Computing-storage stream joint scheduling optimization method and system based on DDQN and heuristic strategy

Through the joint scheduling optimization method of computing-store streams based on DDQN and heuristic strategies, the dynamic perception and resource separation problems of traditional scheduling systems in large-scale distributed computing environments are solved, and the refinement of task scheduling and multi-objective optimization are achieved, which improves the system's task execution efficiency and resource utilization.

CN120371483AActive Publication Date: 2025-07-25QILU UNIVERSITY OF TECHNOLOGY (SHANDONG ACADEMY OF SCIENCES) +1
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Patent Information

Application Number
CN202510863895.X
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-06-26
Publication Date
2025-07-25
Estimated Expiration
2045-06-26

AI Technical Summary

Technical Problem

Traditional task scheduling systems lack dynamic perception capabilities in large-scale distributed computing environments, resulting in low resource utilization, high task latency, and separation of data from computing, making it difficult to adapt to high concurrency and dynamic load scenarios. In addition, the existing scheduling methods generally have weak generalization capabilities in state representations, making it difficult to meet the needs of high performance and high elasticity.

Method used

The calculation-store flow joint scheduling optimization method based on DDQN and heuristic strategies is adopted. By decoupling the computing flow and storage flow, the state representation and action space are optimized, the task dependency structure, resource state and historical scheduling records are integrated, and the scheduling decisions are made in combination with Q-value evaluation and heuristic rules to achieve task load balancing and execution time minimization.

Benefits of technology

It significantly improves task execution efficiency and resource utilization, realizes refinement of task scheduling and multi-objective optimization, and improves the overall performance and resource utilization of the system.

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Abstract

The invention relates to a calculation-storage stream joint scheduling optimization method and system based on DDQN and a heuristic strategy, and belongs to the technical field of distributed calculation and intelligent scheduling. The calculation-storage stream joint scheduling optimization method comprises the following steps: (1) defining a calculation stream and a storage stream; (2) workflow scheduling and execution: when the workflow is triggered, a scheduler evaluates the executable states of the tasks one by one according to the dependency relationship between the tasks and a scheduling strategy, and submits the executable tasks to a task executor; the task executor is responsible for executing the tasks on the working nodes and ensuring that the tasks are completed according to a preset sequence and priority; (3) collecting a DAG structure, queue information and an operation number; (4) task operation time and resource use prediction based on historical operation data; (5) scheduling optimization based on DDQN and a heuristic strategy; and (6) acquiring and storing scheduling information. The task execution efficiency and the resource utilization rate of the system are remarkably improved.
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