移动边缘计算中的时延和能耗均衡优化算法

doi:10.13190/j.jbupt.2019-093

北京邮电大学学报 ›› 2020, Vol. 43 ›› Issue (2): 110-115.doi: 10.13190/j.jbupt.2019-093

移动边缘计算中的时延和能耗均衡优化算法

景泽伟¹, 杨清海¹, 秦猛²

1. 西安电子科技大学综合业务网理论及关键技术国家重点实验室, 西安 710071;
2. 鹏城实验室, 深圳 518055

收稿日期:2019-05-28 发布日期:2020-04-28
通讯作者: 杨清海(1976-),男,教授,E-mail:qhyang@xidian.edu.cn. E-mail:qhyang@xidian.edu.cn
作者简介:景泽伟(1993-),男,博士生.
基金资助:
国家自然科学基金项目（61971327）；中国博士后科学基金项目（2019M663015，2019TQ0210）

A Delay and Energy Tradeoff Optimization Algorithm for Task Offloading in Mobile-Edge Computing Networks

JING Ze-wei¹, YANG Qing-hai¹, QIN Meng²

1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi'an 710071, China;
2. Peng Cheng Laboratory, Shenzhen 518055, China

Received:2019-05-28 Published:2020-04-28

摘要/Abstract

摘要： 为了提升移动边缘计算（MEC）网络中的任务卸载效用，提出了一种基于任务卸载增益最大化的时延和能耗均衡优化算法.通过分析通信资源和计算资源对时延和能耗这2种性能指标的制约关系，将原问题分解为联合发射功率子信道分配子问题和MEC计算频率分配子问题.通过Karush-Kuhn-Tucker条件，导出了最优的MEC计算频率闭式解.此外，提出了一种基于二分法的发射功率分配算法和基于匈牙利二部图匹配的子信道分配算法.仿真结果表明，提出的算法相比传统算法可以显著提升用户的任务卸载效用.

关键词: 移动边缘计算, 任务卸载, 子信道分配, 能耗, 任务完成时间

Abstract: In order to enhance the task offloading utility in mobile-edge computing (MEC) networks,a delay and energy tradeoff optimization algorithm was proposed for maximizing the users' task offloading gains. The original optimization problem was decomposed into two sub-problems,i.e.,the joint transmit power and sub-channel allocation sub-problem and the MEC computing frequency allocation sub-problem,upon the analysis of the restriction of communication and computation resources to the delay and energy consumption performances. The optimal MEC computing frequency was directly derived in the closed form by the Karush-Kuhn-Tucker condition. In addition,an efficient bisection method based transmit power allocation algorithm and a bipartite graph matching based sub-channel allocation algorithm were proposed,respectively. Numerical simulation results showed that,the proposed algorithm could improve the task offloading utility remarkably when compared with some traditional algorithms.

Key words: mobile-edge computing, task offloading, sub-channel allocation, energy consumption, task completion time

中图分类号:

TP393

景泽伟, 杨清海, 秦猛. 移动边缘计算中的时延和能耗均衡优化算法[J]. 北京邮电大学学报, 2020, 43(2): 110-115.

JING Ze-wei, YANG Qing-hai, QIN Meng. A Delay and Energy Tradeoff Optimization Algorithm for Task Offloading in Mobile-Edge Computing Networks[J]. Journal of Beijing University of Posts and Telecommunications, 2020, 43(2): 110-115.

