一种面向移动边缘计算的多用户细粒度任务卸载调度方法

崔玉亚; 张德干; 张婷; 杨鹏; 朱浩丽

doi:10.12263/DZXB.20201039

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一种面向移动边缘计算的多用户细粒度任务卸载调度方法

学术论文 | 更新时间：2025-12-08

- 一种面向移动边缘计算的多用户细粒度任务卸载调度方法
- A Multi-User Fine-Grained Task Offloading Scheduling Approach of Mobile Edge Computing
- 电子学报 2021年49卷第11期页码：2202-2207
- 作者机构：
  
  1.天津理工大学天津市智能计算及软件新技术重点实验室, 天津 300384
  2.天津理工大学计算机视觉与系统省部共建教育部重点实验室, 天津 300384
  3.天津体育学院体育经济与管理学院，天津 301617
- 作者简介：
  
  [ "崔玉亚男，1992年生，江苏淮安人，天津理工大学计算机科学与工程学院在读博士生，研究兴趣为网络通信、物联网、无线传感器网络、移动边缘计算等．E-mail:844511468@qq.com" ]
  [ "张德干男，1970年生，湖北黄冈人，天津理工大学计算机科学与工程学院教授/博士，博导，研究兴趣为物联网、无线传感器网络、移动边缘计算、云计算等.E-mail:zhangdegan@tsinghua.org.cn" ]
  [ "张婷(通信作者) 女，1972年，河北唐山人，天津体院学院体育经济与管理学院教授/博士，硕导，研究兴趣为物联网、无线传感器网络、移动边缘计算、大数据等.E-mail:zhangtingts@163.com" ]
- 基金信息：
  
  国家自然科学基金(61571328);天津市重大科技专项(15ZXDSGX00050;16ZXFWGX00010);天津市科技支撑重点项目(17YFZCGX00360);天津市自然科学基金(18JCZDJC96800;18JCYBJC19300);天津市科技创新和131人才团队(TD13-5025;2015-23)
- DOI：10.12263/DZXB.20201039
  中图分类号： TP393
- 收稿：2020-09-21，
  
  修回：2021-05-13，
  
  纸质出版：2021-11-25
- 稿件说明：
移动端阅览
崔玉亚,张德干,张婷等.一种面向移动边缘计算的多用户细粒度任务卸载调度方法[J].电子学报,2021,49(11):2202-2207.

CUI Yu-ya,ZHANG De-gan,ZHANG Ting,et al.A Multi-User Fine-Grained Task Offloading Scheduling Approach of Mobile Edge Computing[J].ACTA ELECTRONICA SINICA,2021,49(11):2202-2207.
崔玉亚,张德干,张婷等.一种面向移动边缘计算的多用户细粒度任务卸载调度方法[J].电子学报,2021,49(11):2202-2207. DOI： 10.12263/DZXB.20201039.

CUI Yu-ya,ZHANG De-gan,ZHANG Ting,et al.A Multi-User Fine-Grained Task Offloading Scheduling Approach of Mobile Edge Computing[J].ACTA ELECTRONICA SINICA,2021,49(11):2202-2207. DOI： 10.12263/DZXB.20201039.

摘要

在移动边缘计算中(Mobile Edge Computing

MEC)，任务卸载可以有效地解决移动设备资源受限的问题，但是将全部任务都卸载到边缘服务器并非最优.本文提出一种面向移动边缘计算的多用户细粒度任务卸载调度新方法，把计算任务看作一个有向无环图(Directed Acyclic Graph

DAG)，对节点的执行位置和调度顺序进行了优化决策.考虑系统的延迟把计算卸载看作一个约束多目标优化问题(Constrained Multi-object Optimization Problem

CMOP)，提出了一个改进的NSGA-Ⅱ算法来解决CMOP.所提出的算法能够实现本地和边缘的并行处理从而减少延迟.实验结果表明，算法能够在实际应用程序中做出最优决策.

Abstract

In mobile edge computing (MEC)

task offloading can solve the problem of resource constraint on mobile devices effectively

but it is not optimal to offload all tasks to edge servers. In this paper

a multi-user fine-grained task offloading scheduling approach of mobile edge computation is proposed. The computation task is regarded as a directed acyclic graph (DAG)

and task nodes’ execution location and scheduling order are optimized. Considering the delay of the system

the computation offloading is considered as a constrained multi-objective optimization problem (CMOP)

and an improved NSGA -Ⅱ algorithm is proposed to solve the CMOP. The proposed algorithm can realize local and edge parallel processing to reduce delay. The experimental results show that the algorithm can make the optimal decision in practical applications.

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references

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