or one type of bus.

[0102] Optionally, in a specific implementation, if the memory 1201, the processor 1202, and the communication interface 1203 are integrated on a single chip, the memory 1201, the processor 1202, and the communication interface 1203 can communicate with each other through an internal interface.

[0103] The processor 1202 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0104] This embodiment also provides a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the intelligent traffic scheduling policy optimization method as described above is implemented.

[0105] This embodiment also provides a computer program product, including a computer program, which is used to implement the intelligent traffic scheduling policy optimization method as described above when executed.

[0106] In the description of this specification, the descriptions referring to terms such as "one embodiment", "some embodiments", "examples", "specific examples", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of this application. In this specification, the schematic expressions of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without contradiction, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of different embodiments or examples.

[0107] In addition, the terms "first" and "second" are used only for descriptive purposes and cannot be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one of such features. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0108] Any process or method description depicted in a flowchart or described in other ways herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logic function or process, and the scope of the preferred embodiments of this application includes additional implementations, where the functions can be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0109] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a definite sequence list of executable instructions for implementing logical functions, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with these instruction execution systems, apparatuses, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection part (electronic device) having one or N wirings, a portable computer disk cartridge (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which the program can be printed, because the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other suitable processing as necessary, and then stored in a computer memory.

[0110] It should be understood that various parts of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logical functions on data signals, application specific integrated circuits having suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0111] Those of ordinary skill in the art of this technology can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by a program instructing relevant hardware, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0112] In addition, each functional unit in various embodiments of the present application may be integrated into one processing module, may exist separately physically for each unit, or two or more units may be integrated into one module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0113] The above-mentioned storage medium may be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. An intelligent traffic scheduling strategy optimization method, characterized in that It includes the following steps: Based on the target traffic scheduling request of the target device, obtain the actual network status, actual device type, and current traffic demand of the target device; Determine the target traffic scheduling policy of the target device according to the actual network status, the actual device type, and the current traffic demand, and perform node processing on the target traffic scheduling request based on the target priority policy node to obtain a processed traffic request; Use the processed traffic request to update the target traffic scheduling policy to determine a new traffic scheduling policy, and based on the new traffic scheduling policy, generate a target traffic scheduling management instance using the actual network status, the actual device type, and the current traffic demand, so as to perform intelligent traffic scheduling on the target device according to the target traffic scheduling management instance.

2. The method according to claim 1, wherein The determining the target traffic scheduling policy of the target device according to the actual network status, the actual device type, and the current traffic demand includes: Extract the bandwidth, delay, and load in the actual network status; Match the bandwidth priority policy, delay optimization policy, or load balancing policy in the target traffic scheduling policy according to the bandwidth, the delay, the load, the actual device type, and the current traffic demand.

3. The method according to claim 2, wherein The performing node processing on the target traffic scheduling request based on the target priority policy node includes: When the bandwidth in the actual network status meets the first preset condition, perform bandwidth priority scheduling processing on the target traffic scheduling request using the bandwidth priority node in the target priority policy node; When the delay in the actual network status meets the second preset condition, perform delay optimization scheduling processing on the target traffic scheduling request using the delay optimization node in the target priority policy node; When the load in the actual network status meets the third preset condition, perform load balancing processing on the target traffic scheduling request using the load balancing node in the target priority policy node.

4. The method according to claim 1, wherein After performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, it further includes: When it is detected that the bandwidth, delay, or load in the actual network status meets the preset change condition, obtain the new bandwidth, new delay, and new load in the actual network status; Switch the new traffic scheduling policy using the new bandwidth, the new delay, and the new load to obtain a switched traffic scheduling policy, and perform intelligent traffic scheduling on the target device according to the switched traffic scheduling policy.

5. The method according to claim 1, characterized in that, After performing intelligent traffic scheduling on the target device according to the target traffic scheduling management instance, it further includes: Generate an intelligent traffic scheduling report according to the actual network status, the actual device type, the current traffic demand, and the new traffic scheduling policy; Send the intelligent traffic scheduling report to a preset terminal and display the intelligent traffic scheduling report on the preset terminal.

6. An intelligent traffic scheduling strategy optimization device, characterized in that, It includes: An acquisition module, configured to obtain the actual network status, actual device type, and current traffic demand of the target device based on the target traffic scheduling request of the target device; A determination module, configured to determine a target traffic scheduling policy for the target device according to the actual network state, the actual device type, and the current traffic demand, and perform node processing on the target traffic scheduling request based on the target priority policy node to obtain a processed traffic request; A scheduling module, configured to update the target traffic scheduling policy by using the processed traffic request to determine a new traffic scheduling policy, and generate a target traffic scheduling management instance based on the new traffic scheduling policy, the actual network state, the actual device type, and the current traffic demand, so as to perform intelligent traffic scheduling on the target device according to the target traffic scheduling management instance.

7. The device according to claim 6, characterized in that, The determination module includes: An extraction unit, configured to extract the bandwidth, delay, and load in the actual network state; A matching unit, configured to match the bandwidth priority policy, the delay optimization policy, or the load balancing policy in the target traffic scheduling policy according to the bandwidth, the delay, the load, the actual device type, and the current traffic demand.

8. The device according to claim 6, wherein The determination module includes: A first processing unit, configured to perform bandwidth priority scheduling processing on the target traffic scheduling request by using the bandwidth priority node in the target priority policy node when the bandwidth in the actual network state meets a first preset condition; A second processing unit, configured to perform delay optimization scheduling processing on the target traffic scheduling request by using the delay optimization node in the target priority policy node when the delay in the actual network state meets a second preset condition; A third processing unit, configured to perform load balancing processing on the target traffic scheduling request by using the load balancing node in the target priority policy node when the load in the actual network state meets a third preset condition.

9. An electronic device, characterized in that, It includes: A memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the program to implement the intelligent traffic scheduling policy optimization method according to any one of claims 1-5.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used for implementing the intelligent traffic scheduling policy optimization method according to any one of claims 1-5.

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