Optimized task scheduling and resource allocation on cloud computing environment using improved differential evolution algorithm

The objective of this study is to optimize task scheduling and resource allocation using an improved differential evolution algorithm (IDEA) based on the proposed cost and time models on cloud computing environment. The proposed IDEA combines the Taguchi method and a differential evolution algorithm...

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Veröffentlicht in:	Computers & operations research Jg. 40; H. 12; S. 3045 - 3055
Hauptverfasser:	Tsai, Jinn-Tsong, Fang, Jia-Cen, Chou, Jyh-Horng
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.12.2013 Elsevier Pergamon Press Inc
Schlagworte:	Algorithmics. Computability. Computer arithmetics Algorithms Applied sciences Cloud computing Computer science; control theory; systems Cost and time models Differential evolution algorithm Evolutionary algorithms Exact sciences and technology Experimental design Exploration Genetic algorithms Mathematical models Mathematics Multi-objective approach Nonparametric inference Operational research and scientific management Operational research. Management science Optimization techniques Probability and statistics Receiving Resource allocation Scheduling Scheduling, sequencing Sciences and techniques of general use Statistics Studies Taguchi methods Task scheduling Theoretical computing Task scheduling Cloud computing Cost and time models Differential evolution algorithm Multi-objective approach Performance evaluation Costs Evolutionary algorithm Project management Resource allocation Multiobjective programming Processing time Makespan Modeling Waiting time Optimization Rank test Taguchi method Execution time Capability index Dominating set Pareto optimum Decision making Coverage Scheduling Conceptual analysis Objective Experimental result Genetic algorithm Reasoning Differential method Data envelopment analysis
ISSN:	0305-0548, 1873-765X, 0305-0548
Online-Zugang:	Volltext
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