Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (2): 32-41.doi: 10.11896/jsjkx.220300198

• Edge Intelligent Collaboration Technology and Frontier Applications • Previous Articles     Next Articles

Resource Allocation Strategy Based on Game Theory in Mobile Edge Computing

CHEN Yipeng1,2,3, YANG Zhe1,2,3, GU Fei1,2, ZHAO Lei1,2,3   

  1. 1 School of Computer Science and Technology,Soochow University,Suzhou,Jiangsu 215006,China
    2 Provincial Key Laboratory for Computer Information Processing Technology,Soochow University,Suzhou,Jiangsu 215006,China
    3 Provincial Laboratory for Big Data Intelligent Engineering,Soochow University,Suzhou,Jiangsu 215006,China
  • Received:2022-03-21 Revised:2022-08-16 Online:2023-02-15 Published:2023-02-22
  • Supported by:
    National Natural Science Foundation of China(62072321),National Science Foundation of Jiangsu Higher Education Institutions of China(20KJB520002),Future Network Scientific Research Fund Project(FNSRFP-2021-YB-38) and Priority Academic Program Development of Jiangsu Higher Education Institutions

PDF (PC)

2053

Abstract: The existing research on resource allocation strategies of mobile edge computing mostly focus on delay and energy consumption,but relatively less consideration is given to the benefits of edge servers.When considering the benefits of edge servers,many studies ignore the optimization of delay.Therefore,a two-way update strategy based on game theory(TUSGT) is proposed.TUSGT transforms the task competition between servers into a non-cooperative game problem on the side of edge servers,and adopts a joint optimization strategy based on potential game,which allows every edge server to determine the task selection prefe-rence with the goal of maximizing its own profit.On the side of mobile devices,the EWA algorithm in online learning is used to update the parameters,which affects the task selection preference of the edge servers from a global perspective and improves the overall deadline hit rate.Simulation results show that,compared with BGTA,MILP,greedy strategy,random strategy,and ideal strategy,TUSGT can increase the deadline hit rate by up to 30%,and increase the average profit of edge servers by up to 65%.

Key words: Mobile edge computing, Resource allocation, Game theory, Two-way update, Potential game

CLC Number: 

