微信服务号

微信订阅号

2025年6月16日 19:26 星期一
首页 > 过刊浏览>2023年第34卷第9期 >4275-4293. DOI:10.13328/j.cnki.jos.006642
PDF HTML阅读 XML下载 导出引用 引用提醒
基于双层Stackelberg博弈的MEC计算卸载方案
作者:
作者简介:

孙伟峰(1978-),男,博士,副教授,CCF高级会员,主要研究领域为边缘计算,无线网络协议优化,基于SDN的跨层智能算法设计;张渊櫆(1998-),男,硕士,主要研究领域为边缘计算中的任务卸载与资源分配;江贺(1980-),男,博士,教授,博士生导师,CCF杰出会员,主要研究领域为基于搜索的软件工程,软件仓库挖掘;秦一星(1999-),女,硕士生,主要研究领域为边缘计算.

通讯作者:

孙伟峰,E-mail:wfsun@dlut.edu.cn

基金项目:

国家重点研发计划(2018YFB1700100); CERNET创新工程(NGII20190801); 中央高校基本科研专项 (DUT21LAB115)


Computation Offloading Scheme Based on Two-layer Stackelberg Game for Multi-access Edge Computing
Author:
  • 摘要
    • | |
  • 访问统计
    • |
  • 参考文献 [32]
    • |
  • 相似文献 [20]
    • | | |
  • 文章评论
    摘要:

    多接入边缘计算 (multi-access edge computing, MEC)中的计算卸载问题已经成为当前研究的热点之一. 目前的计算卸载方案仅考虑云、边、端结构中的计算卸载问题, 而未考虑到其公、私有云的属性. 提出了一种新的计算卸载方案, 所提方案考虑了边缘计算中公有云与私有云之间的关系, 将公有云作为了私有云资源的补充, 可以缓解由于私有云资源局限性带来的算力不足问题; 并通过建立双层Stackelberg博弈来解决计算卸载问题. 对公有云、私有云以及用户的策略和收益进行了分析, 求出了各参与人的最优策略, 证明了双层博弈的纳什均衡解的存在性及唯一性. 仿真结果和分析也验证了基于双层Stackelberg博弈的计算卸载方案的可行性, 且相较基于单层Stackelberg博弈的卸载方案更高效, 更适合可扩展的边缘计算的环境.

    Abstract:

    The computation offloading problem of multi-access edge computing (MEC) has become one of the research focuses. The current computation offloading scheme only considers the computation offloading problem in the cloud, edge, and end structures and does not take into account the attributes of the public and private clouds. In this study, a novel computation offloading scheme is proposed, which considers the relationship between the public cloud and private cloud in edge computing and regards the public cloud as a supplement to private cloud resources to alleviate the insufficient computing power caused by the limitations of private cloud resources. Moreover, a two-layer Stackelberg game is established to solve the computation offloading problem. The optimal strategies of each player are obtained upon the analysis of the strategies and profits of the public cloud, the private cloud, and users, and the existence and uniqueness of the Nash equilibrium solution to the two-layer game are proved. The simulation results and analysis verifies the feasibility of the computation offloading scheme based on the two-layer Stackelberg game. Compared with the computation offloading scheme based on the single-layer Stackelberg game, the proposed scheme is more efficient and more suitable for edge computing environments.

