摘要
为降低抽水蓄能电站中终端设备密集计算型任务的处理时延,针对抽水蓄能电站的物联网体系,提出了一种基于边缘计算的任务卸载算法.在该文方案中,首先基于层次分析法对计算任务进行优先级划分,并以终端能耗为约束、以终端计算任务处理时延为优化目标建立卸载模型,其次基于Q学习算法(Q-Learning,QL)探索系统的状态转移信息,以获取终端设备与边缘节点间的最佳卸载策略.另外,采用深度学习(Deep Learning,DL)的方法映射状态与动作之间的关系,避免算法迭代求解过程中的维度爆炸问题.仿真结果表明,本文提出的方法有效降低了抽水蓄能电站的任务平均执行时延,能够大幅提高抽水蓄能电站的生产作业及安全监测等工作的执行效率.
To reduce the delay of processing intensive computing tasks by terminal equipment in pumped storage power stations,this paper proposes a task offloading algorithm based on edge computing for the Internet of Things(IoT)system of pumped storage power stations.Firstly,the computing tasks are prioritized based on the analytic hierarchy process,and an offloading model is built with the terminal energy consumption as the constraint and the processing delay of terminal computing tasks as the optimization objective.Secondly,the Q-Learning(QL)algorithm is adopted to collect the state transition information,in the hope to obtain the best offloading strategy between the terminal device and the edge node.Finally,Deep Learning(DL)is used to map the relationship between states and actions to avoid a dimensional explosion in the iterative solution of the algorithm.The simulation results show that the proposed method greatly reduces the average delay of computing tasks and can greatly improve the execution efficiency of production operations and safety monitoring associated with pumped storage power stations.
作者
崔运进
江帆
黄建德
阎峻
赵锋
CUI Yun-Jin;JIANG Fan;HUANG Jian-De;YAN Jun;ZHAO Feng(East China Tongbai Pumped Storage Power Generation Co.Ltd.,Hangzhou 310003,China;State Grid Xinyuan Holdings Ltd.,Beijing 100761,China)
出处
《计算机系统应用》
2021年第8期225-231,共7页
Computer Systems & Applications
关键词
抽水蓄能电站
物联网
边缘计算
深度Q网络
任务卸载
pumped storage power station
Internet of Things(IoT)
edge computing
deep Q network
task offloading
