边缘计算迁移研究综述

doi:10.11959/j.issn.1000-0801.2019059

Abstract

Abstract:

Edge computing integrate the computing,storage and other resources on the edge of the network into a unified user service platform.According to the principle of nearest service,the network edge node task request is timely and correspondingly processed.Computational migration is a key issue in edge computing due to limited edge node capabilities,resources,bandwidth,and energy.The causes,evolution and development trends of mobile edge computing were reviewed from the distributed computing,pervasive computing,cloud computing to edge computing as the main line.The characteristics of computing migration at each stage were compared and analyzed,and the classical models were reviewed.The latest research progress and application fields of edge computing were focused on,mobile edge computing models based on energy optimization management were researched and compared.Finally,a time division multiple access based multi-user edge mobile terminal computing migration strategy system for LTE applications was proposed.

Key words: edge computing, computation offloading, IoT, cloud computing, mobile terminal

CLC Number:

TP393

Youkang ZHU,Guangxue YUE,Xiaohui YANG,Jiansheng LIU. A survey on edge computing offloading[J]. Telecommunications Science, 2019, 35(4): 74-94.

Figures/Tables 23

References 70

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发展阶段	时间	研究动机	研究内容	实现方法
分布式计算	1995—2000年	便携式计算机的性能/能耗	迁移可行性研究	开发人员手动划分
普适计算	2001—2008年	智能手机性能/能耗	迁移系统架构	基于计算代理
云计算	2008—2016年	智能移动终端功能增强	基于云的计算迁移系统实现	基于云服务器
边缘计算	2016年至今	智能移动终端、资源互联共享	基于移动设备的计算迁移	基于WLAN/蓝牙的终端互联

框架名称	架构设计	存储	通信	任务执行过程
Hadoop^[9]	TraskTacker JobTracker/	HDFS	RPC/HTTP	MapReduce Hadoop把一个大的任务划分为多个小的计算任务并分配给集群的每个计算节点，跟踪每个计算节点的进度以决定是否重新执行，最后收集每个节点上的计算结果并输出
Storm^[10]	Nimbus/supervisor	实时的输入流	zeroMQ消息队列	向客户端提交一个声明好的拓扑，Nimbus 通过与 Zookeeper 交互获取适合的运行机器，把任务分配到具体的机器，机器上的supervisor根据分配到的任务启动相应进程。期间，无论是supervisor还是worker都与Zookeeper保持心跳联系
Spark^[11]	master/workers	内存、磁盘	共享、广播变量	用户自己开发的程序以 driver的方式连接 master，并指定数据集 RDD的生成与转换，将RDD的操作发送至任务执行节点workers。workers即执行具体任务也存储计算所需数据，workers 收到操作定义对本地化数据进行操作，生成预期结果，对结果返回或者存储

所处时期	特点
集群化时期	将分散的资源组合成了规模化的数据中心。这样的集中化时期也产生了不同的问题：数据传输中断、数据中心业务崩塌带来的数据中断等
网格计算时期	利用互联网把地理上广泛分布的各种资源连成一个逻辑的整体，就像一台超级计算机
虚拟化时期	将多台服务器整合，屏蔽了底层设备的差异之处转向统一提供处理能力，实现了物理服务器资源利用率的提升。在虚拟化技术出现之前，软件只能被绑定在静态硬件环境中，而虚拟化则打断了这种软硬件之间的依赖性
云计算时期	进入可配置的计算机资源共享池，这些资源能够被快速提供，只需投入很少的管理工作，或与服务供应商进行很少的交互。按需自助服务，网络接入无处不在，与位置无关的资源池，快速弹性，按使用付费

实现方法	迁移粒度	特点	缺点	目标	适用场景
cloudlet^[27]	VM	支持以VM为粒度的应用划分，对系统进行设计和实现，从而使系统更灵活，应用性能也更高	安全性一般，且不支持对已有应用进行划分	响应时间	针对网络时延敏感的应用
Surrogate^[28]	进程	支持自适应计算迁移，并基于 Android 为开发人员提供应用开发环境，能够根据移动终端周围的环境自动地进行计算迁移和并行化决策	安全性差	性能	针对网络时延敏感的应用
CloneCloud^[29]	VM	自动转换所有运行在应用VM上的移动应用，完全不需要开发人员的参与	需要针对各种执行环境预先建立划分数据库	执行时间/能耗	针对网络时延不敏感或需要并行处理的应用

评价指标	云计算	边缘计算
部署方式	集中式	分布式
与用户的距离	远	近
时延	高	低
抖动	大	小
计算能力	强	有限
存储能力	强	有限

A survey on edge computing offloading

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研究成果	发表时间	实现方法	设计目标
物联网传感数据异常实时检测^[44]	2018年	建立基于边缘计算的分布式数据异常检测模型	提高物联网实时采集数据的质量
数据转发^[45]	2018年	物联网数据转发模型	提升物联网数据转发性能
CloudAware^[43]	2016年	优化迁移策略	加速计算、节能
Replisom^[37]	2016年	克隆虚拟机、压缩	减少响应时间
Femtoclouds^[36]	2015年	优化迁移策略	增强计算能力
ME-VoLTE^[38]	2015年	优化传输	降低能耗
EAB^[40]	2015年	优化传输	加速资源访问
上下文感知协作实时应用^[41]	2015年	上下文感知	在实时场景中降低时延
端到端计算迁移^[39]	2014年	优化迁移策略	提高整个网络资源利用效率

作者	发表时间	优化目标	创新点	存在不足
Zhang等^[46]	2013年	能耗	比较本地计算和迁移计算的能量消耗，得出最优迁移策略	基于单用户 MECO 系统，仅对比了迁移前后的能量消耗
Zhang等^[47]	2016年	传输代价	以无线能量传输为目标,设计了基于无缝集成移动云计算的解决方案	研究了低复杂度设备（如传感器），对于高计算复杂度的智能终端缺乏研究
Mao等^[55]	2016年	传输代价	以无线能量采集为目标，设计出了基于微波功率传输的解决方案	基于 CPU 频率和发射功率的优化，未综合考虑计算任务
Xiang等^[56]	2014年	算法优化	将动态迁移与自适应的 LTE/Wi-Fi 链接选择进行集成，提出了一种可扩展的近似动态规划算法	基于单用户的动态算法设计优化，缺乏全局最优考量
Stefania等^[49]	2015年	能耗	将无线和计算资源进行联合优化	缺乏严密的计算时延考量
Zhao等^[50]	2015年	负载均衡	研究了迁移到不同云的最优用户调度问题，提出了一个基于任务负载的阈值迁移策略	研究时延问题，未结合终端能耗研究
Chen等^[57]	2015年	节点负载	研究了多用户分布式计算迁移，运用博弈论来实现能量和时延的最小化	分布式的计算迁移算法得到局部最优
Zhang等^[58]	2018年	算法优化	基于马尔可夫决策和动态规划算法建立最优策略	计算复杂度较高
Reza等^[60]	2018年	隐私安全	使用智能分区和动态迁移研究了迁移计算中的用户隐私安全问题	未考虑富应用程序复杂性
Liu^[53]	2018年	时延	基于启发式搜索、重构线性化技术和半定松弛3种算法来实现时延和可靠性的权衡	缺乏对能耗的优化

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