    参考文献
    [1] Hao YX, Jiang YG, Hossain MS, Alhamid MF, Amin SU. Learning for smart edge: Cognitive learning-based computation offloading. Mobile Networks and Applications, 2020, 25(3): 1016-1022. [doi: 10.1007/s11036-018-1119-7
    [2] Ndikumana A, Tran NH, Ho TH, Han Z, Saad W, Niyato D, Hong CS. Joint communication, computation, caching, and control in big data multi-access edge computing. IEEE Trans. on Mobile Computing, 2020, 19(6): 1359-1374. [doi: 10.1109/TMC.2019.2908403
    [3] Ren JK, Yu GD, Cai YL, He YH, Qu FZ. Partial offloading for latency minimization in mobile-edge computing. In: Proc. of the 2017 IEEE Global Communications Conf. Singapore: IEEE, 2017. 1-6.
    [4] Mahmood A, Ahmed A, Naeem M, Hong Y. Partial offloading in energy harvested mobile edge computing: A direct search approach. IEEE Access, 2020, 8: 36757-36763. [doi: 10.1109/ACCESS.2020.2974809
    [5] Tuong VD, Truong TP, Nguyen TV, Noh W, Cho S. Partial computation offloading in NOMA-assisted mobile-edge computing systems using deep reinforcement learning. IEEE Internet of Things Journal, 2021, 8(17): 13196-13208. [doi: 10.1109/JIOT.2021.3064995
    [6] Zhao HL, Du W, Liu W, Lei T, Lei QW. QoE aware and cell capacity enhanced computation offloading for multi-server mobile edge computing systems with energy harvesting devices. In: Proc. of the 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation. Guangzhou: IEEE, 2018. 671-678.
    [7] Huang BB, Li ZJ, Tang P, Wang SG, Zhao J, Hu HY, Li WQ, Chang V. Security modeling and efficient computation offloading for service workflow in mobile edge computing. Future Generation Computer Systems, 2019, 97: 755-774. [doi: 10.1016/j.future.2019.03.011
    [8] Goudarzi M, Zamani M, Haghighat AT. A genetic-based decision algorithm for multisite computation offloading in mobile cloud computing. Int’l Journal of Communication Systems, 2017, 30(10): e3241. [doi: 10.1002/dac.3241
    [9] Liu JH, Zhang Q. Code-partitioning offloading schemes in mobile edge computing for augmented reality. IEEE Access, 2019, 7: 11222-11236. [doi: 10.1109/ACCESS.2019.2891113
    [10] Huynh LNT, Pham QV, Pham XQ, Nguyen TDT, Hossain D, Huh EN. Efficient computation offloading in multi-tier multi-access edge computing systems: A particle swarm optimization approach. Applied Sciences, 2020, 10(1): 203. [doi: 10.3390/app10010203
    [11] Wang QY, Mao YC, Wang YC, Wang LB. Computation tasks offloading scheme based on multi-cloudlet collaboration for edge computing. In: Proc. of the 7th Int’l Conf on Advanced Cloud and Big Data. Suzhou: IEEE, 2019. 339-344.
    [12] Shakarami A, Ghobaei-Arani M, Shahidinejad A. A survey on the computation offloading approaches in mobile edge computing: A machine learning-based perspective. Computer Networks, 2020, 182: 107496. [doi: 10.1016/j.comnet.2020.107496
    [13] Shin KY, Jeong HJ, Moon SM. Enhanced partitioning of DNN layers for uploading from mobile devices to edge servers. In: Proc. of the 3rd Int’l Workshop on Deep Learning for Mobile Systems and Applications. New York: ACM, 2019. 35-40.
    [14] Jeong HJ, Lee HJ, Shin CH, Moon SM. IONN: Incremental offloading of neural network computations from mobile devices to edge servers. In: Proc. of ACM Symp. on Cloud Computing. Carlsbad: ACM, 2018. 401-411.
    [15] Huang L, Feng X, Feng AQ, Huang YP, Qian LP. Distributed deep learning-based offloading for mobile edge computing networks. Mobile Networks and Applications, 2018.
    [16] Huang L, Feng X, Zhang C, Qian LP, Wu Y. Deep reinforcement learning-based joint task offloading and bandwidth allocation for multi-user mobile edge computing. Digital Communications and Networks, 2019, 5(1): 10-17. [doi: 10.1016/j.dcan.2018.10.003
    [17] Chen XF, Zhang HG, Wu CLMG, Mao SW, Ji YS, Bennis M. Performance optimization in mobile-edge computing via deep reinforcement learning. In: Proc. of the 88th IEEE Vehicular Technology Conf. Chicago: IEEE, 2018. 1-6.
