Cloud Task Scheduling Based on Chaotic Particle Swarm Optimization Algorithm

In cloud computing environment, there is a large quantity of submitted tasks by users. How to schedule these massive tasks efficiently and reasonably becomes a serious challenge. This paper proposes a Chaotic Particle Swarm Optimization algorithm (CPSO) to overcome the problems of Standard Particle...

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Bibliographic Details
Published in2016 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS) pp. 493 - 496
Main Authors Li Yingqiu, Li Shuhua, Gao Shoubo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2016
Subjects
Algorithm design and analysis
Chaotic Mutation
Chaotic Sequence
Cloud computing
Computational modeling
Convergence
Optimization
Particle swarm optimization
Particle Swarm Optimization Algorithm
Processor scheduling
Task Scheduling
Online AccessGet full text
DOI10.1109/ICITBS.2016.28

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Summary:In cloud computing environment, there is a large quantity of submitted tasks by users. How to schedule these massive tasks efficiently and reasonably becomes a serious challenge. This paper proposes a Chaotic Particle Swarm Optimization algorithm (CPSO) to overcome the problems of Standard Particle Swarm algorithm such as premature convergence and low accuracy. Firstly, in initial process, chaotic sequence is introduced to enhance the diversity of particles. Then, an effective diagnosis mechanism of premature is adopted to determine local convergence and algorithm correction is performed by chaotic mutation, which could activate the particles in stagnation and make them escape from local optimum. Simulation experiments show that the proposed approach is feasible and effective.
DOI:10.1109/ICITBS.2016.28