Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 601-605.doi: 10.11896/jsjkx.210200114

• Computer Network • Previous Articles     Next Articles

Optimization of Offloading Decisions in D2D-assisted MEC Networks

FANG Tao1, YANG Yang1, CHEN Jia-xin2   

  1. 1 College of Communications Engineering,Army Engineering University of PLA,Nanjing 210007,China
    2 College of Electronic and Information Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:FANG Tao,born in 1993,postgraduate.His main research interests include game theory,D2D technology and machine learning.
    YANG Yang,born in 1983,associate professor.Her main research interests include UAV-assisted communications and dynamic spectrum sharing in wireless communications.
  • Supported by:
    National Natural Science Foundations of China(61671474),Jiangsu Provincial Natural Science Fund for Outstanding Young Scholars(BK20180028),Jiangsu Provincial Natural Science Fund for Excellent Young Scholars(BK20170089) and Postgraduate Research and Practice Innovation Program of Jiangsu Province(KYCX19_0188).

Abstract: Mobile edge computing(MEC) can provide convenient service for users due to the properties of resource subsidence.To further reduce the offloading pressure caused by massive users and smart applications,this paper utilizes device-to-device(D2D) technology to achieve reasonable utilization of idle local resources.That is,the compute-intensive users can offload their complex tasks to their idle neighbors in the proximity by D2D communications besides the local computing.First,the problem is formulated to minimize the aggregate delay of all users.Then,to analyze the resource competition among users and reduce the complexity,game theory is introduced and the multi-user cooperation offloading game is proposed.The proposed game is proved to be an exact potential game with at least one pure-strategy nash equilibrium(NE).Meanwhile,this paper proposes a better reply based distributed offloading algorithm to obtain the desired solution.Finally,simulation results show that the formulated game model and the proposed algorithm can decrease the network delay and average delay effectively,which validates the feasibility and effectiveness of our work.

Key words: Delay minimization, Device-to-Device, Game theory, Mobile edge computing, Offloading decision

CLC Number: 

  • TN929.5
[1] GEIGER A,LENZ P,URTASUN R.Are we ready for autonomous driving? The KITTI vision benchmark suite[C]//2012 IEEE Conference on Computer Vision and Pattern Recognition.Providence,RI:IEEE,2012:3354-3361.
[2] AIJAZ A.Toward Human-in-the-Loop Mobile Networks:A Radio Resource Allocation Perspective on Haptic Communications[J].IEEE Transactions on Wireless Communications,2018,17(7):4493-4508.
[3] ANTONAKOGLOU K,XU X,STEINBACH E,et al.TowardHaptic Communications Over the 5G Tactile Internet[J].IEEE Communications Surveys Tutorials,2018,20(4):3034-3059.
[4] BANGERTER B,TALWAR S,AREFI R,et al.Networks and devices for the 5G era[J].IEEE Communications Magazine,2014,52(2):90-96.
[5] ABBAS N,ZHANG Y,TAHERKORDI A,et al.Mobile Edge Computing:A Survey[J].IEEE Internet of Things Journal,2018,5(1):450-465.
[6] XIAO Y,KRUNZ M.Dynamic Network Slicing for Scalable Fog Computing Systems With Energy Harvesting[J].IEEE Journal on Selected Areas in Communications,2018,36(12):2640-2654.
[7] LIU P,CHAUDHRY S R,HUANG T,et al.Multi-FactorialEnergy Aware Resource Management in Edge Networks[J].IEEE Transactions on Green Communications and Networking,2019,3(1):45-56.
[8] HABER E E,NGUYEN T M,EBRAHIMI D,et al.Computational Cost and Energy Efficient Task Offloading in Hierarchical Edge-Clouds[C]//2018 IEEE 29th Annual International Symposium on Personal,Indoor and Mobile Radio Communications(PIMRC).Bologna,Italy:IEEE,2018:1-6.
[9] SARDELLITTI S,SCUTARI G,BARBAROSSA S.Joint Optimization of Radio and Computational Resources for Multicell Mobile-Edge Computing[J].IEEE Transactions on Signal and Information Processing over Networks,2015,1(2):89-103.
[10] TALEB T,DUTTA S,KSENTINI A,et al.Mobile Edge Computing Potential in Making Cities Smarter[J].IEEE Communications Magazine,2017,55(3):38-43.
[11] YOO W,YANG W,CHUNG J M.Energy Consumption Minimization of Smart Devices for Delay-Constrained Task Proces-sing with Edge Computing[C]//2020 IEEE International Confe-rence on Consumer Electronics(ICCE).2020:1-3.
[12] VU T T,HUYNH N V,HOANG D T,et al.Offloading Energy Efficiency with Delay Constraint for Cooperative Mobile Edge Computing Networks[C]//2018 IEEE Global Communications Conference(GLOBECOM).Abu Dhabi,United Arab Emirates:IEEE,2018:1-6.
[13] GUO M,LI L,GUAN Q.Energy-Efficient and Delay-Guaran-teed Workload Allocation in IoT-Edge-Cloud Computing Systems[J].IEEE Access,2019,7:78685-78697.
[14] XING H,LIU L,XU J,et al.Joint Task Assignment and Resource Allocation for D2D-Enabled Mobile-Edge Computing[J].IEEE Transactions on Communications,2019,67(6):4193-4207.
[15] REN J,YU G,CAI Y,et al.Latency Optimization for Resource Allocation in Mobile-Edge Computation Offloading[J].IEEE Transactions on Wireless Communications,2018,17(8):5506-5519.
[16] CHEN X,JIAO L,LI W,et al.Efficient Multi-User Computation Offloading for Mobile-Edge Cloud Computing[J].IEEE/ACM Transactions on Networking,2016,24(5):2795-2808.
[17] WU D,ZHOU L,LU P.Win-Win Driven D2D Content Sharing[J].IEEE Internet of Things Journal,2021,8(9):7346-7359.
[18] HE Y,REN J,YU G,et al.D2D Communications Meet Mobile Edge Computing for Enhanced Computation Capacity in Cellular Networks[J].IEEE Transactions on Wireless Communications,2019,18(3):1750-1763.
[19] YAMAMOTO K.A Comprehensive Survey of Potential Game Approaches to Wireless Networks[J].IEICE Transactions on Communications,2015,E98.B(9):1804-1823.
[20] WU Q,DUCHENG W,XU Y,et al.Demand-Aware Multichannel Opportunistic Spectrum Access:A Local Interaction Game Approach With Reduced Information Exchange[J].IEEE Transactions on Vehicular Technology,2014,64(10):4899-4904.
[21] CHEN J,WU Q,XU Y,et al.Joint Task Assignment and Spectrum Allocation in Heterogeneous UAV Communication Networks:A Coalition Formation Game-theoretic Approach[J].IEEE Transactions on Wireless Communications,2021,20(1):440-452.
[22] HAN Z,NIYATO D,SAAD W,et al.Game theory in wireless and communication networks:Theory,models,and applications[M].Cambridge University Press,2011.
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