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

作　　者：雷雪梅 张贺同 LEI Xuemei;ZHANG Hetong(Office of Information Technology,University of Science and Technology Beijing,Beijing 100083,China;School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China)

机构地区：[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

出　　处：《现代电子技术》2025年第6期45-51,共7页Modern Electronics Technique

摘　　要：移动边缘计算的计算密集型任务多为工作流任务,传统方法在解决工作流任务卸载问题时很难充分考虑子任务之间的依赖关系,并且计算卸载算法性能不佳。为了解决以上问题,将工作流任务卸载问题建模为马尔可夫决策过程下的最优策略问题,构建问题的状态空间、动作空间和奖励函数。以最小化工作流任务的任务完成时间和系统能耗为目标,提出一种融合注意力机制的基于深度强化学习(DRL)的工作流任务卸载算法(DWTOAA)。该方法使用分段式奖励函数来提高模型训练速度,并结合注意力机制提高算法对工作流任务终止执行状态的识别能力。实验结果表明,DWTOAA方法相较于DRL算法具有更快的训练速度,同时在求解不同子任务数的工作流任务时,DWTOAA得到的卸载决策均具有更少的任务完成时间和系统能耗。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 

分 类 号：TN929.5-34[电子电信—通信与信息系统]