Research on energy management of multi-user mobile edge computing offloading

Luyao WANG; Wenqian ZHANG; Guanglin ZHANG

doi:10.11959/j.issn.2096-3750.2019.00091

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Research on energy management of multi-user mobile edge computing offloading

Theory and Technology | 更新时间：2024-06-05

- Research on energy management of multi-user mobile edge computing offloading
- Chinese Journal on Internet of Things Vol. 3, Issue 1, Pages: 73-81(2019)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：10.11959/j.issn.2096-3750.2019.00091
  CLC： TN929.5
- Published：30 March 2019，
  
  Published Online：2019-03，
- 稿件说明：
移动端阅览
LUYAO WANG, WENQIAN ZHANG, GUANGLIN ZHANG. Research on energy management of multi-user mobile edge computing offloading. [J]. Chinese journal on internet of things, 2019, 3(1): 73-81.
DOI：

LUYAO WANG, WENQIAN ZHANG, GUANGLIN ZHANG. Research on energy management of multi-user mobile edge computing offloading. [J]. Chinese journal on internet of things, 2019, 3(1): 73-81. DOI： 10.11959/j.issn.2096-3750.2019.00091.

摘要

在移动边缘计算系统中，通过将计算任务从移动设备迁移到移动边缘计算服务器，可以大幅度提高计算质量。考虑将可再生能源纳入多用户移动边缘计算系统中，并在模型中加入电池作为能量收集装置以实现能量收集和存储。通过提出的基于强化学习的资源管理算法制定了移动边缘计算系统中的任务分配策略，实现了移动设备成本最优化（包括时延成本和能耗成本）。仿真结果表明，与其他算法相比，该算法显著减少了移动设备的成本。

Abstract

In mobile edge computing system

the quality of computing experience can be improved greatly by offloading computing tasks from mobile devices to mobile edge computing servers.Consider incorporating renewable energy into a multi-user mobile edge system.Moreover

a battery as an energy harvesting device was added to the model to harvest energy and storage.The task allocation strategy in mobile edge computing system was formulated through the resource management algorithm based on reinforcement learning

which achieved the cost minimization of mobile devices (including delay cost and computing cost).The simulation results show that the proposed algorithm significantly minimizes the cost of mobile devices compared with other algorithms.

关键词

能量收集可再生能源移动边缘计算成本优化强化学习

Keywords

energy harvestingrenewable energymobile edge computingcost optimizationreinforcement learning

references

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