面向移动边缘计算的联合计算卸载和资源分配策略研究

黄冬晴 俞黎阳 陈珏 魏同权

摘要：随着无人驾驶、在线游戏、虚拟现实等低延迟应用的大量涌现，传统集中式的移动云计算范式越来越难以满足此类用户服务质量的需求.为弥补云计算的不足，移动边缘计算应运而生.移动边缘计算通过计算卸载，将计算任务迁移到网络边缘服务器来为用户提供计算和存储资源.然而，现有大部分工作仅考虑了延迟或能耗的单目标性能优化，未考虑延迟和能耗的均衡优化.为减少任务延迟和设备能耗，提出了一种面向多用户的联合计算卸载和资源分配策略.该策略首先利用拉格朗日乘子法获得给定卸载决策的最佳计算资源分配;然后，提出一个基于贪心算法的计算卸载算法获得最佳卸载决策;最后，通过不断迭代得到最终解.实验结果表明，与基准算法相比，所提算法最高可以降低40%的系统成本.

关键词：移动边缘计算;计算卸载;资源分配;拉格朗日乘子法;贪心算法

中图分类号：TP391文献标志码：ADOI：10.3969/j.issn.l000-5641.2021.06.010

Research on joint computation offloading and resource allocation strategy for mobile edge computing

HUANG Dongqing1，YU Liyang1，CHEN Jue2，WEI Tongquan1

（1. School of Computer Science and Technology. East China Normal University，Shanghai 200062. China;

2. School of Electronic and Electrical Engineering. Shanghai University of Engineering Science，Shanghai 201620，China）

Abstract：With the emergence of low-latency applications such as driverless cars，online gaming，and virtual reality，it is becoming increasingly difficult to meet users demands for service quality using the traditional centralized mobile cloud computing model. In order to make up for the shortages of cloud computing，mobile edge computing came into being，which provides users with computing and storage resources by migrating computing tasks to network edge servers through computation offloading. However，most of the existing work processes only consider single-objective performance optimization of delay or energy consumption，and do not consider the balanced optimization of delay and energy consumption. Therefore，in order to reduce task delay and equipment energy consumption，a multi-user joint computation offloading and resource allocation strategy is proposed. In this strategy，the Lagrange multiplier method is used to obtain the optimal allocation of computing resources for a given offloading decision. Then，a computation offloading algorithm based on a greedy algorithm is proposed to obtain the optimal offloading decision：thefinal solution is obtained through continuous iteration. Experimental results show that，compared with the benchmark algorithm，the proposed algorithm can reduce system costs by up to 40%.

Keywords：mobile edge computing;computation offloading;resource allocation;Lagrange multiplier method;greedy algorithm

0引言

隨着移动互联网和5G通信技术[1]的不断发展，移动设备上需要处理越来越多的延迟敏感性应用，例如无人驾驶、在线游戏、虚拟现实和增强现实等[2].但是，由于计算和存储资源的限制，移动设备运行这些应用会造成很高的延迟和能耗.为解决该问题，移动云计算（Mobile Cloud Computing，MCC）诞生了，它将用户部分计算任务通过上行链路卸载到云端服务器，减少了任务的执行时间.然而在传统MCC中，集中式部署的云服务器与……