广东工业大学学报 ›› 2022, Vol. 39 ›› Issue (04): 17-23.doi: 10.12052/gdutxb.210177

• • 上一篇    下一篇

基于计算吞吐量最大化的能量采集边缘计算系统在线资源优化配置

王丰, 李宇龙, 林志飞, 崔苗, 张广驰   

  1. 广东工业大学 信息工程学院, 广东 广州 510006
  • 收稿日期:2021-11-08 出版日期:2022-07-10 发布日期:2022-06-29
  • 作者简介:王丰(1987–)，男，副教授，博士，主要研究方向为无线通信系统、移动边缘计算资源管理等，E-mail：fengwang13@gdut.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(61901124)；广东省自然科学基金资助项目(2021A1515012305)；广州市科技计划项目(202102020856)

An Online Resource Allocation Design for Computation Capacity Maximization in Energy Harvesting Mobile Edge Computing Systems

Wang Feng, Li Yu-long, Lin Zhi-fei, Cui Miao, Zhang Guang-chi   

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2021-11-08 Online:2022-07-10 Published:2022-06-29

HTML

PDF (PC)

2090

摘要： 在基于可再生能量收集技术的移动边缘计算(Mobile Edge Computing, MEC)系统中，可再生能量到达和计算卸载无线信道呈现较强的时空变化特性，因此该系统的无线及计算资源管理与用户任务计算之间存在着动态适配的挑战。针对此类问题，本文研究多时隙多用户的能量采集边缘计算系统，建立可再生能量随机到达和无线信道模型以及预测误差模型，以系统总计算吞吐量最大化为准则，通过逐时隙联合优化用户本地计算和计算卸载模块，提出了一种在线滑动窗设计方案, 需要通过调整滑动窗长度M来实现。该方案逐时隙求解凸优化问题，基于离线资源动态管控的最优结构，实时制定资源管理策略，具有较低的计算复杂度。仿真实验结果表明，提出的在线滑动窗设计方案在系统计算吞吐量性能方面优于已有的基准方案，并在对抗信道/能量状态信息预测误差方面有较好的鲁棒性能。

关键词: 移动边缘计算, 能量采集, 计算卸载, 在线滑动窗设计

Abstract: In the energy harvesting based mobile edge computing (MEC) system, the energy arrivals and wireless channels for computing offloading are both dynamically changing in time and space, which results in dynamic adaptation between communication/computational resource management and task execution. To address such problems, based on the criterion of maximizing the system’s computing throughput, the predication models for renewable energy random arrival and wireless channel are established, and a novel online design framework is proposed for dynamically managing communication/computation resources over time. This solution solves the convex optimization problem time slot by time slot, and based on the optimal structure of offline resource dynamic management and control, real-time resource management strategies are formulated, and it has low computational complexity. Numerical results show that the proposed online sliding window design scheme is superior to the existing benchmark schemes in terms of system computational throughput performance, and has better robust performance against channel/energy state information prediction errors.

Key words: mobile edge computing, energy harvesting, computation offloading, online sliding-window design

中图分类号: 

  • TN929.5
[1] MACH P, BECVAR Z. Mobile edge computing: a survey on architecture and computation offloading [J]. IEEE Communications Surveys and Tutorials, 2017, 19(3): 1628-1656.
[2] MAO Y, YOU C, ZHANG J, et al. A survey on mobile edge computing: the communication perspective [J]. IEEE Communications Surveys and Tutorials, 2017, 19(4): 2322-2358.
[3] 高志鹏, 尧聪聪, 肖楷乐. 移动边缘计算: 架构、应用和挑战[J]. 中兴通讯技术, 2019, 25(3): 23-30.
GAO Z P, YAO C C, XIAO K L. Mobile edge computing: architecture, applications, and challenges [J]. ZTE Technology Journal, 2019, 25(3): 23-30.
[4] WANG F, XU J, DING Z. Multi-antenna NOMA for computation off loading in multiuser mobile edge computing systems [J]. IEEE Transactions on Communications, 2019, 67(3): 2450-2463.
[5] YOU C, HUANG K, CHAE H. Energy efficient mobile cloud computing powered by wireless energy transfer [J]. IEEE Journal on Selected Areas in Communications, 2016, 34(5): 1757-1771.
[6] WANG F, XU J, WANG X, et al. Joint offloading and computing optimization in wire-less powered mobile-edge computing systems [J]. IEEE Transactions on Wireless Communications, 2018, 17(3): 1784-1797.
[7] BI S Z, ZHANG Y J. Computation rate maximization for wireless powered mobile-edge computing with binary computation offloading [J]. IEEE Transactions on Wireless Communications, 2018, 17(6): 4177-4190.
[8] WANG Y, SHENG M, WANG X, et al. Mobile edge computing: partial computation offloading using dynamic voltage scaling [J]. IEEE Transactions on Communications, 2016, 64(10): 4268-4282.
[9] CHEN X, JIAO L, LI W, et al. Efficient multi-user computation offloading for mobile-edge cloud computing [J]. IEEE/ACM Transactions on Networking, 2016, 24(5): 2795-2808.
[10] AO Y, ZHANG J, LETAIEF K. Dynamic computation offloading for mobile-edge computing with energy harvesting devices [J]. IEEE Journal on Selected Areas in Communications, 2016, 34(12): 3590-3605.
[11] XU J, CHEN L, REN S. Online learning for offloading and autoscaling in energy harvesting mobile edge computing [J]. IEEE Transactions on Cognitive Communications and Net- working, 2017, 3(3): 361-373.
[12] LIN Q, WANG F, XU J. Optimal task offloading scheduling for energy efficient D2D cooperative computing [J]. IEEE Communications Letters, 2019, 23(10): 1816-1820.
[13] WANG F, XU J, CUI S. Optimal energy allocation and task offloading policy for wire-less powered mobile edge computing systems [J]. IEEE Transactions on Wireless Communications, 2020, 19(4): 2443-2459.
[14] ZHOU F, WU F, HU R, et al. Computation rate maximization in UAV-enabled wireless- powered mobile-edge computing systems [J]. IEEE Journal on Selected Areas in Communications, 2018, 36(9): 1927-1941.
[15] OZEL O, TUTUNCUOGLU K, YANG J, et al. Transmission with energy harvesting nodes in fading wireless channels: optimal policies [J]. IEEE Journal on Selected Areas in Communications, 2011, 29(8): 1732-1743.
[16] HO C, ZHANG R. Optimal energy allocation for wireless communications with energy harvesting constraints [J]. IEEE Transactions on Signal Processing, 2012, 60(9): 4808-4818.
[17] MIN M, XIAO L, CHEN Y, et al. Learning based computation offloading for IoT devices with energy harvesting [J]. IEEE Transactions on Vehicular Technology, 2019, 68(2): 1930-1941.
[18] ZHANG G, CHEN Y, SHEN Z, et al. Distributed energy management for multiuser mobile edge computing systems with energy harvesting devices and QoS constraints [J]. IEEE Internet of Things Journal, 2019, 6(3): 4035-4048.
[19] ZHANG J, DU J, SHEN Y, et al. Dynamic computation offloading with energy harvesting devices: a hybrid-decision-based deep reinforce- ment learning approach [J]. IEEE Internet of Things Journal, 2020, 7(10): 9303-9317.
[20] BOYD S, VANDENBERGHE L. Convex optimization[M]. Cambridge: Cambridge University Press, 2004.
[1] 朱清华, 鹿安邦, 周俭铁, 侯艳. 改进多种群进化算法求解移动边缘计算中任务调度问题[J]. 广东工业大学学报, 2022, 39(04): 9-16.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发