基于双重拍卖的移动边缘计算任务卸载和资源分配策略
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国家自然科学基金面上项目(61471306);四川省自然科学基金(2022NSFSC0548);四川省重点研发计划(2020YFS0360)


Double-auction-based Task Offloading and Resource Allocation Strategy for Mobile Edge Computing
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    摘要:

    在移动边缘计算(mobile edge computing, MEC)系统中, 用户的卸载策略会影响能耗和计算成本, 进而影响用户效益. 然而, 目前多数研究未考虑边缘服务器随机分布场景中用户的卸载策略和资源请求策略对效益的影响. 针对该问题, 提出了一种基于改进双重拍卖算法的计算卸载和资源分配策略. 首先, 该策略将用户与边缘服务器之间的交互过程建模为Stackelberg博弈, 并且证明了在该博弈内存在唯一纳什均衡点; 其次, 计算出用户对于不同服务器的卸载意愿以及计算资源请求量, 并将用户与最优服务器进行拍卖; 最后, 采用遍历法交换上一轮拍卖中部分交易中的用户与服务器, 以实现系统整体效益最优. 仿真实验结果表明, 与其他基准算法相比, 所提算法在服务器随机分布场景下提高了33.4%的系统用户总效益, 有效降低系统损失.

    Abstract:

    In the mobile edge computing (MEC) system, users’ offloading strategies will affect energy consumption and computing cost, which in turn affects the users’ benefit. However, most of the existing studies have not considered the impact of users’ offloading strategies and resource request strategies on the benefit in the random distribution of edge servers. Therefore, this study proposes a computing offloading and resource allocation strategy based on an improved double auction algorithm. Firstly, this strategy models the interaction process between users and edge servers as a Stackelberg game and proves that there is a unique Nash equilibrium point in the game. Secondly, the users’ willingness to offload different servers and the amount of computing resource requests are calculated, and then users and the optimal server are auctioned. Finally, the traversal method is employed to exchange some transactions in the previous auction for the optimal overall benefit of the system. Simulation results show that, compared with other benchmark algorithms, the proposed algorithm can improve the total benefit of system users by 33.4% in the scenario of random distribution of servers and effectively reduce system loss.

