计算机工程Issue(2)：12-16,5.DOI:10.3969/j.issn.1000-3428.2015.02.003
基于改进蚁群算法的云计算任务调度模型
Cloud Computing Task Scheduling Model Based on Improved Ant Colony Algorithm
摘要
Abstract
To solve the problem of resource scheduling problem in cloud computing, a parallel scheduling model is proposed,which can improve the task parallelism while maintaining the serial relationships between tasks. Dynamic tasks submitted by users are divided into sub-tasks in some serial sequences,and it puts into scheduling queue with different priorities according to running order. For these tasks in the same priority scheduling queue, an improved Delay Time Shortest and Fairness Ant Colony Optimization(DSFACO) algorithm is applied to schedule. Considering both fairness and efficiency,DSFACO algorithm applies to subtask scheduling problem to realize shortest delay time,thus improves the user satisfaction. Experimental results show DSFACO algorithm is better than the TS-EACO algorithm in fairness, efficiency and task delay time,and it can realize the optimal scheduling in cloud computing.关键词
云计算/蚁群算法/任务调度/公平性/任务延迟时间Key words
cloud computing/ant colony algorithm/task scheduling/fairness/task delay time分类
信息技术与安全科学引用本文复制引用
魏赟,陈元元..基于改进蚁群算法的云计算任务调度模型[J].计算机工程,2015,(2):12-16,5.基金项目
国家自然科学基金资助项目(61170277) （61170277）
上海市教委科研创新基金资助项目(12YZ094)。 （12YZ094）
