云辅助移动边缘计算中的计算卸载策略  

Computation Offloading Strategy in Cloud-Assisted Mobile Edge Computing

作　　者：王妍 葛海波 冯安琪 WANG Yan;GE Haibo;FENG Anqi(School of Electronic Engineering,Xi’an University of Posts and Telecommunications,Xi’an 710121,China)

机构地区：[1]西安邮电大学电子工程学院,西安710121

出　　处：《计算机工程》2020年第8期27-34,共8页Computer Engineering

基　　金：陕西省自然科学基金(2011JM8038);陕西省重点产业创新链(群)项目(S2019-YF-ZDCXL-ZDLGY-0098)。

摘　　要：移动边缘计算通过将计算资源迁移至网络边缘来降低时延并缩减能耗,相比云计算,边缘计算的计算资源有限,不能满足所有移动服务的需求。针对上述问题,提出一种云辅助移动边缘计算的计算卸载策略。将移动服务建模为具有优先约束关系的工作流模型来分析系统运行过程中的时延和能耗,并以系统总代价(时延和能耗的加权和)最小化为研究目标,将遗传算法作为基础算法,通过改进传统遗传算法的编码、交叉、变异等操作,设计基于改进遗传算法的计算卸载算法。仿真结果表明,与All-Local算法、Random算法和ECGA算法相比,该算法的系统总代价最小。Mobile Edge Computing(MEC)reduces delay and energy consumption by migrating computing resources to network edge.Compared with cloud computing,edge computing has limited computing resources and cannot meet the needs of all mobile services.To address the problems,this paper proposes a computation offloading strategy for cloud-assisted mobile edge computing.The mobile service is modeled as a workflow model with a priority constraint relationship to analyze the delay and energy consumption during system operation.Then,with minimizing the total system cost(weighted sum of delay and energy consumption)as research objective,a computation offloading algorithm is designed on the basis of improved Genetic Algorithm(GA),of which the operations of coding,crossover,and mutation are partially modified.Simulation results show that compared with the All-Local algorithm,the Random algorithm,the ECGA algorithm,the total system cost of the proposed algorithm is the smallest of existing algorithms.

关 键 词：移动边缘计算 云计算 计算卸载 遗传算法 工作流 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]