    参考文献
    [1] Abbas N, Zhang Y, Taherkordi A, et al. Mobile edge computing: A survey. IEEE Internet of Things Journal, 2018, 5(1): 450–465. [doi: 10.1109/JIOT.2017.2750180
    [2] Hassan N, Yau KLA, Wu C. Edge computing in 5G: A review. IEEE Access, 2019, 7: 127276–127289. [doi: 10.1109/ACCESS.2019.2938534
    [3] Hassan N, Gillani S, Ahmed E, et al. The role of edge computing in Internet of Things. IEEE Communications Magazine, 2018, 56(11): 110–115. [doi: 10.1109/MCOM.2018.1700906
    [4] Huda SMA, Moh S. Survey on computation offloading in UAV-enabled mobile edge computing. Journal of Network and Computer Applications, 2022, 201: 103341. [doi: 10.1016/j.jnca.2022.103341
    [5] Mach P, Becvar Z. Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1628–1656
    [6] Kong LH, Tan JL, Huang JQ, et al. Edge-computing-driven Internet of Things: A survey. ACM Computing Surveys, 2023, 55(8): 174
    [7] Qiu HM, Zhu K, Luong NC, et al. Applications of auction and mechanism design in edge computing: A survey. IEEE Transactions on Cognitive Communications and Networking, 2022, 8(2): 1034–1058. [doi: 10.1109/TCCN.2022.3147196
    [8] Sadeeq MM, Abdulkareem NM, Zeebaree SRM, et al. IoT and cloud computing issues, challenges and opportunities: A review. Qubahan Academic Journal, 2021, 1(2): 1–7. [doi: 10.48161/qaj.v1n2a36
    [9] Zhou ZY, Liu PJ, Feng JH, et al. Computation resource allocation and task assignment optimization in vehicular fog computing: A contract-matching approach. IEEE Transactions on Vehicular Technology, 2019, 68(4): 3113–3125. [doi: 10.1109/TVT.2019.2894851
    [10] Pham QV, Fang F, Ha VN, et al. A survey of multi-access edge computing in 5G and beyond: Fundamentals, technology integration, and state-of-the-art. IEEE Access, 2020, 8: 116974–117017. [doi: 10.1109/ACCESS.2020.3001277
    [11] Liu YQ, Peng MG, Shou GC, et al. Toward edge intelligence: Multiaccess edge computing for 5G and Internet of Things. IEEE Internet of Things Journal, 2020, 7(8): 6722–6747. [doi: 10.1109/JIOT.2020.3004500
    [12] Zhang K, Mao YM, Leng SP, et al. Energy-efficient offloading for mobile edge computing in 5G heterogeneous networks. IEEE Access, 2016, 4: 5896–5907. [doi: 10.1109/ACCESS.2016.2597169
    [13] Wang YT, Sheng M, Wang XJ, et al. Mobile-edge computing: Partial computation offloading using dynamic voltage scaling. IEEE Transactions on Communications, 2016, 64(10): 4268–4282
    [14] Ning ZL, Dong PR, Kong XJ, et al. A cooperative partial computation offloading scheme for mobile edge computing enabled Internet of Things. IEEE Internet of Things Journal, 2019, 6(3): 4804–4814. [doi: 10.1109/JIOT.2018.2868616
    [15] Tran TX, Pompili D. Joint task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Transactions on Vehicular Technology, 2019, 68(1): 856–868. [doi: 10.1109/TVT.2018.2881191
    [16] Zhan YF, Guo S, Li P, et al. A deep reinforcement learning based offloading game in edge computing. IEEE Transactions on Computers, 2020, 69(6): 883–893. [doi: 10.1109/TC.2020.2969148
    [17] Asheralieva A, Niyato D. Bayesian reinforcement learning and Bayesian deep learning for blockchains with mobile edge computing. IEEE Transactions on Cognitive Communications and Networking, 2021, 7(1): 319–335. [doi: 10.1109/TCCN.2020.2994366
    [18] Xue JB, An YN. Joint task offloading and resource allocation for multi-task multi-server NOMA-MEC networks. IEEE Access, 2021, 9: 16152–16163. [doi: 10.1109/ACCESS.2021.3049883
    [19] Zhao MX, Yu JJ, Li WT, et al. Energy-aware task offloading and resource allocation for time-sensitive services in mobile edge computing systems. IEEE Transactions on Vehicular Technology, 2021, 70(10): 10925–10940. [doi: 10.1109/TVT.2021.3108508
    [20] Kuang ZF, Ma ZH, Li Z, et al. Cooperative computation offloading and resource allocation for delay minimization in mobile edge computing. Journal of Systems Architecture, 2021, 118: 102167. [doi: 10.1016/j.sysarc.2021.102167
    [21] 刘振鹏, 郭超, 王仕磊, 等. 基于博弈论和启发式算法的超密集网络边缘计算卸载. 计算机工程, 2022, 48(12): 54–61, 71. [doi: 10.19678/j.issn.1000-3428.0063734
    [22] 董思岐, 吴嘉慧, 李海龙, 等. 面向优先级任务的移动边缘计算资源分配方法. 计算机工程, 2020, 46(3): 18–23. [doi: 10.19678/j.issn.1000-3428.0054490
    [23] Kiani A, Ansari N. Toward hierarchical mobile edge computing: An auction-based profit maximization approach. IEEE Internet of Things Journal, 2017, 4(6): 2082–2091. [doi: 10.1109/JIOT.2017.2750030
    [24] Zhou H, Wang ZN, Cheng N, et al. Stackelberg-game-based computation offloading method in cloud-edge computing networks. IEEE Internet of Things Journal, 2022, 9(17): 16510–16520. [doi: 10.1109/JIOT.2022.3153089
    [25] Sun W, Liu JJ, Yue YL, et al. Double auction-based resource allocation for mobile edge computing in industrial Internet of Things. IEEE Transactions on Industrial Informatics, 2018, 14(10): 4692–4701. [doi: 10.1109/TII.2018.2855746
    [26] Su Y, Fan WH, Liu YA, et al. A truthful combinatorial auction mechanism towards mobile edge computing in industrial Internet of Things. IEEE Transactions on Cloud Computing, 2022.
    [27] Wang QY, Guo ST, Liu JD, et al. Profit maximization incentive mechanism for resource providers in mobile edge computing. IEEE Transactions on Services Computing, 2022, 15(1): 138–149. [doi: 10.1109/TSC.2019.2924002
    [28] Yue YL, Sun W, Liu JJ. Multi-task cross-server double auction for resource allocation in mobile edge computing. Proceedings of the 2019 ICC IEEE International Conference on Communications (ICC). Shanghai: IEEE, 2019. 1–6.
    [29] Wang YP, Lang P, Tian DX, et al. A game-based computation offloading method in vehicular multiaccess edge computing networks. IEEE Internet of Things Journal, 2020, 7(6): 4987–4996. [doi: 10.1109/JIOT.2020.2972061
    [30] Gao LX, Chen X, Yin B, et al. Computation offloading based on game theory in multi-access edge computing for 6G network. Proceedings of the 14th International Conference on Communication Software and Networks (ICCSN). Chongqing: IEEE, 2022. 63–68.
    [31] Yan Y, Zhang JX, Wei YK, et al. Double action mechanism for vehicle edge computing resource allocation and pricing. Proceedings of the 4th International Conference on Information Communication and Signal Processing (ICICSP). Shanghai: IEEE, 2021. 547–552.
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郑景舜,贾小林.基于双重拍卖的移动边缘计算任务卸载和资源分配策略.计算机系统应用,2023,32(5):45-56

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  • 收稿日期:2022-11-11
  • 最后修改日期:2022-12-10
  • 在线发布日期: 2023-03-17
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