Task Scheduling Algorithm Based on Simulated Annealing Ant Colony Algorithm in Cloud Computing Environment

Zhang Hao-rong; Chen Ping-hua; Xiong Jian-bin

doi:10.3969/j.issn.1007-7162.2014.03.014

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Task Scheduling Algorithm Based on Simulated Annealing Ant Colony Algorithm in Cloud Computing Environment

更新时间：2026-05-27

- Task Scheduling Algorithm Based on Simulated Annealing Ant Colony Algorithm in Cloud Computing Environment
- Vol. 31, Issue 3, Pages: 77-82(2014)
- 作者机构：
- 作者简介：
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- DOI：10.3969/j.issn.1007-7162.2014.03.014
  CLC：
- Online First：30 September 2014，
  
  Published：2014
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张浩荣, 陈平华, 熊建斌. 基于蚁群模拟退火算法的云环境任务调度[J]. 广东工业大学学报, 2014, 31(3): 77-82.

Zhang Hao-rong, Chen Ping-hua, Xiong Jian-bin. Task Scheduling Algorithm Based on Simulated Annealing Ant Colony Algorithm in Cloud Computing Environment[J]. 2014, 31(3): 77-82.
张浩荣, 陈平华, 熊建斌. 基于蚁群模拟退火算法的云环境任务调度[J]. 广东工业大学学报, 2014, 31(3): 77-82. DOI： 10.3969/j.issn.1007-7162.2014.03.014.

Zhang Hao-rong, Chen Ping-hua, Xiong Jian-bin. Task Scheduling Algorithm Based on Simulated Annealing Ant Colony Algorithm in Cloud Computing Environment[J]. 2014, 31(3): 77-82. DOI： 10.3969/j.issn.1007-7162.2014.03.014.

摘要

针对云计算的MapReduce编程框架，提出一种融合蚁群算法和模拟退火算法的混合调度算法（ACOSA）.该算法以最小化调度时间为目标，引入了任务与资源的匹配因子和负载均衡度，先利用蚁群算法得到一组任务到资源的优化解，然后通过模拟退火算法对解进行路径的优化和信息素的更新.通过扩展Cloudsim云计算仿真平台，对其进行重新编译，实现了所提出的算法，实验结果表明该算法在调度时间、负载均衡等方面表现良好.

Abstract

It studies the task scheduling in cloud computing

and proposes a hybrid scheduling algorithm(ACOSA) combined with ant colony algorithm and simulated annealing algorithm for the MapReduce programming framework of cloud computing. This algorithm aims at minimizing the scheduling time and introduces the task and resource matching factors and load balance. Firstly

the ant colony algorithm was used to get the optimal solution to a set of tasks and resources. Then

the path was optimized

and the pheromone of solution was updated by the simulated annealing algorithm. Lastly

they were recompiled by extending Cloudsim cloud computing simulation platform

and the ACOSA algorithm was achieved. The experimental results show that the algorithm has a good performance in scheduling time and load balancing.

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