基于Q-learning的工业互联网资源优化调度  

Optimization of Resource Allocation for Industrial Internet Based on Q-learning

作　　者：张延华[1,2] 杨乐 李萌[1,2] 吴文君 杨睿哲[1,2] 司鹏搏 ZHANG Yanhua;YANG Le;LI Meng;WU Wenjun;YANG Ruizhe;SI Pengbo(Beijing Laboratory of Advanced Information Networks,Beijing University of Technology,Beijing 100124,China;Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China)

机构地区：[1]北京工业大学先进信息网络北京实验室,北京100124 [2]北京工业大学信息学部,北京100124

出　　处：《北京工业大学学报》2020年第11期1213-1221,共9页Journal of Beijing University of Technology

基　　金：国家自然科学基金资助项目(61901011,61671029);中国博士后科学基金资助项目(2018M640032);北京市博士后科研活动经费资助项目(ZZ2019-73);国家自然科学基金民航联合研究基金资助项目(U1633115)。

摘　　要：面对5G与工业互联网中日益增长的数据传输与计算需求,移动边缘计算已逐渐成为一种新兴的解决方法,可有效应对工业互联网设备自身计算能力的不足,并充分缓解网络拥塞等问题.然而,当数量庞大的设备同时发送计算请求时,往往会超出边缘计算服务器的计算负载.此外,工业互联网设备通常仅装配有限的能量供给,无法承受能源消耗过多的任务,且庞大的设备数量还决定了网络连接、数据计算等系统开销.因此,面向工业互联网场景中机器类型通信设备的计算任务卸载问题,提出一种基于Q-learning的计算任务卸载决策方法,综合考虑任务卸载过程中的网络环境和服务器状态,并联合优化卸载过程产生的时延、能耗和经济开销.仿真结果表明,所提优化框架可有效减少计算任务卸载系统的时延、能耗和经济的总开销.Faced with the increasing requirements of data transmission and computing in 5G and industrial Internet,mobile edge computing(MEC)has gradually become a novel methodology,which can effectively deal with the shortage of computing capacity of industrial Internet devices and alleviate network congestion.However,when many devices send computing requests at the same time,the computing load of the edge computing server is always exceeded.In addition,industrial Internet devices are usually equipped with limited battery,unable to execute the task with excessive energy consumption,and the huge number of devices also determine the network connection,data computing and other system overhead.Therefore,a computation offloading decision method of machine-type communication devices based on Q-learning in the industrial Internet scenario in this paper was proposed.The network environment and server state in the process of computation offloading was considered,and the delay,energy consumption and economic overhead caused by the process of offloading were jointly optimized.The simulation results demonstrate that the proposed scheme can effectively reduce total overhead of the delay,energy consumption and economy in the computing task offloading system.

关 键 词：资源优化 计算任务卸载 工业互联网 移动边缘计算 Q-LEARNING 机器类型通信设备 

分 类 号：TN915[电子电信—通信与信息系统]