Computing power network resource scheduling method

By building a global resource pool, dividing computing power areas, using deep reinforcement learning and long-term and short-term memory networks to optimize resource scheduling, the problem of resource scheduling separation in computing power network is solved, precise quantitative evaluation and efficient utilization of resources are achieved, and the overall scheduling efficiency and stability of the system are improved.

CN120434159APending Publication Date: 2025-08-05STATE GRID INFORMATION & TELECOMM BRANCH
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Patent Information

Application Number
CN202510797321.7
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-16
Publication Date
2025-08-05

AI Technical Summary

Technical Problem

The separation of resource scheduling in the computing power network leads to the inability to form a global optimal solution, affecting the overall operation efficiency, and making it difficult to achieve efficient resource utilization and service quality improvement.

Method used

By obtaining the floating-point computing performance and operating status data of each node, building a global resource pool and dividing computing power areas, combining task requirements and network traffic data, deep reinforcement learning and long-term memory networks are used to predict link congestion, and scheduling execution plans are generated, and tasks are remapping with the effective computing power of the node as the weight.

Benefits of technology

It realizes accurate quantitative evaluation of computing resources, improves the matching accuracy between tasks and resources, ensures the stability of data transmission, and significantly improves resource utilization and system scheduling efficiency.

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Abstract

The invention relates to a computing power network resource scheduling method. The method comprises the following steps: acquiring floating point operation performance parameters and operation states of node processors and energy index data of data centers where the node processors are located, and calculating to generate a node list; constructing a global resource pool based on the list, and generating a resource distribution table containing the total calculation power of the region; obtaining calculation requirements and time delay constraints of the task queue, extracting feature vectors in combination with the resource distribution table, and generating a resource utilization rate table; obtaining network link flow data, predicting a link congestion probability through a long short-term memory network, and generating a flow control strategy table; and finally, updating resource pool network constraints according to the resource utilization rate table and the flow control strategy table, and remapping tasks by taking node effective computing power as a weight to generate a scheduling execution scheme. According to the method, accurate quantitative evaluation of the computing power resources is realized, the matching precision of tasks and the computing power resources is effectively improved, the resource utilization rate of the computing power network can be remarkably improved, and the overall scheduling efficiency and stability of the system are enhanced.
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