Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing

Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing

Task resource management is important in cloud computing system. It's necessary to find the efficient way to optimize scheduling in cloud computing. In this paper, an optimized particle swarm optimization (PSO) algorithms with adaptive change of parameter (viz., inertial weight and acceleration...

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Main Authors: Shuang Zhao, Xianli Lu, Xuejun Li
Format: Article
Language:English
Published: EDP Sciences 2015-01-01
Series:MATEC Web of Conferences
Online Access:http://dx.doi.org/10.1051/matecconf/20152806001
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spelling doaj-67d3c8e0b2e74accaf8496a5ffb6e9c22021-02-02T00:27:24ZengEDP SciencesMATEC Web of Conferences2261-236X2015-01-01280600110.1051/matecconf/20152806001matecconf_icame2015_06001Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud ComputingShuang ZhaoXianli LuXuejun LiTask resource management is important in cloud computing system. It's necessary to find the efficient way to optimize scheduling in cloud computing. In this paper, an optimized particle swarm optimization (PSO) algorithms with adaptive change of parameter (viz., inertial weight and acceleration coefficients) according to the evolution state evaluation is presented. This adaptation helps to avoid premature convergence and explore the search space more efficiently. Simulations are carried out to test proposed algorithm, test reveal that the algorithm can achieving significant optimization of makespan.http://dx.doi.org/10.1051/matecconf/20152806001
collection DOAJ
language English
format Article
sources DOAJ
author Shuang Zhao
Xianli Lu
Xuejun Li
spellingShingle Shuang Zhao
Xianli Lu
Xuejun Li
Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing
MATEC Web of Conferences
author_facet Shuang Zhao
Xianli Lu
Xuejun Li
author_sort Shuang Zhao
title Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing
title_short Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing
title_full Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing
title_fullStr Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing
title_full_unstemmed Particle Swarm Optimization with Time Varying Parameters for Scheduling in Cloud Computing
title_sort particle swarm optimization with time varying parameters for scheduling in cloud computing
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2015-01-01
description Task resource management is important in cloud computing system. It's necessary to find the efficient way to optimize scheduling in cloud computing. In this paper, an optimized particle swarm optimization (PSO) algorithms with adaptive change of parameter (viz., inertial weight and acceleration coefficients) according to the evolution state evaluation is presented. This adaptation helps to avoid premature convergence and explore the search space more efficiently. Simulations are carried out to test proposed algorithm, test reveal that the algorithm can achieving significant optimization of makespan.
url http://dx.doi.org/10.1051/matecconf/20152806001
work_keys_str_mv AT shuangzhao particleswarmoptimizationwithtimevaryingparametersforschedulingincloudcomputing
AT xianlilu particleswarmoptimizationwithtimevaryingparametersforschedulingincloudcomputing
AT xuejunli particleswarmoptimizationwithtimevaryingparametersforschedulingincloudcomputing
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