一种DAG云任务多目标调度算法

郑宇超; 夏学文; 艾冬梅

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一种DAG云任务多目标调度算法

更新时间：2026-09-04

- 一种DAG云任务多目标调度算法
- A Multi-objective Scheduling Algorithm of DAG Cloud Tasks
- 微电子学与计算机 2018年35卷第8期页码：48-54
- 作者机构：
  
  1. 华东交通大学机构软件学院,江西,南昌,330013
  2. 北京科技大学数理学院,北京,100083
- 作者简介：
- 基金信息：
  
  江西省教育厅科研项目 GJJ150539
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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郑宇超, 夏学文, 艾冬梅. 一种DAG云任务多目标调度算法[J]. 微电子学与计算机, 2018, 35(8): 48-54.

ZHENG Yu-chao, XIA Xue-wen, AI Dong-mei. A Multi-objective Scheduling Algorithm of DAG Cloud Tasks[J]. 2018, 35(8): 48-54.
郑宇超, 夏学文, 艾冬梅. 一种DAG云任务多目标调度算法[J]. 微电子学与计算机, 2018, 35(8): 48-54. DOI：

ZHENG Yu-chao, XIA Xue-wen, AI Dong-mei. A Multi-objective Scheduling Algorithm of DAG Cloud Tasks[J]. 2018, 35(8): 48-54. DOI：

摘要

提出了一种基于模糊占优排序的DAG任务调度离散粒子群算法FDS-PSO.首先

构建了预算与截止期限双QoS约束的多目标调度优化模型

模型构建了任务执行时间、执行代价及调度可靠性的多目标优化指标; 然后

设计了一种离散PSO算法求解多冲突目标的均衡最优化问题

算法通过模糊占优排序的形式量化多目标解的相对适应度

进而得到满足帕累托最优的调度均衡解集合; 最后

通过随机工作流结构实验仿真

与同类智能群体多目标调度算法进行性能比较.仿真结果证明

FDS-PSO算法不仅求解调度解的收敛速度更快

且解空间分布更均匀

能以更高的效率实现多目标的均衡优化.

Abstract

A DAG tasks scheduling algorithm FDS-PSO of discrete particle swarm optimization based on the fuzzy dominance sort is presented. First

the multi-objective optimization model of workflow scheduling under bi-QoS with the budget and deadline constraint is established

which introduces three optimizaiton objectives

including the execution makespan

the execution cost and the scheduling reliability. Second

a discrete particle swarm optimizaiton algorithm is designed to solve this three conflicting objectives optimization. FDS-PSO can quantify the relative fitness of multi-objective solutions through the fuzzy dominance sorting and obtain the solutions set of workflow scheduling satisfying Pareto optimal. Finally

through the simulation experiments of generated randomly workflow

compared with the same types of intelligent swarm multi-objective scheduling algorithms.Experimental results show that the scheduling solution obtained by FDS-PSO not noly has better convergence

but has better and uniform spacing distribution among solutions

which can better achieve multi-objective trade-off optimization.

关键词

Keywords

references

Zheng W, Sakellariou R. Budget-deadline constrained workflow planning for admission control[J]. Journal of Grid Computing, 2013, 11(4):633-651. DOI: 633

Beegom A, Rajasree M. A particle swarm optimization based pareto optimal task scheduling in cloud computing[C]//International Conference in Swarm Intelligence. Hefei, China, Springer International Publishing, 2014: 79-86.

Fard H, Prodan R, Barrionuevo J, et al. A multi-objective approach for workflow scheduling in heterogeneous environments[C]//Ieee/acm International Symposium on Cluster, Cloud and Grid Computing. Ottawa, Canada, ACM, 2012: 300-309.

Durillo J, NaeV, Prodan R. Multi-objective energy-efficient workflow scheduling using list-based heuristics[J]. Future Generation Computer Systems, 2014, 36(3):221-236. DOI: 221

Su S, Li J, Huang Q, et al. Cost-efficient task scheduling for executing large programs in the cloud[J]. Parallel Computing, 2013, 39(4/5):177-188. DOI: 177

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Smanchat S, Viriyapant K. Taxonomies of workflow scheduling problem and techniques in the cloud[J]. Future Generation Computer Systems, 2015, 52(3):1-12. DOI: 1

Garg R, Kumar Singh A. Multi objective optimization to workflow grid scheduling using reference point based evolutionary algorithm[J]. Journal of Computer Applications, 2012, 22(6):44-49. DOI: 44

Verma A, Kaushal S. Cost-time efficient scheduling plan for executing workflows in the cloud[J]. Journal of Grid Computing, 2015, 13(4):1-12. DOI: 1

Durillo J, Prodan R, Barbosa G. Pareto tradeoff scheduling of workflows on federated commercial Clouds[J]. Simulation Modelling Practice % Theory, 2015, 58(1):95-111. DOI: 95

李静梅, 尤晓非, 韩启龙.基于任务复制的多关键路径任务调度算法[J].计算机工程与设计, 2014, 35(5):1639-1645. DOI: 1639

Topcuoglu H, Hariri S, Wu M Y, Performance-effective and low-complexitytask scheduling for heterogeneous computing[J]. IEEE Transactions Parallel Distributed Systems, 2012, 13(3):260-274. DOI: 260

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