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

doi:10.12052/gdutxb.210177

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

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

更新时间：2026-05-27

- An Online Resource Allocation Design for Computation Capacity Maximization in Energy Harvesting Mobile Edge Computing Systems
- Vol. 39, Issue 4, Pages: 17-23(2022)
- 作者机构：
  
  广东工业大学信息工程学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.210177
  CLC： TN929.5
- Online First：10 July 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
王丰, 李宇龙, 林志飞, 崔苗, 张广驰. 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[J]. 广东工业大学学报, 2022, 39(4): 17-23.

Wang Feng, Li Yu-long, Lin Zhi-fei, et al. An Online Resource Allocation Design for Computation Capacity Maximization in Energy Harvesting Mobile Edge Computing Systems[J]. 2022, 39(4): 17-23.
王丰, 李宇龙, 林志飞, 崔苗, 张广驰. 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[J]. 广东工业大学学报, 2022, 39(4): 17-23. DOI： 10.12052/gdutxb.210177.

Wang Feng, Li Yu-long, Lin Zhi-fei, et al. An Online Resource Allocation Design for Computation Capacity Maximization in Energy Harvesting Mobile Edge Computing Systems[J]. 2022, 39(4): 17-23. DOI： 10.12052/gdutxb.210177.

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

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。

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.

关键词

Keywords

references

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.

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.

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.

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.

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.

WANG F, XU J, WANG X, et al . Joint offloading and computing optimization in wire-less powered mobile-edg e computing systems [J ] . IEEE Transactions on Wireless Communications, 2018, 17(3): 1784-1797.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

BOYD S, VANDENBERGHE L. Convex optimization[M]. Cambridge: Cambridge University Press, 2004.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Optimized Design and Resource Allocation for Dual-server Mobile Edge Computing Systems

Optimized Design for Multiuser Cache-enabled Mobile Edge Computing

Double Reconfigurable Intelligent Surface-aided Green Internet of Things Edge Computing for Research on Computation Capacity

Computing Delay Minimization for UAV-enabled Mobile Edge Computing Systems with URLLC-based Offloading

An Improved Multi-population Evolutionary Algorithm for Task Scheduling in a Mobile Edge Computing Environment

Related Author

Wang Feng

Liang Jing-xuan

Li Yu-Long

Zeng Xiang

Chen Yan-Long

Cui Miao

Wu Qing-jie

Related Institution

School of Computer Science and Technology, Guangdong University of Technology

AI问答

⁰