    [18] Yang TY, Hu YL, Gursoy MC, Schmeink A, Mathar R. Deep reinforcement learning based resource allocation in low latency edge computing networks. In: Proc. of the 15th Int’l Symp. on Wireless Communication Systems. Lisbon: IEEE, 2018. 1-5.
    [19] Lu HF, Gu CH, Luo F, Ding WC, Liu XP. Optimization of lightweight task offloading strategy for mobile edge computing based on deep reinforcement learning. Future Generation Computer Systems, 2020, 102: 847-861. [doi: 10.1016/j.future.2019.07.019
    [20] Tan LT, Hu RQ. Mobility-aware edge caching and computing in vehicle networks: A deep reinforcement learning. IEEE Trans. on Vehicular Technology, 2018, 67(11): 10190-10203. [doi: 10.1109/TVT.2018.2867191
    [21] Chen Z, Wang XD. Decentralized computation offloading for multi-user mobile edge computing: A deep reinforcement learning approach. EURASIP Journal on Wireless Communications and Networking, 2020, 2020(1): 188. [doi: 10.1186/s13638-020-01801-6
    [22] Zeng DZ, Gu L, Pan SL, Cai JJ, Guo S. Resource management at the network edge: A deep reinforcement learning approach. IEEE Network, 2019, 33(3): 26-33. [doi: 10.1109/MNET.2019.1800386
    [23] Verma T, Kumar A. Fuzzy Solution Concepts for Non-cooperative Games. Cham: Springer, 2020.
    [24] Mukherjee A, Deb P, De D, Buyya R. C2OF2N: A low power cooperative code offloading method for femtolet-based fog network. The Journal of Supercomputing, 2018, 74(6): 2412-2448. [doi: 10.1007/s11227-018-2269-x
    [25] Guo HZ, Liu JJ, Qin HL. Collaborative mobile edge computation offloading for IoT over fiber-wireless networks. IEEE Network, 2018, 32(1): 66-71. [doi: 10.1109/MNET.2018.1700139
    [26] Chen X. Decentralized computation offloading game for mobile cloud computing. IEEE Trans. on Parallel and Distributed Systems, 2015, 26(4): 974-983. [doi: 10.1109/TPDS.2014.2316834
    [27] Jošilo S, Dán G. Wireless and computing resource allocation for selfish computation offloading in edge computing. In: Proc. of the 2019 IEEE Conf. on Computer Communications. Paris: IEEE, 2019. 2467-2475.
    [28] Liang BZ, Fan RF, Hu H, Zhang Y, Zhang N, Anpalagan A. Nonlinear pricing based distributed offloading in multi-user mobile edge computing. IEEE Trans. on Vehicular Technology, 2021, 70(1): 1077-1082. [doi: 10.1109/TVT.2020.3045473
    [29] Lei Y, Zheng WB, Ma Y, Xia YN, Xia Q. A novel probabilistic-performance-aware and evolutionary game-theoretic approach to task offloading in the hybrid cloud-edge environment. In: Proc. of the 16th Int’l Conf. on Collaborative Computing: Networking, Applications and Worksharing. Shanghai: Springer, 2020. 255-270.
    [30] Zhou SC, Jadoon W. The partial computation offloading strategy based on game theory for multi-user in mobile edge computing environment. Computer Networks, 2020, 178: 107334. [doi: 10.1016/j.comnet.2020.107334
    [31] Gao L, Moh M. Joint Computation Offloading and Prioritized Scheduling in Mobile Edge Computing. In: Proc. of the 2018 Int’l Conf. on High Performance Computing & Simulation (HPCS). IEEE, 2018. 1000-1007.
    [32] Xiao Y, Bi GA, Niyato D. A simple distributed power control algorithm for cognitive radio networks. IEEE Trans. on Wireless Communications, 2011, 10(11): 3594-3600. [doi: 10.1109/TWC.2011.090611.102049
    引证文献
    网友评论
    网友评论
    分享到微博
    发 布
引用本文

孙伟峰,张渊櫆,江贺,秦一星.基于双层Stackelberg博弈的MEC计算卸载方案.软件学报,2023,34(9):4275-4293

复制
分享
文章指标
  • 点击次数:1101
  • 下载次数: 3887
  • HTML阅读次数: 1159
  • 引用次数: 0
历史
  • 收稿日期:2021-05-25
  • 最后修改日期:2021-08-25
  • 在线发布日期: 2022-06-06
  • 出版日期: 2023-09-06
文章二维码
友情链接:中国科学院软件研究所中国计算机学会中国科学院国家自然科学基金委员会CCF数字图书馆Int'l J of Softw Info计算机系统应用
您是第20061685位访问者
版权所有：中国科学院软件研究所 京ICP备05046678号-3
地址：北京市海淀区中关村南四街4号,邮政编码：100190
电话：010-62562563 传真：010-62562533 Email：jos@iscas.ac.cn
技术支持：北京勤云科技发展有限公司

京公网安备 11040202500063号