Energy-efficient cooperative offloading for mobile edge computing

被引:1
作者
Shi, Wenjun [1 ]
Wu, Jigang [2 ]
Chen, Long [2 ]
Zhang, Xinxiang [2 ]
Wu, Huaiguang [1 ]
机构
[1] Zhengzhou Univ Light Ind, Sch Comp & Commun Engn, Zhengzhou 450002, Peoples R China
[2] Guangdong Univ Technol, Sch Comp Sci & Technol, Guangzhou 510006, Peoples R China
基金
中国国家自然科学基金;
关键词
Mobile edge computing; Task offloading; Cooperation; Energy efficiency; Forwarding method; COMPUTATION; OPTIMIZATION; ALLOCATION;
D O I
10.1007/s11276-023-03311-x
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Mobile edge computing (MEC) has emerged as a promising paradigm to improve the energy efficiency for latency-constraint computation. This paper proposes a novel user cooperation approach in both computation and communication for MEC, based on the three-node cooperative offloading architecture, which consists of two mobile users and a computing access point (CAP). The mobile application tasks can be executed locally or be offloaded to either a cooperative mobile user or CAP for remote execution. The cooperative task offloading problem is investigated to minimize the energy consumption of mobile users while satisfying the execution delay. The problem is formulated as a mixed integer programming, and the NP-hardness is provided by reducing it to a 0-1 knapsack problem. This paper also provides an optimal algorithm based on dynamic programming and an efficient heuristic approach. Numerical results show that the cooperative offloading scheme outperforms the local computing method by 66.4% on the energy consumption of mobile nodes. Furthermore, the proposed heuristic algorithm can achieve near-optimal performance under different network settings.
引用
收藏
页码:2419 / 2435
页数:17
相关论文
共 39 条
  • [1] Mobile Edge Computing: A Survey
    Abbas, Nasir
    Zhang, Yan
    Taherkordi, Amir
    Skeie, Tor
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2018, 5 (01): : 450 - 465
  • [2] [Anonymous], 2011, COMPUTER VISION PATT, DOI DOI 10.1109/CVPRW.2011.5981820
  • [3] Barbarossa S., 2013, IEEE Future Network and Mobile Summit (FutureNetworkSummit), 2013, P1
  • [4] Communicating While Computing [Distributed mobile cloud computing over 5G heterogeneous networks]
    Barbarossa, Sergio
    Sardellitti, Stefania
    Di Lorenzo, Paolo
    [J]. IEEE SIGNAL PROCESSING MAGAZINE, 2014, 31 (06) : 45 - 55
  • [5] Can B, 2006, IEEE ICC, P4520
  • [6] Joint Computation and Communication Cooperation for Energy-Efficient Mobile Edge Computing
    Cao, Xiaowen
    Wang, Feng
    Xu, Jie
    Zhang, Rui
    Cui, Shuguang
    [J]. IEEE INTERNET OF THINGS JOURNAL, 2019, 6 (03) : 4188 - 4200
  • [7] TARCO: Two-Stage Auction for D2D Relay Aided Computation Resource Allocation in HetNet
    Chen, Long
    Wu, Jigang
    Zhang, Xin-xiang
    Zhou, Gangqiang
    [J]. IEEE TRANSACTIONS ON SERVICES COMPUTING, 2021, 14 (01) : 286 - 299
  • [8] BRAINS: Joint Bandwidth-Relay Allocation in Multihoming Cooperative D2D Networks
    Chen, Long
    Wu, Jigang
    Dai, Hong-Ning
    Huang, Xiaoxia
    [J]. IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, 2018, 67 (06) : 5387 - 5398
  • [9] Resource Sharing of a Computing Access Point for Multi-User Mobile Cloud Offloading with Delay Constraints
    Chen, Meng-Hsi
    Dong, Min
    Liang, Ben
    [J]. IEEE TRANSACTIONS ON MOBILE COMPUTING, 2018, 17 (12) : 2868 - 2881
  • [10] Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing
    Chen, Xu
    Jiao, Lei
    Li, Wenzhong
    Fu, Xiaoming
    [J]. IEEE-ACM TRANSACTIONS ON NETWORKING, 2016, 24 (05) : 2827 - 2840