MEC中资源分配与卸载决策联合优化策略

doi:10.3778/j.issn.1673-9418.2006087

摘要/Abstract

摘要：

针对移动边缘计算（MEC）中用户任务处理时延与能耗过高的问题，提出了“云-边-端”三层MEC计算卸载结构下的资源分配与卸载决策联合优化策略。首先，考虑系统时延与能耗，将优化问题规划为系统总增益（任务处理时延与能耗相对减少的加权和）最大化问题；其次，为用户任务设置优先级，并根据任务数据量初始化卸载决策方案；然后，采用均衡传输性能的信道分配算法为卸载任务分配信道资源，对于卸载至同一边缘服务器上的任务以最大化资源收益为目标进行资源竞争，实现计算资源最优配置；最后，基于博弈论证明优化问题为关于卸载决策的势函数，即存在纳什均衡，并利用迭代增益值比较法得到了纳什均衡下的卸载决策方案。仿真结果表明，所提联合优化策略在满足用户处理时延要求的情况下最大化系统总增益，有效地提高了计算卸载的性能。

关键词: 移动边缘计算（MEC）, 资源分配, 卸载决策, 势博弈

Abstract:

Considering the problem of users?? high processing delay and energy consumption in mobile edge computing (MEC), a joint optimization scheme of resource allocation and offloading decision based on the “cloud-edge-end” three-tier MEC computation offloading structure is proposed. Firstly, considering the system delay and energy con-sumption, the optimization problem is studied in order to maximize the users?? task offloading gain, which is measured by a weighted sum of reductions in tasks?? relative processing delay and energy consumption. Secondly, the priority is set for users?? tasks and the offloading decision is initialized according to the data size of tasks. Then, the channel allocation algorithm that balances transmission performance is proposed to allocate channel resources for offloading tasks. For the tasks that are offloaded to the same edge server, the optimal allocation of computing resources can be achieved by computing for resources with the goal of maximizing resources?? profit. Finally, the optimization problem is proven to be a potential function about the offloading decision based on game theory, that is, there exists a Nash equilibrium, and the iterative method by comparing the gain value is used to achieve the offloading decision under Nash equilibrium. The simulation results show that the proposed joint optimization scheme achieves the maximum total system gain under meeting the processing delay requirements of users, and effectively improves the performance of computation offloading.

Key words: mobile edge computing (MEC), resource allocation, offloading decision, potential game

刘继军, 邹山花, 卢先领. MEC中资源分配与卸载决策联合优化策略[J]. 计算机科学与探索, 2021, 15(5): 848-858.

LIU Jijun, ZOU Shanhua, LU Xianling. Joint Optimization Scheme of Resource Allocation and Offloading Decision in Mobile Edge Computing[J]. Journal of Frontiers of Computer Science and Technology, 2021, 15(5): 848-858.

参考文献

[1] WANG S Q, MURTAZA Z, KIN K L. Online placement of multi-component applications in edge computing environments[J]. IEEE Access, 2017(5): 2514-2533.
[2] MAO Y Y, YOU C S, ZHANG J, et al. A survey on mobile edge computing: the communication perspective[J]. IEEE Communications Surveys & Tutorials, 2017, 19(4): 2322-2358.
[3] YU W, LIANG F, HE X F, et al. A survey on the edge com-puting for the Internet of things[J]. IEEE Access, 2018(6): 6900-6919.
[4] 4G Americas. 4G Americas?? recommendations on 5G requi-rements and solutions white paper[EB/OL]. (2014-10-23). http://www.4gamericas.org.
[5] TUYEN X T, DARIO P. Joint task offloading and resource allocation for multi-server mobile-edge computing networks[J]. IEEE Transactions on Vehicular Technology, 2019, 68(1): 856-868.
[6] MIAO Y M, WU G X, LI M, et al. Intelligent task prediction and computation offloading based on mobile-edge cloud com-puting[J]. Future Generation Computer Systems, 2019, 102: 925-931.
[7] CHEN M, HAO Y X. Task offloading for mobile edge com-puting in software defined ultra-dense network[J]. IEEE Jour-nal on Selected Areas in Communications, 2018, 36(3): 587-597.
[8] ZHANG J, XIA W W, YAN F, et al. Joint computation off-loading and resource allocation optimization in heterogeneous networks with mobile edge computing[J]. IEEE Access, 2018(6): 19324-19337.
[9] CHEN Y, ZHANG N, ZHANG Y C, et al. Energy efficient dynamic offloading in mobile edge computing for Internet of things[J]. IEEE Transactions on Cloud Computing, 2019: 1.
[10] HAO Y X, CHEN M, HU L, et al. Energy efficient task caching and offloading for mobile edge computing[J]. IEEE Access, 2018(6): 11365-11373.
[11] ZHAO H T, DING Y, ZHANG M K, et al. Multipath trans-mission workload balancing optimization scheme based on mobile edge computing in vehicular heterogeneous network[J]. IEEE Access, 2019(7): 116047-116055.
[12] DAI H J, ZENG X Y, YU Z L, et al. A scheduling algorithm for autonomous driving tasks on mobile edge computing servers[J]. Journal of Systems Architecture, 2019, 94: 14-23.
[13] WU Y L, WU J G, CHEN L, et al. Efficient task scheduling for servers with dynamic states in vehicular edge computing[J]. Computer Communications, 2019, 150: 245-253.
[14] ZHANG H B, LI H, CHEN S X, et al. Computing offloading and resource optimization in ultra-dense networks with mobile edge computation[J]. Journal of Electronics & Information Technology, 2019, 41(5): 1194-1201.
张海波, 李虎, 陈善学, 等. 超密集网络中基于移动边缘计算的任务卸载和资源优化[J]. 电子与信息学报, 2019, 41(5): 1194-1201.
[15] LYU X C, TIAN H, NI W, et al. Energy-efficient admission of delay-sensitive tasks for mobile edge computing[J]. IEEE Transactions on Communications, 2018, 66(6): 2603-2616.
[16] HUANG X G, CUI Y F, ZHANG D Y, et al. Joint optimiza-tion scheme of task offloading and resource allocation based on MEC[J]. Systems Engineering and Electronics, 2020, 42(6): 1386-1394.
黄晓舸, 崔艺凡, 张东宇，等. 基于MEC的卸载任务和计算资源分配联合优化方案[J]. 系统工程与电子技术, 2020, 42(6): 1386-1394.
[17] WANG C, DONG C W, QIN J H, et al. Energy-efficient off-loading policy for resource allocation in distributed mobile edge computing[C]//Proceedings of the 2018 IEEE Sympo-sium on Computer and Communications, Natal, Jun 25-28, 2018. Piscataway: IEEE, 2018: 366-372.