信息工程大学学报2024，Vol.25Issue(1)：30-38,9.DOI:10.3969/j.issn.1671-0673.2024.01.005
基于改进蚁群的异构平台负载均衡调度算法
Load Balancing Scheduling Algorithm for Heterogeneous Platform Based on Improved Ant Colony Optimization
李宇东 1马金全 2胡泽明 2岳春生 2谢宗甫2
作者信息
- 1. 信息工程大学,河南 郑州 450001||65022 部队,辽宁 沈阳 110000
- 2. 信息工程大学,河南 郑州 450001
- 折叠
摘要
Abstract
To address the problems of single scheduling algorithms and wasted processor resources for signal processing tasks in current heterogeneous platforms,a load balancing scheduling algorithm with Q-learning enhanced ant colony algorithm for heterogeneous systems is proposed.The algorithm is designed to prioritize tasks by a triage sorting method for the different needs of computation-inten-sive and communication-intensive tasks.Q-learning and ant colony algorithms are mapped to task scheduling in heterogeneous signal processing platforms through scenario adaptation.The Q-table is dynamically computed using the reward function and is used as the initial pheromone of the ant colo-ny algorithm,which speeds up the convergence of the ant colony.The load matrix is designed to dy-namically adjust system load balancing according to the real-time load on the processor.Pseudo-ran-dom scaling rules are used to make processor selections.Tasks are assigned by creating a schedule list with constraint relationships between tasks.Finally,simulation experiments are performed with randomly generated directed acyclic graphs.The results show significant improvements in both the reduction of the maximum completion time(scheduling length)and the increase in processor utiliza-tion.关键词
任务调度/异构信号处理平台/Q学习/蚁群算法Key words
task scheduling/heterogeneous platform/Q-learning/ant colony algorithm分类
信息技术与安全科学引用本文复制引用
李宇东,马金全,胡泽明,岳春生,谢宗甫..基于改进蚁群的异构平台负载均衡调度算法[J].信息工程大学学报,2024,25(1):30-38,9.