参考文献

[1] Mach P, Becvar Z. Mobile edge computing:a survey on architecture and computation offloading[J]. IEEE Commun Surveys Tuts, 2017, 19(3):1628-1656.
[2] Wang C, Liang C, Yu F R, et al. Computation offloading and resource allocation in wireless cellular networks with mobile edge computing[J]. IEEE Trans Wireless Commun, 2017, 16(8):4924-4938.
[3] Mao Y, You C, Zhang J, et al. A survey on mobile edge computing:the communication perspective[J]. IEEE Commun Surveys Tuts, 2017, 19(4):2322-2358.
[4] Wang Y, Sheng M, Wang X, et al. Mobile-edge computing:partial computation offloading using dynamic voltage scaling[J]. IEEE Trans Commun, 2016, 64(10):4268-4282.
[5] Mao Y, Zhang J, Letaief K B. Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J]. IEEE J Sel Areas Commun, 2016, 34(12):3590-3605.
[6] Wu Y, Ni K, Zhang C, et al. NOMA assisted multi-access mobile edge computing:a joint optimization of computation offloading and time allocation[J]. IEEE Trans Veh Technol, 2018, 67(12):12244-12258.
[7] Chen X, Zhang H, Wu C, et al. Optimized computation offloading performance in virtual edge computing systems via deep reinforcement learning[J]. IEEE IoT J, 2019, 6(3):4005-4018.
[8] Zhang J, Xia W, Yan F, et al. Joint computation offloading and resource allocation optimization in heterogeneous networks with mobile edge computing[J]. IEEE Access, 2018(6):19324-19337.
[9] Boyd S, Vandenberghe L. Convex optimization[M]. Cambridge:Cambridge Univ Press, 2004.
[10] Liu R, Sheng M, Wu W. Energy-efficient resource allocation for heterogeneous wireless network with multi-homed user equipment[J]. IEEE Access, 2018(6):14591-14601.
[11] Kuhn H W. The Hungarian method for the assignment problem[J]. Nav Res Log, 2005, 52(1):7-21.
[12] 吴伟华, 杨清海. 面向无线自主网络高效信息扩散的资源分配[J]. 北京邮电大学学报, 2016, 39(2):88-92. Wu Weihua, Yang Qinghai. Efficient resource allocation for information diffusion in wireless autonomic network[J]. Journal of Beijing University of Posts and Telecommunications, 2016, 39(2):88-92.
[13] Liu D, Wang L. Distributed energy efficient fair user association in massive MIMO enabled HetNets[J]. IEEE Commun Lett, 2015, 19(10):1770-1773.

[1]	魏明亮耿绥燕赵雄文胡玮范静怡. 超密集网络中移动边缘计算的资源分配和任务卸载联合优化研究[J]. 北京邮电大学学报, 2023, 46(2): 50-56.
[2]	李晓辉苏家楠吕思婷张鹏. 基于SMDP模型的车路协同任务智能卸载算法研究[J]. 北京邮电大学学报, 2023, 46(2): 15-21.
[3]	付志远施丽琴叶迎晖卢光跃. 反向散射辅助的无线供能NOMA-MEC网络中公平性优化研究[J]. 北京邮电大学学报, 2023, 46(2): 71-77.
[4]	谢人超杨煜天唐琴琴陈清霞向雪霜. 低轨卫星网络星载边缘DNN推理策略研究[J]. 北京邮电大学学报, 2023, 46(2): 57-63.
[5]	蒋丽缪家辉郑镐谢正昊赖健鑫. 虚实映射误差条件下数字孪生辅助的无人机网络计算任务卸载及资源自适应优化机制[J]. 北京邮电大学学报, 2022, 45(6): 138-144.
[6]	李松王新荣王博文孙彦景陈瑞瑞. 基于随机网络演算的车联网边缘计算性能分析[J]. 北京邮电大学学报, 2022, 45(5): 16-22.
[7]	鲁蔚锋, 李学晴, 徐佳, 陈思光. 边缘计算架构中P2P的组合多资源任务卸载算法[J]. 北京邮电大学学报, 2022, 45(4): 98-103.
[8]	郅佳琳, 王楠, 满毅, 滕颖蕾. 面向硬件感知的边缘计算卸载和资源分配[J]. 北京邮电大学学报, 2022, 45(2): 22-28.
[9]	杜梅, 周军华, 李敦桥, 陈士钊, 魏翼飞. MEC计算卸载与资源分配联合智能优化方案[J]. 北京邮电大学学报, 2022, 45(2): 65-71.
[10]	张雨晴, 李云, 黄鸿锐, 庄宏成. 异构网络中任务卸载与资源分配联合优化算法[J]. 北京邮电大学学报, 2022, 45(2): 91-97.
[11]	高晓娜, 卢光跃, 叶迎晖, 昝金枚. 认知反向散射网络通信容量公平的资源优化[J]. 北京邮电大学学报, 2021, 44(6): 26-32.
[12]	郑远鹏, 张天魁, 朱光宇, 沈鸿. MEC系统中面向网络切片的3C联合资源分配算法[J]. 北京邮电大学学报, 2021, 44(5): 41-47.
[13]	王国卿. NDN数据提供者移动支持策略能耗建模与分析[J]. 北京邮电大学学报, 2021, 44(2): 75-80.
[14]	康曼聪, 李曦, 纪红, 张鹤立. 基于任务间依赖关系的小小区协作卸载策略[J]. 北京邮电大学学报, 2021, 44(1): 72-78.
[15]	薛建彬, 白子梅. 边缘计算中移动终端安全高效认证协议[J]. 北京邮电大学学报, 2021, 44(1): 110-116.

移动边缘计算中的时延和能耗均衡优化算法

A Delay and Energy Tradeoff Optimization Algorithm for Task Offloading in Mobile-Edge Computing Networks

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价