基于粒子群优化的云工作流任务调度

薛凡; 吴志健

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基于粒子群优化的云工作流任务调度

更新时间：2026-09-04

- 基于粒子群优化的云工作流任务调度
- Cloud Workflow Tasks Scheduling Based on Particle Swarm Optimization
- 微电子学与计算机 2018年35卷第8期页码：122-127
- 作者机构：
  
  1. 武汉大学计算机学院软件工程国家重点实验室机构,湖北,武汉,430072
  2. 黄淮学院机构创新创业学院,河南,驻马店,463000
- 作者简介：
- 基金信息：
  
  国家自然科学基金项目 61364025国家自然科学基金项目 61402481武汉大学软件工程国家重点实验室开放基金项目 SKLSE2014-10-4
- DOI：
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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薛凡, 吴志健. 基于粒子群优化的云工作流任务调度[J]. 微电子学与计算机, 2018, 35(8): 122-127, 131.

XUE Fan, WU Zhi-jian. Cloud Workflow Tasks Scheduling Based on Particle Swarm Optimization[J]. 2018, 35(8): 122-127.
薛凡, 吴志健. 基于粒子群优化的云工作流任务调度[J]. 微电子学与计算机, 2018, 35(8): 122-127, 131. DOI：

XUE Fan, WU Zhi-jian. Cloud Workflow Tasks Scheduling Based on Particle Swarm Optimization[J]. 2018, 35(8): 122-127. DOI：

摘要

为了解决云环境中科学工作流调度的代价优化问题

提出一种基于改进粒子群优化模型的科学工作流调度算法CWS_PSO.该算法将科学工作流调度问题形式化为截止时间约束下的调度代价最优化问题

利用粒子群模型建立了粒子位置与任务调度方案的编码与转换模型

并通过粒子的迭代进化寻找满足截止时间约束的代价最小化调度方案.通过科学工作流模型的仿真实验

与同类型调度算法进行了性能比较.实验结果表明

CWS_PSO算法不仅可以有效均衡工作流的执行跨度与执行代价

而且能以更高的截止时间约束满意度实现工作流执行代价的最小化.

Abstract

For solving the cost optimization of the scientific workflow scheduling in cloud environment

a scientific workflow scheduling algorithm CWS_PSO based on improved particle swarm optimization (PSO) model is presented in this paper. Our algorithm formalizes the scientific workflow scheduling problem as the scheduling cost optimization problem with the deadline constraint. The encoding and transfer model are constructed between the particles position and tasks scheduling scheme by PSO model

and searching for the cost optimization scheme satisfying the deadline constraint is processed by the particles iteration evoluation. Through the simulation experimental of scientific workflows model

the performance comparisions are conducted in the same kind of algorithms. Experimental results show that our algorithm could efficiently achieve the trade-off between the workflow execution makespan and the execution cost

and achieve the execution cost minimization with the deadline constraint satisfaction.

关键词

Keywords

references

Liu L, Zhang M, Lin Y, et.al. A survey on workflow management and scheduling in cloud computing[C]\\In 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, IEEE, 2014: 837-846.

Chen W, WorkflowSim: a toolkit for simulating scientific workflows in distributed environments[C]\\In IEEE 8th International Conference on E-Science, IEEE, 2012: 1-8.

Clerc M. Particle Swarm Optimization[M]// Fractional Order Darwinian Particle Swarm Optimization. Springer International Publishing, 2016: 149-150.

Wu F, Wu Q, Tan Y. Workflow scheduling in cloud: a survey[J]. The Journal of Supercomputing, 2015, 71(9):3373-3418. DOI: 3373

M. Mao and M. Humphrey, Auto-scaling to minimize cost and meet application deadlines in cloud workflows[C]\\in Proceeding International Conference on High Performance Computing, Network, Storage Anal., IEEE, 2012: 1-12.

M. Malawski, G. Juve, Cost-and deadline-constrained provisioning for scientific workflow ensembles in IaaS clouds[C]\\in Proceeding on High Performance Computing, Network, Storage Anal., IEEE, 2012: 1-11.

M.Rodriguez, R.Buyya, Deadline based resource provisioning and scheduling algorithm for scientific workflows on Clouds[J], IEEE Transactions on Cloud Computing, 2014, 2(2):222-235. DOI: 222

S. Abrishami, D. Epema, Deadline-constrained workflow scheduling algorithms for IaaS clouds[J], Future Generation Computer System, 2012, 23(8):1400-1414. DOI: 1400

G. Juve, A. Chervenak, E. Deelman, et.al, Characterizing and profiling scientific workflows[J], Future Generation Computer System, 2012, 29(3):682-692. DOI: 682

郑敏, 曹健, 姚艳, 面向价格动态变化的云工作流调度算法[J], 计算机集成制造系统, 2013, 19(8):1849-1858. DOI: 1849

Wang X, Yeo CS, Buyya R, Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm[J]. Future Generation Computer System, 2012, 27(8):1124-1134. DOI: 1124

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