Intelligent resource scheduling method and system based on calculation network map

Abstract

The invention discloses an intelligent resource scheduling method and system based on a computing network map, and belongs to the technical field of computing network fusion, and the method comprises the steps: obtaining the state information of each computing power node in real time, obtaining the geographic position of the node and a network topology structure at the same time, and constructing a computing network map model; based on the calculation network map data, analyzing each node index, and evaluating the calculation power availability of the nodes; a multi-path and multi-target dynamic scheduling strategy is generated based on a calculation network map model in combination with business requirements and service indexes so as to ensure that tasks are executed on the optimal path; according to the real-time monitoring data, a task scheduling path is intelligently adjusted to avoid high-load nodes; scheduling effect data are output in real time, and manual intervention and algorithm self-learning optimization are supported, so that the accuracy and efficiency of resource scheduling are continuously improved. According to the method, the optimal computing power path selection of the business task can be realized, the resource utilization rate is improved, the task delay is reduced, and the efficient computing power service requirement is met.

Description

Technical Field

[0001] The present invention relates to the field of computing network integration technology, and in particular to an intelligent resource scheduling method and system based on a computing network map. Background Art

[0002] With the rapid development of cloud computing, edge computing, and network resources, resource scheduling has become increasingly complex. Traditional static resource allocation methods are no longer able to meet the computing power demands of dynamic business scenarios. In a multi-node, multi-path computing and network convergence environment, how to combine network topology, computing power status, and geographic location to achieve efficient and low-latency task scheduling has become a pressing technical challenge. Summary of the Invention

[0003] The technical task of the present invention is to address the above shortcomings and provide an intelligent resource scheduling method and system based on computing network map, which can realize the optimal computing power path selection for business tasks, improve resource utilization, reduce task delays, and meet the needs of efficient computing power services.

[0004] The technical solution adopted by the present invention to solve its technical problem is:

[0005] An intelligent resource scheduling method based on a computing network map, the implementation of which includes the following steps:

[0006] 1) Data collection and modeling: Real-time acquisition of the status information of each computing node, as well as the node's geographic location and network topology, to build a computing network map model;

[0007] 2) Resource status analysis: Based on the computing network map data, analyze the indicators of each node and evaluate the computing power availability of the node;

[0008] 3) Scheduling Strategy Generation: Based on the computing network map model, a multi-path, multi-target dynamic scheduling strategy is generated based on business requirements and service indicators to ensure that tasks are executed on the optimal path. The optimal scheduling path generated based on the computing network map model meets the task execution requirements of low latency and high instance count.

[0009] 4) Dynamic scheduling execution: Based on real-time monitoring data, the task scheduling path is intelligently adjusted to avoid high-load nodes and ensure stable service quality;

[0010] 5) Result feedback and optimization: Output scheduling effect data in real time, support manual intervention and algorithm self-learning optimization, thereby continuously improving the accuracy and efficiency of resource scheduling.

[0011] This method collects network computing resource status data in real time, combines the spatial geographic location and node load of the computing network map, intelligently analyzes and dynamically adjusts the resource scheduling strategy, thereby achieving the optimal computing path selection for business tasks, improving resource utilization, reducing task delays, and meeting the needs of efficient computing services.

[0012] Furthermore, the status information of each computing power node includes information such as the CPU, memory, and bandwidth of the computing power node.

[0013] Furthermore, the resource status analysis analyzes the indicators of each node, including load, network delay, bandwidth, etc.

[0014] Furthermore, the method also includes automated verification, wherein the automated verification execution step receives the process model and related rules as input and automatically performs a verification function.

[0015] The present invention also claims protection for an intelligent resource scheduling system based on a computing network map, comprising:

[0016] The data collection and modeling module is used to obtain the status information of each computing power node in real time, as well as the node's geographic location and network topology to build a computing network map model;

[0017] The resource status analysis module is used to analyze the indicators of each node based on the computing network map data and evaluate the computing power availability of the node;

[0018] The scheduling strategy generation module is used to combine business needs and service indicators to generate multi-path, multi-target dynamic scheduling strategies based on the computing network map model to ensure that tasks are executed on the optimal path. The optimal scheduling path generated based on the computing network map model meets the task execution requirements of low latency and high instance count.

[0019] Dynamic scheduling execution module, used to intelligently adjust task scheduling paths based on real-time monitoring data to avoid high-load nodes and ensure stable service quality;

[0020] The result feedback and optimization module is used to output scheduling effect data in real time, and supports manual intervention and algorithm self-learning optimization to continuously improve the accuracy and efficiency of resource scheduling.

[0021] Furthermore, the status information of each computing power node includes information such as the CPU, memory, and bandwidth of the computing power node.

[0022] Furthermore, the resource status analysis analyzes the indicators of each node, including load, network delay, bandwidth, etc.

