计算机工程与应用2024，Vol.60Issue(10)：301-310,10.DOI:10.3778/j.issn.1002-8331.2304-0194
混合动作空间下的多设备边缘计算卸载方法
Multi-Device Edge Computing Offload Method in Hybrid Action Space
摘要
Abstract
In order to reduce the total cost of device-level in multi-device multi-edge server scenarios and solve the algo-rithm limitation of existing deep reinforcement learning(DRL)that only supports a single action space,a hybrid-based multi-agent deep determination policy gradient(H-MADDPG)is proposed.Firstly,the MEC system model is established by considering various complex environmental conditions,such as the dynamic change of computing power of IoT devices/servers with load,time-varying wireless transmission channel gain,unknown energy harvesting,and the uncertainty of task size.Then,the problem model is established with the minimum total cost of integrated delay and energy consumption in a continuous time slot as the optimization objective.Finally,the problem is delivered to H-MADDPG in the form of Markov decision process(MDP),which trains two parallel policy networks with the assistance of the value network,and outputs discrete server selection and continuous task offload rate.The experimental results show that the H-MADDPG method has good convergence and stability.From different perspectives,such as whether the computing tasks are inten-sive or delay sensitive,the overall system return of H-MADDPG is better than Local,OffLoad and DDPG.Compared with other methods,it can maintain greater system throughput under the demand of computationally intensive tasks.关键词
物联网(IoT)/边缘计算卸载/多智能体深度确定性策略梯度(MADDPG)/混合动作空间Key words
Internet of things(IoT)/mobile edge computing/multi-agent deep determination policy gradient(MADDPG)/hybrid action space分类
信息技术与安全科学引用本文复制引用
张冀,齐国梁,朵春红,龚雯雯..混合动作空间下的多设备边缘计算卸载方法[J].计算机工程与应用,2024,60(10):301-310,10.基金项目
国家自然科学基金(61971190) （61971190）
河北省省级科技计划(22310302D) （22310302D）
中央高校基本科研业务费专项资金(2021MS086). （2021MS086）