  • TP393
[1]HEUVELDOP N.Ericsson mobility report(5g)[R].Stock-holm,Ericsson,2018.
[2]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,2018,17(6):4177-4190.
[3]MAO Y Y,ZHANG J,SONG S H,et al.Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems[J].IEEE Transactions on Wireless Communications,2017,16(9):5994-6009.
[4]SAMIMI F A,MCKINLEY P K,SADJADI S M.Mobile service clouds:A self-managing infrastructure for autonomic mobile computing services [C]//Self-managed Networks,Systems,& Services,Second IEEE International Workshop.Selfman,Dublin,Ireland:IEEE,2006.
[5]ZHOU B,DASTJERDI A V,CALHEIROS R N,et al.A context sensitive offloading scheme for mobile cloud computing service [C]//2017 IEEE 8th International Conference on Cloud Computing.Piscataway:IEEE Press,2017:869-876.
[6]PATEL M,NAUGHTON B,CHAN C,et al.Mobile-edge computing introductory technical white paper [R].Mobile-edge Computing(MEC)Industry Initiative,2014.
[7]HU Y C,PATEL M,SABELLA D,et al.Mobile edge compu-ting－a key technology towards 5g [J].ETSI White Paper,2015,11(1):1-16
[8]MAO Y Y,YOU C S,ZHANG J,et al.A survey on mobile edgecomputing:the communication perspective[J].IEEE Communication Surveys and Tutorials,2017,19(4):2322-2358.
[9]ABBAS N,ZHANG Y,TAHERKORDI A,et al.Mobile edgecomputing:A survey[J].IEEE Internet of Things Journal,2018,5(1):450-465.
[10]MACH P,BECVAR Z.Mobile edge computing:a survey on architecture and computation offloading[J].IEEE Communication Surveys and Tutorials,2017,19(3):1628-1656.
[11]LI J,ZHANG Y P,PANG L,et al.Joint Resource Allocation andTask Scheduling in Mobile Edge Computing[ J].Journal of Chongqing University of Technology(Natural Science),2020,34(11):156-163.
[12]LIU L,CHEN C,FENG J,et al.Joint intelligent optimization of task offloading and service caching for vehicular edge computing[J].Journal on Communications,2021,42(1):18-26.
[13]HU M,XIE Z X,WU D,et al.Heterogeneous edge offloadingwith incomplete information:A minority game approach[J].IEEE Transactions on Parallel and Distributed Systems,2020,31(9):2139-2154.
[14]YOU C S,HUANG K B,CHAE H,et al.Energy-Efficient resource allocation for mobile-edge computation offloading[J].IEEE Transactions on Wireless Communications,2017,16(3):1397-1411.
[15]JI L Y,GUO S T.Energy-efficient coopera-tive resource allocation in wireless powered mobile edge computing[J].IEEE Internet of Things Journal,2018,6(3):4744-4754.
[16]GAO J X,WANG J.Multi-edge Collabora-tive Computing Unloading Scheme Based on Gene-tic Algorithm[J].Computer Science,2021,48(1):72-80.
[17]CHEN L.Multicast resource allocation algorithm based on layered coding in sparse code multiple access systems[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2020,32(6):917-924.
[18]JIAO Y T,WANG P,NIYATO D,et al.So-cial welfare maximization auction in edge compu-ting resource allocation for mobile blockchain [C]//2018 IEEE International Conference on Communi-cations(ICC).Piscataway:IEEE Press,2018:1-6.
[19]LUONG N C,XIONG Z H,WANG P,et al.Optimal auction for edge computing resource mana-gement in mobile blockchain networks:A deep learning approach [C]//2018 IEEE International Conference on Communications(ICC).Piscataway:IEEE Press,2018:1-6.
[20]LIU D Q,KHOUKHI L,HAFID A.Decentralized data offloa-ding for mobile cloud computing based on game theory [C]//2017 Second International Conference on Fog and Mobile Edge Computing(FMEC).Valencia,Spain,2017:20-24.
[21]YAN J,BI S Z,ZHANG J Y,et al.Optimal task offloading and resource allocation in mobile-edge computing with inter-user task dependency[J].IEEE Transactions on Wireless Communications,2020,19(1):235-250.
[22]ZHANG D Y,WANG D.An integrated top-down and bottom-up task allocation approach in social sensing based edge computing systems [C]//IEEE INFOCOM 2019-IEEE Conference on Computer Communications.Piscataway:IEEE Press,2019:766-774.
[23]MASHHADI F,SALINAS S,BOZORGCHENANI A,et al.Optimal auction for delay and energy constrained task offloading in mobile edge computing[J].Computer Networks,2020,183:107527.
[24]ALKHALAILEH M,CALHEIROS R N,NGUYEN Q V,et al.Data-intensive application scheduling on mobile edge cloud computing[J].Journal of Network and Computer Applications,2020,167:102735.
