摘要
针对车联网场景下计算任务卸载至远端云中造成的延迟波动和能耗增加问题,搭建了融合软件定义网络和移动边缘计算的车联网系统模型。为了最小化车联网中计算卸载的时延和能耗,设计了一种基于深度强化学习的动态计算卸载策略。该策略从计算任务的能耗和时延这两个维度进行建模,并提出在传统的双延迟深度确定性策略梯度的基础上加入Softmax和优先经验回放的改进算法(SP-TD3)。仿真实验结果表明,设计的计算卸载策略在不同车辆数下对于能耗和时延有较为优越的性能。
To solve the problems of delay fluctuation and increased energy consumption caused by offloading computational tasks to the remote cloud in the Internet of vehicles scenario,an Internet of vehicles system model integrating software defined networking and mobile edge computing was built.To minimize the delay and energy consumption of computation offloading in the Internet of vehicles,a dynamic computation offloading strategy based on deep reinforcement learning was designed.The strategy was modeled in two dimensions of energy consumption and delay of computational tasks,and an improved algorithm of adding Softmax and prioritized experience replay based on the traditional twin delayed deep deterministic policy gradient was proposed,which named Twin Delayed Deep Deterministic policy gradient with Softmax and Prioritized experience replay(SP-TD3).The simulation experimental results showed that the proposed algorithm had superior performance for energy consumption and delay under different vehicle numbers.
作者
李国燕
薛翔
刘毅
潘玉恒
LI Guoyan;XUE Xiang;LIU Yi;PAN Yuheng(School of Computer and Information Engineering,Tianjin Chengjian University,Tianjin 300384,China)
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2023年第5期1627-1634,共8页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(61876131)。
关键词
边缘计算
深度强化学习
计算卸载
软件定义网络
edge computing
deep reinforcement learning
computation offloading
software defined networking
作者简介
李国燕(1984-),女,河北石家庄人,副教授,博士,研究方向:下一代网络技术、车联网,E-mail:ligy@tcu.edu.cn;薛翔(1997-),男,河南安阳人,硕士研究生,研究方向:边缘计算,E-mail:xuexiangtcj@163.com;刘毅(1969-),男,辽宁锦州人,教授,博士,研究方向:智能信息处理,E-mail:lgliuyi@163.com;潘玉恒(1978-),女,山东龙口人,副教授,博士,研究方向:信号处理,E-mail:yuheng0616@sina.com。
