期刊名称：International Journal of Grid and Distributed Computing
印刷版ISSN：2005-4262
出版年度：2016
卷号：9
期号：4
页码：313-324
DOI：10.14257/ijgdc.2016.9.4.28
出版社：SERSC
摘要：There're huge numbers of users and various tasks need to be handled in the cloud computing environment, the high effective task scheduling algorithm is one of the crucial problems that the cloud computing need to solve. Aiming to the model structure of cloud computing, in this article it introduces the Particle Swarm Optimization algorithm (PSO) and Ant Colony Optimization algorithm (ACO) to combine with optimized task scheduling algorithm. First it takes the particle swarm optimization algorithm to generate the initial scheduling results, and introduces the random inertia weight to improve the scheduling ability of the algorithm, then to take the generated results of improved particle swarm optimization algorithm as the initial pheromones of the ant colony algorithm to find out the optimal scheduling scheme, and use the elitist strategy and crossover operator in the genetic algorithm to improve the ant colony algorithm, among the algorithms to use multistage optimization algorithm to improve the operating efficiency. The experimental results show that under the same conditions, the total task completion time of improved algorithm has been reduced and its performance advantage are getting more obvious with the increased task measures.
关键词：Cloud computing; Task scheduling; Improved ant colony algorithm; ; Improved particle swarm optimization algorithm; Total task completion time
