[1]王俊英,颜芬芬,陈鹏,等.基于概率自适应蚁群算法的云任务调度方法[J].郑州大学学报(工学版),2017,38(04):51-56.[doi:10.13705/j.issn.1671-6833.2017.01.018]
　Wang Junying,Yan Fenfen,Chen Peng,et al.Task Scheduling Method Based on Probabilistic Adaptive Ant Colony Optimization in Cloud Computing[J].Journal of Zhengzhou University (Engineering Science),2017,38(04):51-56.[doi:10.13705/j.issn.1671-6833.2017.01.018]
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基于概率自适应蚁群算法的云任务调度方法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
38
期数:
2017年04期
页码:
51-56
栏目:
出版日期:
2017-07-18

文章信息/Info

Title:
Task Scheduling Method Based on Probabilistic Adaptive Ant Colony Optimization in Cloud Computing
作者:
王俊英; 颜芬芬; 陈鹏; 董方敏; 臧兆祥
1.三峡大学计算机与信息学院,湖北宜昌443002;2.三峡大学湖北省水电工程智能视觉监测重点实验室,湖北宜昌443002
Author(s):
Wang Junying1; Yan Fenfen1; Chen Peng2; Dong Fangmin1; Zang Zhaoxiang2
1. School of Computer and Information, Three Gorges University, Yichang, Hubei 443002; 2. Key Laboratory of Intelligent Visual Monitoring for Hydropower Engineering, Three Gorges University, Yichang, Hubei 443002 
关键词:
Keywords:
DOI:
10.13705/j.issn.1671-6833.2017.01.018
文献标志码:
A
摘要:
针对基本蚁群算法在求解云任务调度问题时易陷入局部最优的不足,提出一种任务分配概率自适应的蚁群算法.算法根据任务量的大小对任务进行降序排序.定义了任务分配集中度,引入了概率自适应调整因子对任务分配过于集中的资源节点的分配概率进行调整.结果表明,相对基本蚁群算法及改进蚁群算法,该算法有效地缩短了任务完成时间,且算法的执行效率、收敛速度均有一定程度的改善.
Abstract:
The basic ant colony algorithm tended to be trapped in local optimum in solving task scheduling problems of cloud computing.A probability adaptive ant colony optimization was proposed.This algorithm rankd the tasks in descending order according to their size,defines the task concentration degree,and introduces the probability adaptive adjustment factor to adjust the assignment probability of over-concentrated resource noded.The results showed that the proposed algorithm shortened the task completion time,and had some improvements on convergence speed,compared with the Ant Colony Optimization and Modified Ant Colony Optimization.
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