移动边缘计算卸载技术综述

doi:10.11959/j.issn.1000-436x.2018215

Abstract

Abstract:

Computation offloading in mobile edge computing would transfer the resource intensive computational tasks to the edge network.It can not only solve the shortage of mobile user equipment in resource storage,computation performance and energy efficiency,but also deal with the problem of resource occupation,high latency and network load compared to cloud computing.Firstly the architecture of MEC was introduce and a comparative analysis was made according to various deployment schemes.Then the key technologies of computation offloading was studied from three aspects of decision on computation offloading,allocation of computing resource within MEC and system implement of MEC.Based on the analysis of MEC deployment scheme in 5G,two optimization schemes on computation offloading was proposed in 5G MEC.Finally,the current challenges in the mobility management was summarized,interference management and security of computation offloading in MEC.

Key words: MEC, MEC deployment scheme, computation offloading, decision on computation offloading, resource allocation, computation offloading system

CLC Number:

TP393

Renchao XIE,Xiaofei LIAN,Qingmin JIA,Tao HUANG,Yunjie LIU. Survey on computation offloading in mobile edge computing[J]. Journal on Communications, 2018, 39(11): 138-155.

Figures/Tables 15

References 67

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方案	服务器部署位置	控制实体	控制实体部署位置	部署方式	优缺点
小基站云(SCC)	SCeNBs	SCM	1) 部署在靠近 RAN 或作为MME的扩展部署在CN 2) 分层式部署本地 SCM （L-SCM）和位于 CN 的SCM（R-SCM）	分布式部署	优点：靠近网络边缘，有较低的时延缺点：1）安装成本高；2）在边缘部署会引入鉴权和认证及安全等问题
移动微型云(MCC)	eNodes	无	—	分布式部署	优点：1）靠近网络边缘，减小终端时延；2）MMC服务器互连，在VM迁移时能保证业务连续性缺点：1）无集中式控制实体会增大信令开销；2）边缘部署会引入鉴权和认证以及安全问题
快速移动私人云(FMPC)	接近RAN的运营商云（cloud）	MC	在SDN传输网络中分布式部署	分布式部署	优点：1）有较低时延；2)引入SDN，信令开销小；3）减小基站压力缺点：引入鉴权和认证及安全问题
漫游云(FMC)	CN侧	FMCC	在分布式CN后以集中方式部署	分布式部署	优点：网络接入的鉴权认证和安全问题得到解决缺点：1)有相对较高的时延；2）占用核心网资源
CONCERT	eNode B或者CN	Conductor	在控制平面以集中或分层方式部署	分层式部署	优点：1）分层地放置资源可以灵活和弹性地管理网络和云服务；2）能更好地实现负载均衡

优化目标	卸载类型	相关文献	关键研究点
			?以降低时延为目标的卸载决策方案
			?分析时延模型
以降低时延为目标	全部卸载	文献[24,25,26]	?多用户的计算卸载问题，建模为NP-hard 问题
			?基于Lyapunov优化的动态卸载算法
			?联合优化通信资源和计算资源分配
			?采用Stackelberg博弈论的方法优化多用户卸载方案
			?研究卸载内容之间的依赖关系
	部分卸载	文献[27,28]	?与计算任务全部卸载的时延相对比
			?采用启发式算法
			?在保证时延的要求下，以降低能耗为目标的卸载决策方案
			?能量模型的优化方案
以降低能量消耗为目标	全部卸载	文献[29,30,31]	?相关参数的对比分析，如信道链路状况、CPU、系统容量等
			?在线学习方案和预先计算的离线策略
			?采用人工鱼群算法、约束性马尔可夫链方法
			?研究卸载内容之间的依赖关系
	部分卸载	文献[32,33]	?与计算任务全部卸载的能耗相对比
			?基于阈值结构的最优卸载方案
			?采用凸优化的方法
			?以权衡能量和时延为目标的卸载决策方案
权衡能耗和时延为目标	全部卸载	文献[35,36,37]	?能量和时延的权衡模型建立y 采用基于Lyapunov的优化算法、在线学习策略
权衡能耗和时延为目标	部分卸载	文献[34]	?研究卸载内容之间的依赖关系
			?与计算任务全部卸载的时延和能耗均衡方案对比

节点数量	优化目标	参考文献	计算卸载方案	仿真优化结果
		文献[38]	通过动态考虑整个迁移过程来最优化VM分配方案	减少46%的时延以及减小80%的迁移成本
	时延	文献[39]	提出了MEC在满足应用程序时延要求的同时，提供服务的应用程序数量最大化为目标的卸载方案，通过优先级来分配计算节点	减小25%的时延
单节点		文献[26]	考虑了MEC的计算资源有限的情况，如何进行资源分配的问题。提出了分层的MEC部署架构	—
	时延和能耗	文献[40]	通过分配索引策略来让UE选择合适的MEC服务器，解决了高复杂度和高通信开销问题	索引策略和普通策略相比减小了7%的能耗
	能耗	文献[43]	提出了合作式缓存和卸载方案，MEC服务器联合起来为UE执行计算和缓存任务	—
		文献[44]	采用深度学习的方法分配资源，提出了动态的卸载方案	减小了50%的能耗
	时延	文献[45]	多用户卸载，将问题建模为 NP-hard 问题，并采用背包模型去优化整个资源分配和负载均衡的问题	相比于全部在 UE 执行任务减少了70%的延时，比在CC执行减小了58%的时延
多节点		文献[46]	提出了干扰管理的方案，在最小化干扰的条件下进行通信资源分配、计算资源分配的方案	减小了40%的时延
多节点	能耗和时延	文献[41]	提出了通过优化时延和基站能耗的集群选择策略，并对比分析不同的回传技术和网络拓扑的影响	—
		文献[42]	提出 3 种不同的云集群选择策略，分别以优化时延、优化集群总能耗和优化集群中每个SCeNB的能耗为目标	减少了22%的时延，降低了61%的能耗

Survey on computation offloading in mobile edge computing

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