Study on Resources Scheduling Based on ACO Allgorithm and PSO Algorithm in Cloud Computing

Author

Wen, Xiaotang ; Huang, Minghe ; Shi, Jianhua

Author_Institution

Sch. of Software, Jiangxi Normal Univ., Nanchang, China

fYear

2012

fDate

19-22 Oct. 2012

Firstpage

219

Lastpage

222

Abstract

It improves the algorithm because of the shortcoming that the ACO algorithm is easy to fall into local optimal solution in the cloud computing resource scheduling. The improved algorithm makes particle optimization inosculated into ant colony algorithm, which first finds out several groups of solutions using ACO algorithm according to the updated pheromone, and then gets more effective solutions using PSO algorithm to do crossover operation and mutation operation so as to avoid the algorithm prematurely into the local optimal solution.

Keywords

cloud computing; particle swarm optimisation; resource allocation; ACO algorithm; PSO algorithm; ant colony algorithm; cloud computing resource scheduling; crossover operation; local optimal solution; mutation operation; Algorithm design and analysis; Cloud computing; Particle swarm optimization; Processor scheduling; Resource management; Scheduling; Software algorithms; ACO algorithm; Cloud Computing; PSO algorithm; Resources Scheduling;

fLanguage

English

Publisher

ieee

Conference_Titel

Distributed Computing and Applications to Business, Engineering & Science (DCABES), 2012 11th International Symposium on

Conference_Location

Guilin

Print_ISBN

978-1-4673-2630-8

Type

conf

DOI

10.1109/DCABES.2012.63

Filename

6385275