[0023] Furthermore, the system also includes an automatic verification module, which is used to receive the process model and related rules as input and automatically perform a verification function.

[0024] The present invention also claims protection for an intelligent resource scheduling device based on a computing network map, comprising: at least one memory and at least one processor;

[0025] The at least one memory is configured to store a machine-readable program;

[0026] The at least one processor is configured to call the machine-readable program to implement the above method.

[0027] The present invention also claims protection for a computer-readable medium having computer instructions stored thereon, which implement the above method when executed by a processor.

[0028] Compared with the prior art, the intelligent resource scheduling method and system based on computing network map of the present invention has the following beneficial effects:

[0029] The present invention collects network computing resource status data in real time, combines the spatial geographic location and node load conditions of the computing network map, intelligently analyzes and dynamically adjusts resource scheduling strategies, thereby achieving optimal computing path selection for business tasks, improving resource utilization, reducing task delays, and meeting the needs of efficient computing services.

[0030] Able to achieve:

[0031] 1. Dynamically perceive network and computing power status, adjust scheduling paths in real time, and improve resource utilization.

[0032] 2. Combined with the geographic information of the computing network map, intelligent resource scheduling across regions and nodes can be achieved to meet low-latency business needs.

[0033] 3. Support strategy self-learning and optimization to continuously improve scheduling efficiency and service quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 This is a flowchart of an intelligent resource scheduling method based on a computing network map provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0036] An embodiment of the present invention provides an intelligent resource scheduling method based on a computing network map. The implementation of the method includes the steps of data collection and modeling, resource status analysis, scheduling strategy generation, dynamic scheduling execution, and result feedback and optimization.

[0037] 1. Data collection and modeling:

[0038] Obtain the CPU, memory, bandwidth and other status information of each computing node in real time, and obtain the node's geographical location and network topology structure to build a computing network map model.

[0039] 2. Resource status analysis:

[0040] Based on the computing network map data, analyze indicators such as node load, network latency, bandwidth, etc. to evaluate the computing power availability of the node.

[0041] 3. Scheduling strategy generation:

[0042] Combining business needs and service indicators, a multi-path, multi-target dynamic scheduling strategy is generated to ensure that tasks are executed on the optimal path. The optimal scheduling path is generated based on the computing network map model to meet the task execution requirements of low latency and high instance count.

[0043] 4. Dynamic scheduling execution:

[0044] Based on real-time monitoring data, the task scheduling path is intelligently adjusted to avoid high-load nodes and ensure stable service quality.

[0045] 5. Result feedback and optimization:

[0046] Output scheduling effect data in real time, support manual intervention and algorithm self-learning optimization, thereby continuously improving the accuracy and efficiency of resource scheduling.

[0047] The method further includes an automated verification step, wherein the automated verification execution step receives the process model and related rules as input and automatically performs a verification function.

[0048] This method collects network computing resource status data in real time, combines the spatial geographic location and node load of the computing network map, intelligently analyzes and dynamically adjusts the resource scheduling strategy, thereby achieving the optimal computing path selection for business tasks, improving resource utilization, reducing task delays, and meeting the needs of efficient computing services.

[0049] The embodiment of the present invention further provides an intelligent resource scheduling system based on a computing network map, comprising:

[0050] The data acquisition and modeling module is used to obtain real-time status information of each computing node, as well as the node's geographic location and network topology, to build a computing network map model. The status information of each computing node includes information such as the CPU, memory, and bandwidth of the computing node.

[0051] The resource status analysis module is used to analyze the indicators of each node based on the computing network map data, including load, network latency, bandwidth, etc., and evaluate the computing power availability of the node.

[0052] The scheduling strategy generation module is used to combine business needs and service indicators to generate multi-path, multi-target dynamic scheduling strategies to ensure that tasks are executed on the optimal path; based on the computing network map model, the optimal scheduling path is generated to meet the task execution requirements of low latency and high instance count.

[0053] The dynamic scheduling execution module is used to intelligently adjust the task scheduling path based on real-time monitoring data to avoid high-load nodes and ensure stable service quality.

[0054] The result feedback and optimization module is used to output scheduling effect data in real time, and supports manual intervention and algorithm self-learning optimization to continuously improve the accuracy and efficiency of resource scheduling.

[0055] The system also includes automated verification. The automated verification module receives the process model and related rules as input and automatically performs verification functions.

[0056] An embodiment of the present invention further provides an intelligent resource scheduling device based on a computing network map, comprising: at least one memory and at least one processor;

[0057] The at least one memory is configured to store a machine-readable program;

[0058] The at least one processor is used to call the machine-readable program to implement the intelligent resource scheduling method based on the computing network map described in the above embodiment.

