现代电子技术2025，Vol.48Issue(6)：45-51,7.DOI:10.16652/j.issn.1004-373x.2025.06.007
移动边缘计算中融合注意力机制的DRL工作流任务卸载算法
DRL workflow task offloading algorithm with attention mechanism in MEC
雷雪梅 1张贺同2
作者信息
- 1. 北京科技大学 信息化建设与管理办公室,北京 100083
- 2. 北京科技大学 自动化学院,北京 100083
- 折叠
摘要
Abstract
Most of the computing intensive tasks of mobile edge computing(MEC)are workflow tasks.It is difficult for traditional methods to fully consider the dependency between sub tasks when solving the workflow task offloading,and the performance of computing offloading algorithm is poor.In order to solve above problems,the problem of workflow task offloading is modeled as the problem of the optimal strategy under the Markov decision process,and the state space,action space,and reward function of problems are constructed.In order to minimize the task completion time and system energy consumption of the workflow tasks,a deep reinforcement learning(DRL)based workflow task offloading algorithm integrating attention mechanism(DWTOAA)is proposed.In this method,the segmented reward function is used to increase the training speed of the model,and the attention mechanism is combined to improve the algorithm's ability to recognize the termination status of workflow tasks.The experimental results show that the DWTOAA has a faster training speed compared with the DRL algorithm,and the offloading decisions obtained by DWTOAA have smaller task completion time and system energy consumption when solving workflow tasks with different numbers of subtasks.关键词
移动边缘计算/注意力机制/工作流任务/任务卸载/深度强化学习/马尔可夫决策过程/系统能耗Key words
mobile edge computing/attention mechanism/workflow task/task offloading/deep reinforcement learning/Markov decision process/system energy consumption分类
信息技术与安全科学引用本文复制引用
雷雪梅,张贺同..移动边缘计算中融合注意力机制的DRL工作流任务卸载算法[J].现代电子技术,2025,48(6):45-51,7.
