Telecommunications Science ›› 2019, Vol. 35 ›› Issue (4): 74-94.doi: 10.11959/j.issn.1000-0801.2019059
Special Issue: 边缘计算
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Youkang ZHU1,Guangxue YUE1,2(),Xiaohui YANG1,Jiansheng LIU1
Revised:
2019-03-10
Online:
2019-04-20
Published:
2019-04-25
Supported by:
CLC Number:
Youkang ZHU,Guangxue YUE,Xiaohui YANG,Jiansheng LIU. A survey on edge computing offloading[J]. Telecommunications Science, 2019, 35(4): 74-94.
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框架名称 | 架构设计 | 存储 | 通信 | 任务执行过程 |
Hadoop[ | 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[ | 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[ | 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 |
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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 |
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实现方法 | 迁移粒度 | 特点 | 缺点 | 目标 | 适用场景 |
cloudlet[ | 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[ | 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[ | 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 |
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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[ | 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[ | 2018年 | 物联网数据转发模型 | 提升物联网数据转发性能 |
CloudAware[ | 2016年 | 优化迁移策略 | 加速计算、节能 |
Replisom[ | 2016年 | 克隆虚拟机、压缩 | 减少响应时间 |
Femtoclouds[ | 2015年 | 优化迁移策略 | 增强计算能力 |
ME-VoLTE[ | 2015年 | 优化传输 | 降低能耗 |
EAB[ | 2015年 | 优化传输 | 加速资源访问 |
上下文感知协作实时应用[ | 2015年 | 上下文感知 | 在实时场景中降低时延 |
端到端计算迁移[ | 2014年 | 优化迁移策略 | 提高整个网络资源利用效率 |
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作者 | 发表时间 | 优化目标 | 创新点 | 存在不足 |
Zhang等[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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[ | 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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