[0059] An embodiment of the present invention further provides a computer-readable medium having computer instructions stored thereon. When executed by a processor, the computer instructions implement the intelligent resource scheduling method based on a computing network map described in the above embodiments. Specifically, a system or device equipped with a storage medium can be provided. The storage medium stores software program code that implements the functions of any of the above embodiments, and the computer (or CPU or MPU) of the system or device can read and execute the program code stored in the storage medium.

[0060] In this case, the program code itself read from the storage medium can realize the function of any one of the above-mentioned embodiments, and thus the program code and the storage medium storing the program code constitute part of the present invention.

[0061] Examples of storage media for providing program code include floppy disks, hard disks, magneto-optical disks, optical disks (such as CD-ROM, CD-R, CD-RW, DVD-ROM, DVD-RAM, DVD-RW, DVD+RW), magnetic tapes, non-volatile memory cards, and ROMs. Alternatively, the program code can be downloaded from a server computer via a communication network.

[0062] In addition, it should be clear that the functions of any of the above embodiments can be achieved not only by executing the program code read by the computer, but also by enabling the operating system operating on the computer to complete part or all of the actual operations based on the instructions of the program code.

[0063] In addition, it can be understood that the program code read out from the storage medium is written into the memory provided in the expansion board inserted into the computer or into the memory provided in the expansion unit connected to the computer, and then based on the instructions of the program code, the CPU installed on the expansion board or expansion unit is enabled to perform part or all of the actual operations, thereby realizing the functions of any of the above embodiments.

[0064] The present invention has been shown and described in detail above through the accompanying drawings and preferred embodiments. However, the present invention is not limited to these disclosed embodiments. Based on the above multiple embodiments, those skilled in the art can know that the code review methods in the above different embodiments can be combined to obtain more embodiments of the present invention, and these embodiments are also within the scope of protection of the present invention.

Claims

1. An intelligent resource scheduling method based on a computing network map, characterized in that: The implementation of this method includes the following steps: 1) Data collection and modeling: Real-time acquisition of the status information of each computing node, as well as the node's geographic location and network topology, to build a computing network map model; 2) Resource status analysis: Based on the computing network map data, analyze the indicators of each node and evaluate the computing power availability of the node; 3) Scheduling strategy generation: Based on business requirements and service indicators, a multi-path, multi-target dynamic scheduling strategy is generated based on the computing network map model to ensure that tasks are executed on the optimal path. 4) Dynamic scheduling execution: Based on real-time monitoring data, intelligently adjust task scheduling paths to avoid high-load nodes; 5) Result feedback and optimization: Output scheduling effect data in real time, support manual intervention and algorithm self-learning optimization, thereby continuously improving the accuracy and efficiency of resource scheduling.

2. The intelligent resource scheduling method based on computing network map according to claim 1 is characterized in that: The status information of each computing power node includes the CPU, memory, and bandwidth information of the computing power node.

3. The intelligent resource scheduling method based on computing network map according to claim 1 is characterized in that: The resource status analysis analyzes the indicators of each node, including load, network delay, and bandwidth.

4. The intelligent resource scheduling method based on computing network map according to claim 1 is characterized in that: The method further includes an automated verification step, wherein the automated verification execution step receives the process model and related rules as input and automatically performs a verification function.

5. An intelligent resource scheduling system based on a computing network map, characterized in that: include: The data collection and modeling module is used to obtain the status information of each computing power node in real time, as well as the node's geographic location and network topology to build a computing network map model; The resource status analysis module is used to analyze the indicators of each node based on the computing network map data and evaluate the computing power availability of the node; The scheduling strategy generation module is used to combine business needs and service indicators to generate multi-path, multi-target dynamic scheduling strategies based on the computing network map model to ensure that tasks are executed on the optimal path; Dynamic scheduling execution module, used to intelligently adjust task scheduling paths based on real-time monitoring data to avoid high-load nodes; The result feedback and optimization module is used to output scheduling effect data in real time, and supports manual intervention and algorithm self-learning optimization to continuously improve the accuracy and efficiency of resource scheduling.

6. The intelligent resource scheduling system based on computing network map according to claim 5 is characterized in that: The status information of each computing power node includes the CPU, memory, and bandwidth information of the computing power node.

7. The intelligent resource scheduling system based on computing network map according to claim 5 is characterized in that: The resource status analysis analyzes the indicators of each node, including load, network delay, and bandwidth.

8. The intelligent resource scheduling system based on computing network map according to claim 5 is characterized in that: The system also includes an automatic verification module, which receives the process model and related rules as input and automatically performs a verification function.

9. An intelligent resource scheduling device based on a computing network map, characterized in that: include: at least one memory and at least one processor; The at least one memory is configured to store a machine-readable program; The at least one processor is configured to call the machine-readable program to implement the method according to any one of claims 1 to 7.

10. A computer-readable medium, characterized in that The computer readable medium stores computer instructions, which, when executed by a processor, implement the method according to any one of claims 1 to 7.

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