[25]XIE X Z,YAN K,TIAN Y,et al.Resource allocation algorithm with interference constraint for energy-efficient D2D communication based on game theory in cognitive networks[J].Journal of Chongqing University of Posts and Telecommunications(Na-tural Science Edition),2020,32(1):47-56.
[26]QU D Y,HEI K X,GUO H B,et al.Game behavior and model of lane-changing on the internet of vehicles environment[J].Journal of Jilin University(Engineering and Technology Edition),2022,52(1):101-109.
[27]MONDERER D,SHAPLEY L S.Potential games[J].Gamesand Economic Behavior,1996,14(1):124-143.
[28]CHEN X,JIAO L,LI W Z,et al.Efficient multi-user computa-tion off-loading for mobile-edge cloud computing[J].IEEE/ACM Transactions on Networking,2016,24(5):2795-2808.
[29]ZHAN Y,GUO S,LI P,et al.A Deep Reinfor-cement Learning Based Offloading Game in Edge Computing[J].IEEE Transactions on Computers,2020,69(6):883-893.
[30]CESA-BIANCHI N,LUGOSI G.Prediction,learning,and games [M].Cambridge:Cambridge University Press,2006.
[31]ZHANG D,MA Y,ZHANG Y,et al.A real-time and non-coope-rative task allocation framework for social sensing applications in edge computing systems [C]//2018 IEEE Real-Time and Embedded Technology and Applications Symposium(RTAS).Piscataway:IEEE Press,2018:316-326.
[1] ZHENG Hongqiang, ZHANG Jianshan, CHEN Xing. Deployment Optimization and Computing Offloading of Space-Air-Ground Integrated Mobile Edge Computing System [J]. Computer Science, 2023, 50(2): 69-79.
[2] LI Xiaohuan, CHEN Bitao, KANG Jiawen, YE Jin. Coalition Game-assisted Joint Resource Optimization for Digital Twin-assisted Edge Intelligence [J]. Computer Science, 2023, 50(2): 42-49.
[3] JIANG Yang-yang, SONG Li-hua, XING Chang-you, ZHANG Guo-min, ZENG Qing-wei. Belief Driven Attack and Defense Policy Optimization Mechanism in Honeypot Game [J]. Computer Science, 2022, 49(9): 333-339.
[4] YU Bin, LI Xue-hua, PAN Chun-yu, LI Na. Edge-Cloud Collaborative Resource Allocation Algorithm Based on Deep Reinforcement Learning [J]. Computer Science, 2022, 49(7): 248-253.
[5] TANG Feng, FENG Xiang, YU Hui-qun. Multi-task Cooperative Optimization Algorithm Based on Adaptive Knowledge Transfer andResource Allocation [J]. Computer Science, 2022, 49(7): 254-262.
[6] LI Meng-fei, MAO Ying-chi, TU Zi-jian, WANG Xuan, XU Shu-fang. Server-reliability Task Offloading Strategy Based on Deep Deterministic Policy Gradient [J]. Computer Science, 2022, 49(7): 271-279.
[7] FANG Tao, YANG Yang, CHEN Jia-xin. Optimization of Offloading Decisions in D2D-assisted MEC Networks [J]. Computer Science, 2022, 49(6A): 601-605.
[8] LIU Zhang-hui, ZHENG Hong-qiang, ZHANG Jian-shan, CHEN Zhe-yi. Computation Offloading and Deployment Optimization in Multi-UAV-Enabled Mobile Edge Computing Systems [J]. Computer Science, 2022, 49(6A): 619-627.
[9] XIE Wan-cheng, LI Bin, DAI Yue-yue. PPO Based Task Offloading Scheme in Aerial Reconfigurable Intelligent Surface-assisted Edge Computing [J]. Computer Science, 2022, 49(6): 3-11.
[10] ZHOU Tian-qing, YUE Ya-li. Multi-Task and Multi-Step Computation Offloading in Ultra-dense IoT Networks [J]. Computer Science, 2022, 49(6): 12-18.
[11] QIU Xu, BIAN Hao-bu, WU Ming-xiao, ZHU Xiao-rong. Study on Task Offloading Algorithm for Internet of Vehicles on Highway Based on 5G MillimeterWave Communication [J]. Computer Science, 2022, 49(6): 25-31.
[12] XU Hao, CAO Gui-jun, YAN Lu, LI Ke, WANG Zhen-hong. Wireless Resource Allocation Algorithm with High Reliability and Low Delay for Railway Container [J]. Computer Science, 2022, 49(6): 39-43.
[13] SHEN Jia-fang, QIAN Li-ping, YANG Chao. Non-orthogonal Multiple Access and Multi-dimension Resource Optimization in EH Relay NB-IoT Networks [J]. Computer Science, 2022, 49(5): 279-286.
[14] PAN Yan-na, FENG Xiang, YU Hui-qun. Competitive-Cooperative Coevolution for Large Scale Optimization with Computation Resource Allocation Pool [J]. Computer Science, 2022, 49(2): 182-190.
[15] ZHANG Hai-bo, ZHANG Yi-feng, LIU Kai-jian. Task Offloading,Migration and Caching Strategy in Internet of Vehicles Based on NOMA-MEC [J]. Computer Science, 2022, 49(2): 304-311.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.