计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 286-290.doi: 10.11896/jsjkx.200200028

• 计算机网络 • 上一篇    下一篇

一种面向5G网络的移动边缘计算卸载策略

田贤忠, 姚超, 赵晨, 丁军   

  1. 浙江工业大学计算机科学与技术学院 杭州 310023
  • 出版日期:2020-11-15 发布日期:2020-11-17
  • 通讯作者: 田贤忠(txz@zjut.edu.cn)
  • 基金资助:
    国家自然科学基金(61672465,61772472);浙江省自然科学基金(LY15F020027,LY17F020020)

5G Network-oriented Mobile Edge Computation Offloading Strategy

TIAN Xian-zhong, YAO Chao, ZHAO Chen, DING Jun   

  1. College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China
  • Online:2020-11-15 Published:2020-11-17
  • About author:TIAN Xian-zhong,born in 1968,Ph.D,professor,is a member of China Computer Federation.His main research interests include energy harvesting wireless sensor network,network coding,mobile edge computing and optimization protocol in wireless sensor networks.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61672465,61772472) and Natural Science Foundation of Zhejiang Province,China (LY15F020027,LY17F020020).

摘要: 移动边缘计算(Mobile Edge Computing,MEC)技术是当前无线传感器网络的重要研究方向之一。MEC技术能将无线传感器设备的本地计算任务卸载到边缘云服务器进行计算,从而大大提高了无线传感器网络的计算能力。但是无线网络中大量设备同时进行计算卸载会导致信号干扰和边缘云服务器的计算负载过大。为了提高无线网络的计算质量,首先提出了一种最小化多个无线传感器设备的 MEC 系统计算时间周期的合理时间分配和计算卸载的策略,并采用了5G非正交多址接入和串行干扰删除技术使多个无线设备可以利用相同的子载波同时进行计算卸载,从而提高计算卸载的效率;然后建立了无线设备能量捕获和任务计算的相关模型,将上述模型和策略建模为一个优化问题进行求解;最后通过数值分析实验验证了所提策略的有效性。

关键词: 串行干扰删除, 非正交多址接入, 计算卸载, 射频能量捕获, 移动边缘计算

Abstract: Mobile edge computing (MEC) technology is one of the important research directions of current wireless sensor networks.MEC technology can offload local computing tasks of wireless sensor devices to the edge cloud server for computing,thereby greatly improve the computing capacity of wireless sensor networks.However,a large number of devices in the wireless network perform computation offload at the same time,which will cause signal interference and excessive computational load on the edge cloud server.First,in order to improve the computation quality of wireless networks,a reasonable time allocation and computation offloading strategy for minimizing the computing time period of a MEC system with multiple wireless sensor devices is proposed,and 5G non-orthogonal multiple access and successive interference cancellation technology enables multiple wireless devices to perform computation offloading at the same time using the same subcarrier,there by improving the efficiency of computation offloading.Then the related models of wireless device energy harvesting and task computing are established,which are modeled as an optimization problem according to the above models and strategies,and the problem is solved.Finally,the effectiveness of the proposed strategy is verified by numerical analysis experiments.

Key words: Computation offloading, Mobile edge computing, Non-orthogonal multiple access, Radio frequency energy harvesting, Serial interference cancellation

中图分类号: 

