A Multi-User Fine-Grained Task Offloading Scheduling Approach of Mobile Edge Computing

CUI Yu-ya; ZHANG De-gan; ZHANG Ting; YANG Peng; ZHU Hao-li

doi:10.12263/DZXB.20201039

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A Multi-User Fine-Grained Task Offloading Scheduling Approach of Mobile Edge Computing

PAPERS | 更新时间：2025-12-08

- A Multi-User Fine-Grained Task Offloading Scheduling Approach of Mobile Edge Computing
- ACTA ELECTRONICA SINICA Vol. 49, Issue 11, Pages: 2202-2207(2021)
- 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：
- 基金信息：
- DOI：10.12263/DZXB.20201039
  CLC： TP393
- Received：21 September 2020，
  
  Revised：2021-05-13，
  
  Published：25 November 2021
- 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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