DAG task scheduling control method based on neural network scoring

By constructing a multi-dimensional task feature system and a hybrid neural network scoring model, and dynamically optimizing DAG task scheduling, the problems of resource idleness and low task execution efficiency in heterogeneous resource environments are solved. This achieves an efficient and adaptive task scheduling strategy, improving resource utilization and task execution efficiency.

CN121300947AActive Publication Date: 2026-01-09ZHONGBO INFORMATION TECH RES INST CO LTD
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Patent Information

Application Number
CN202511567015.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-30
Publication Date
2026-01-09
Estimated Expiration
2045-10-30

AI Technical Summary

Technical Problem

Existing DAG task scheduling methods struggle to dynamically adapt to task resource requirements in heterogeneous resource environments, leading to resource idleness or overload. Furthermore, traditional scheduling strategies cannot respond to system load changes in real time, resulting in low task execution efficiency and an imbalance in global optimality.

Method used

A multi-dimensional task feature system is constructed, a hybrid neural network scoring model is designed, and the scheduling strategy and parallelism are dynamically optimized by combining CNN and MLP to achieve task priority scoring and resource matching. Combined with a resource contention model and asynchronous monitoring mechanism, the task execution order and parallelism are adjusted in real time.

Benefits of technology

It significantly improves resource utilization, reduces task latency and increases throughput, enhances the adaptability of scheduling strategies, improves task success rate and system reliability, and adapts to complex distributed environments.

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Abstract

The invention provides a DAG (Directed Acyclic Graph) task scheduling control method based on neural network scoring, which belongs to the technical field of task scheduling control and comprises the following steps of: 1, constructing a DAG task dependency graph according to tasks and dependency relationships thereof; 2, extracting multi-dimensional task features from the DAG task dependency graph and system historical data; 3, inputting core task characteristics, and outputting task scheduling priority scores; 4, performing resource availability judgment on the task; step 5, dynamically adjusting the maximum degree of parallelism of the task; step 6, when the scheduling task is executed, performing task execution monitoring and state updating at the same time; and 7, cyclically scheduling and repeatedly executing the processes from the step 2 to the step 6 until all tasks in the DAG task dependency graph are completed. By constructing a multi-dimensional task feature system, designing a hybrid neural network scoring model and dynamically optimizing a scheduling strategy and the degree of parallelism, efficient, intelligent and adaptive task scheduling oriented to a complex distributed environment is realized.
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