  • TN929.5
[1] ALAMEDDINE H A,SHARAFEDDINE S,SEBBAH S,et al.Dynamic Task Offloading and Scheduling for Low-Latency IoT Services in Multi-Access Edge Computing[J].IEEE Journal on Selected Areas in Communications,2019,37(3):668-682.
[2] KWAKJ,KIM Y,LEE J,et al.DREAM:Dynamic Resource and Task Allocation for Energy Minimization in Mobile Cloud Systems[J].IEEE Journal on Selected Areas in Communications,2015,33(12):2510-2523.
[3] ABBASN,ZHANG Y,TAHERKORDI A,et al.Mobile EdgeComputing:A Survey[J].IEEE Internet of Things Journal,2017,PP(99):1-1.
[4] XIE L,SHI Y,HOU Y T,et al.Wireless power transfer and applications to sensor networks[J].IEEE Wireless Communications,2013,20(4):140-145.
[5] SENNURULUKUS,YENER A,ERKIP E,et al.Energy Har-vesting Wireless Communications:A Review of Recent Advances[J].IEEE Journal on Selected Areas in Communications,2015,33(3):360-381.
[6] YOU C,HUANG K,CHAE H.Energy Efficient Mobile Cloud Computing Powered by Wireless Energy Transfer[J].IEEE Journal on Selected Areas in Communications,2016,34(5):1757-1771.
[7] BIS,ZHANG Y J A.Computation Rate Maximization for Wireless Powered Mobile-Edge Computing with Binary Computation Offloading[J].IEEE Transactions on Wireless Communications,2017,17(6):4177-4190.
[8] WANG F,XU J,WANG X,et al.Joint Offloading and Computing Optimization in Wireless Powered Mobile-Edge Computing Systems[J].IEEE Transactions on Wireless Communications,2018,PP(99):1-1.
[9] YOUC,HUANG K,CHAE H,et al.Energy-Efficient Resource Allocation for Mobile-Edge Computation Offloading[J].IEEE Transactions on Wireless Communications,2017,16(3):1397-1411.
[10] DING Z,LEI X,KARAGIANNIDIS G K,et al.A Survey onNon-Orthogonal Multiple Access for 5G Networks:Research Challenges and Future Trends[J].IEEE Journal on Selected Areas in Communications,2017,35(10):2181-2195.
[11] DING Z,FAN P,POOR H V.Impact of Non-orthogonal Multiple Access on the Offloading of Mobile Edge Computing[J].IEEE Transactions on Communications,2019,67(1):375-390.
[12] WANG F,ZHANG X.Dynamic interface-selection and resource allocation over heterogeneous mobile edge-computing wireless networks with energy harvesting[C]// IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).IEEE,2018.
[13] BOYD S,VANDENBERGHE L.Convex Optimization[M].Cambridge University Press,2004.
[1] 孙慧婷, 范艳芳, 马孟晓, 陈若愚, 蔡英.
VEC中基于动态定价的车辆协同计算卸载方案
Dynamic Pricing-based Vehicle Collaborative Computation Offloading Scheme in VEC
计算机科学, 2022, 49(9): 242-248. https://doi.org/10.11896/jsjkx.210700166
[2] 于滨, 李学华, 潘春雨, 李娜.
基于深度强化学习的边云协同资源分配算法
Edge-Cloud Collaborative Resource Allocation Algorithm Based on Deep Reinforcement Learning
计算机科学, 2022, 49(7): 248-253. https://doi.org/10.11896/jsjkx.210400219
[3] 张翀宇, 陈彦明, 李炜.
边缘计算中面向数据流的实时任务调度算法
Task Offloading Online Algorithm for Data Stream Edge Computing
计算机科学, 2022, 49(7): 263-270. https://doi.org/10.11896/jsjkx.210300195
[4] 李梦菲, 毛莺池, 屠子健, 王瑄, 徐淑芳.
基于深度确定性策略梯度的服务器可靠性任务卸载策略
Server-reliability Task Offloading Strategy Based on Deep Deterministic Policy Gradient
计算机科学, 2022, 49(7): 271-279. https://doi.org/10.11896/jsjkx.210600040
[5] 方韬, 杨旸, 陈佳馨.
D2D辅助移动边缘计算下的卸载策略优化
Optimization of Offloading Decisions in D2D-assisted MEC Networks
计算机科学, 2022, 49(6A): 601-605. https://doi.org/10.11896/jsjkx.210200114
[6] 刘漳辉, 郑鸿强, 张建山, 陈哲毅.
多无人机使能移动边缘计算系统中的计算卸载与部署优化
Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems
计算机科学, 2022, 49(6A): 619-627. https://doi.org/10.11896/jsjkx.210600165
[7] 谢万城, 李斌, 代玥玥.
空中智能反射面辅助边缘计算中基于PPO的任务卸载方案
PPO Based Task Offloading Scheme in Aerial Reconfigurable Intelligent Surface-assisted Edge Computing
计算机科学, 2022, 49(6): 3-11. https://doi.org/10.11896/jsjkx.220100249
[8] 周天清, 岳亚莉.
超密集物联网络中多任务多步计算卸载算法研究
Multi-Task and Multi-Step Computation Offloading in Ultra-dense IoT Networks
计算机科学, 2022, 49(6): 12-18. https://doi.org/10.11896/jsjkx.211200147
[9] 沈家芳, 钱丽萍, 杨超.
面向集能型中继窄带物联网的非正交多址接入和多维网络资源优化
Non-orthogonal Multiple Access and Multi-dimension Resource Optimization in EH Relay NB-IoT Networks
计算机科学, 2022, 49(5): 279-286. https://doi.org/10.11896/jsjkx.210400239
[10] 彭冬阳, 王睿, 胡谷雨, 祖家琛, 王田丰.
视频缓存策略中QoE和能量效率的公平联合优化
Fair Joint Optimization of QoE and Energy Efficiency in Caching Strategy for Videos
计算机科学, 2022, 49(4): 312-320. https://doi.org/10.11896/jsjkx.210800027
[11] 张海波, 张益峰, 刘开健.
基于NOMA-MEC的车联网任务卸载、迁移与缓存策略
Task Offloading,Migration and Caching Strategy in Internet of Vehicles Based on NOMA-MEC
计算机科学, 2022, 49(2): 304-311. https://doi.org/10.11896/jsjkx.210100157
[12] 梁俊斌, 张海涵, 蒋婵, 王天舒.
移动边缘计算中基于深度强化学习的任务卸载研究进展
Research Progress of Task Offloading Based on Deep Reinforcement Learning in Mobile Edge Computing
计算机科学, 2021, 48(7): 316-323. https://doi.org/10.11896/jsjkx.200800095
[13] 薛艳芬, 高继梅, 范贵生, 虞慧群, 许亚杰.
边缘计算中基于能耗感知的容错协同任务执行算法
Energy-aware Fault-tolerant Collaborative Task Execution Algorithm in Edge Computing
计算机科学, 2021, 48(6A): 374-382. https://doi.org/10.11896/jsjkx.200900027
[14] 宋海宁, 焦健, 刘永.
高速公路中的移动边缘计算研究
Research on Mobile Edge Computing in Expressway
计算机科学, 2021, 48(6A): 383-386. https://doi.org/10.11896/jsjkx.200900212
[15] 陈勇, 许奇, 王小明, 高金玉, 申瑞娟.
基于多天线NOMA的通信系统高能效功率分配方法
Energy Efficient Power Allocation for MIMO-NOMA Communication Systems
计算机科学, 2021, 48(6A): 398-403. https://doi.org/10.11896/jsjkx.200900175
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!