基于边缘云和移动辅助设备的计算卸载优化方案  

OPTIMAL SCHEME OF COMPUTATION OFFLOADING BASED ON EDGE CLOUD AND MOBILE AUXILIARY EQUIPMENT

作　　者：方加娟[1,2] 李凯[1] Fang Jiajuan;Li Kai(Department of Software Engineering,Zhengzhou Technical College,Zhengzhou 450121,Henan,China;College of Computer Science and Engineering,Nanjing University of Science and Technology,Nanjing 210094,Jiangsu,China)

机构地区：[1]郑州职业技术学院软件工程系,河南郑州450121 [2]南京理工大学计算机科学与工程学院,江苏南京210094

出　　处：《计算机应用与软件》2020年第12期6-12,共7页Computer Applications and Software

基　　金：河南省社科联、河南省经团联2015年度调研课题(SKL-2015-616);河南省社科联、河南省经团联2016年度调研课题(SKL-2016-2663)。

摘　　要：针对移动边缘云和移动辅助设备联合计算卸载和资源分配的优化问题,提出一个联合计算卸载方案。计算用户设备(Computing User Equipment,CUE)通过为辅助用户设备(Helper User Equipment,HUE)提供带宽激励的方式,将计算卸载到边缘云和辅助用户设备,并通过优化本地、辅助用户设备和边缘云中的资源分配,实现对带宽和计算资源及时间的实际约束的最小加权能耗。为了解决非凸优化问题,采用几何规划(Geometric Programming,GP)和瓶颈匹配(Bottleneck Matching,BM)方法求近似凸问题的最优解。数值研究表明,与云卸载策略相比,在卸载延迟没有明显增加的情况下,该方案的总计算时间可以减少35%~40%。To solve the optimization problem of joint computation offloading and resource allocation for mobile edge cloud and mobile auxiliary equipment,this paper proposes a joint computation offloading scheme.The computing user equipment(CUE)offloaded computing to the edge cloud and the auxiliary user equipment by providing bandwidth incentives for the helper user equipment(HUE).The method aimed to optimize the computation load assignments to local processors in the mobile users,mobile peers and the edge cloud jointly with the resource allocation to achieve the minimum weighted energy consumption subject to practical constraints on the bandwidth and computing resources and time.In order to solve the non-convex optimization problem,geometric programming(GP)and bottleneck matching(BM)method were used to solve the optimal solution of the approximate convex problem.Numerical studies show that the total computation time can be reduced by 35%~40%compared with the only cloud offloading strategy without a significant increase in the offload delay time.

关 键 词：移动边缘计算 计算卸载 资源分配 优化 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]