云工作流任务调度的模拟退火遗传改进算法

黄婷婷; 梁意文

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云工作流任务调度的模拟退火遗传改进算法

更新时间：2026-09-04

- 云工作流任务调度的模拟退火遗传改进算法
- An Improved Simulated Annealing Genetic Algorithm for Workflow Scheduling in Cloud Platform
- 微电子学与计算机 2016年33卷第1期页码：42-46
- 作者机构：
  
  武汉大学计算机学院,湖北,武汉,430072
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61170306
- DOI：
  中图分类号：
- 纸质出版：2016
- 稿件说明：
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黄婷婷, 梁意文. 云工作流任务调度的模拟退火遗传改进算法[J]. 微电子学与计算机, 2016, 33(1): 42-46.

HUANG Ting-ting, LIANG Yi-wen. An Improved Simulated Annealing Genetic Algorithm for Workflow Scheduling in Cloud Platform[J]. 2016, 33(1): 42-46.
黄婷婷, 梁意文. 云工作流任务调度的模拟退火遗传改进算法[J]. 微电子学与计算机, 2016, 33(1): 42-46. DOI：

HUANG Ting-ting, LIANG Yi-wen. An Improved Simulated Annealing Genetic Algorithm for Workflow Scheduling in Cloud Platform[J]. 2016, 33(1): 42-46. DOI：

摘要

提出一种模拟退火(simulated annealing

SA)算法和遗传算法(genetic algorithm

GA)相结合的任务完成时间-可靠性的多目标优化算法.该算法首先利用任务的影响程度生成合适的初始种群; 其次对交叉、变异等遗传操作产生出的个体分别进行模拟退火操作

避免随机算法引起的早熟收敛问题; 最后

在变异阶段引入失败率

提高调度结果的可靠性.实验分析表明

该算法在解决早熟收敛和结果不可靠方面比GA性能更优.

Abstract

This paper proposed a multi-objective optimization algorithm combining simulated annealing algorithm with the genetic algorithm concerning makespan and reliability. Firstly

this algorithm makes tasks prioritization considering their influence degree tasks schedule policy to generates a suitable initial population. Secondly

for individuals which produced through crossover and mutation genetic operations to perform simulated annealing operation

in order to avoid the premature convergence problem caused by random algorithm. Then

it introduced failure rate in mutation phase to increase the reliability of the scheduling results. Experimental analysis indicates that the algorithm in solving the problem of premature convergence and result unreliable better than GA.

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references

Calheiros R N, Ranjan R, De Rose C A F, et al. Cloudsim: A novel framework for modeling and simulation of cloud computing infrastructures and services[J]. Computing Research Repository, 2009, 9(3): 2-10. DOI: 2

Delavar A G, Aryan Y. A goal-oriented workflow scheduling in heterogeneous distributed systems[J]. Int J Comput Appl, 2012, 52(8): 27-33. DOI: 27

Sonnek J, Chandra A, Weissman J B. Adaptive reputation-based scheduling on unreliable distributed infrastructures[J]. Parallel and Distributed Systems, IEEE Transactions on, 2007, 18(11): 1551-1564. DOI: 1551

Hakem M, Butelle F. Reliability and scheduling on systems subject to failures[C]//Parallel Processing, 2007. ICPP 2007. International Conference on. China, Xi'an, IEEE, 2007: 38-38.

Delavar A G, Aryan Y. HSGA: a hybrid heuristic algorithm for workflow scheduling in cloud systems[J]. Cluster computing, 2014, 17(1): 129-137. DOI: 129

Wang X, Yeo C S, Buyya R, et al. Optimizing the makespan and reliability for workflow applications with reputation and a look-ahead genetic algorithm[J]. Future Generation Computer Systems, 2011, 27(8): 1124-1134. DOI: 1124

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