Journal of Frontiers of Computer Science and Technology ›› 2022, Vol. 16 ›› Issue (2): 480-488.DOI: 10.3778/j.issn.1673-9418.2009072

• Theory and Algorithm • Previous Articles    

Energy Balancing for Multiple Devices with Multiple Tasks in Mobile Edge Computing

PANG Yuan, WU Jigang, CHEN Long+(), YAO Mianyang   

  1. School of Computer, Guangdong University of Technology, Guangzhou 510006, China
  • Received:2020-09-02 Revised:2020-11-11 Online:2022-02-01 Published:2020-12-17
  • About author:PANG Yuan, born in 1994, M.S. candidate. His research interests include edge computing and machine learning.
    WU Jigang, born in 1963, Ph.D., distinguished professor at Guangdong University of Techno-logy, member of CCF. His research interests include network computing, cloud computing, machine intelligence and reconfigurable architecture.
    CHEN Long, born in 1988, Ph.D., associate professor, M.S. supervisor. His research interests include mobile cloud computing, Internet of vehicles, cognitive radio networks and network economics.
    YAO Mianyang, born in 1997, M.S. candidate. His research interests include mobile edge com-puting and distributed machine learning.
  • Supported by:
    National Natural Science Foundation of China(62072118);National Natural Science Foundation of China(61702115);Key-Area Research and Development Program of Guangdong Province(2019B010118001);Key-Area Research and Development Program of Guangdong Province(2019B010121001)


庞源, 武继刚, 陈龙+(), 姚棉阳   

  1. 广东工业大学 计算机学院,广州 510006
  • 通讯作者: + E-mail:
  • 作者简介:庞源(1994—),男,广东湛江人,硕士研究生,主要研究方向为边缘计算、机器学习。
  • 基金资助:


With the development of technology, mobile edge computing is facing the challenge of energy balancing of multi-device and multi-task. Related research mostly focuses on how to utilize the computing performance of edge servers to reduce the energy consumption and execution time of mobile devices during task processing. However, the existing research has not yet a good solution to the problem of multi-device and multi-task energy balancing. Aiming at this kind of energy balancing problem, this paper improves the existing edge computing system model, and gives a calculation model for the energy balancing optimization problem of multi-device and multi-task. At the same time, a greedy algorithm is proposed and the corresponding approximate ratio analysis is made. In addition, this paper is compared with the total energy consumption minimization algorithm and the random algorithm, and a large number of simulation experiments are conducted. Experimental results reveal that the average performance of the proposed greedy algorithm can be further improved by 66.59% in terms of energy balancing than the random algorithm. Compared with the brute force algorithm, under the classic task topology, when the minimum transmission power of the mobile device is 5 dBm and 6 dBm respectively, the greedy algorithm almost obtains the optimal solution.

Key words: mobile edge computing, task offloading, energy balancing, greedy algorithm


移动边缘计算技术随着科技的发展,面临着多设备多任务的能耗均衡的挑战。相关研究大多集中在如何利用边缘服务器的计算性能以减少移动设备在任务处理过程中的能耗和执行时间。但现有研究在多设备多任务的能耗均衡问题上还没有很好的解决方法。针对此类能耗均衡问题,改进了现有的边缘计算系统模型,并在此基础上,给出了多移动设备多任务的能耗均衡优化问题的计算模型,同时提出了一个贪心算法,并做出了相应的近似比分析。与总能耗优化算法以及随机算法进行对比,并进行了大量的仿真实验。实验结果证明,所提出的贪心算法的平均性能与随机算法相比在能耗均衡方面可进一步提升66.59%。通过与蛮力算法对比,在经典的任务拓扑下,当移动设备的最小传输功率分别为5 dBm和6 dBm时,贪心算法几乎获得最优解。

关键词: 移动边缘计算, 任务卸载, 能耗均衡, 贪心算法

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