计算机科学 ›› 2023, Vol. 50 ›› Issue (2): 32-41.doi: 10.11896/jsjkx.220300198
陈祎鹏1,2,3, 杨哲1,2,3, 谷飞1,2, 赵雷1,2,3
CHEN Yipeng1,2,3, YANG Zhe1,2,3, GU Fei1,2, ZHAO Lei1,2,3
摘要: 现有的对移动边缘计算资源分配策略问题的研究,较多的是针对时延和能耗因素进行优化,考虑边缘服务器的收益问题的相对较少,而在考虑边缘服务器收益时,许多研究忽略了对任务完成时延的优化。因此,提出了一种基于博弈论的双向更新策略(TUSGT)。TUSGT在边缘服务器侧将其之间的任务竞争关系转化为一个非合作博弈问题,采用基于势博弈的联合优化策略,允许边缘服务器以最大化其自身收益为目的来确定任务选择偏好。在移动设备侧使用在线学习中的EWA算法进行参数更新,从全局角度影响边缘服务器的任务选择偏好,提高总体任务完成率。仿真实验结果表明,TUSGT与BGTA、MILP、贪婪策略、随机策略、理想策略相比,任务完成率最多提高30%,边缘服务器平均收益最多提高65%。
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[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. |
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