湖南大学学报（自然科学版）2024，Vol.51Issue(6)：73-85,13.DOI:10.16339/j.cnki.hdxbzkb.2024268
D3DQN-CAA:一种基于DRL的自适应边缘计算任务调度方法
D3DQN-CAA:a DRL-based Adaptive Edge Computing Task Scheduling Method
摘要
Abstract
To solve the problems faced by the existing edge computing task scheduling based on deep reinforcement learning,such as fixed action space exploration,low sample efficiency,large memory demand and poor stability and to better carry out effective task scheduling in the edge computing system with relatively limited computing resources,an adaptive edge computing task scheduling method D3DQN-CAA is proposed based on the improved deep reinforcement learning model D3DQN(Dueling Double DQN).In the task offloading decision,the corresponding relationship between the task and processor is regarded as a multidimensional knapsack problem,and the computing node with the highest matching degree is selected for task processing according to the state information of the current scheduled task and the computing node;For improving the parameters updating efficiency of the evaluation network and reducing the influence of overestimation,a comprehensive Q-value calculation method is proposed;For accelerating the convergence speed of neural networks,an adaptive dynamic exploration degree of action space adjustment strategy is proposed;For reducing the storage resources required and improving the sample efficiency,an adaptive lightweight prioritized playback mechanism is proposed.Experimental results show that compared with multiple benchmark algorithms,the D3DQN-CAA algorithm can effectively reduce the number of training steps of deep reinforcement learning networks and make full use of edge computing resources to improve the real-time performance of task processing and reduce the system energy consumption.关键词
边缘计算/任务调度/深度Q学习/深度强化学习Key words
edge computing/task scheduling/deep Q-learning/deep reinforcement learning分类
信息技术与安全科学引用本文复制引用
巨涛,王志强,刘帅,火久元,李启南..D3DQN-CAA:一种基于DRL的自适应边缘计算任务调度方法[J].湖南大学学报（自然科学版）,2024,51(6):73-85,13.基金项目
国家自然科学基金资助项目(61862037,62262038),National Natural Science Foundation of China(61862037,62262038) （61862037,62262038）
甘肃省科技计划项目(23CXGA0028),Science and Technology Project of Gansu Province(23CXGA0028) （23CXGA0028）
甘肃省自然科学基金资助项目(22JR5RA356),Natural Science Foundation of Gansu Province(22JR5RA356). （22JR5RA356）
