Articles liés à Energy Efficient Resource Allocation in Cloud Computing:...

Energy Efficient Resource Allocation in Cloud Computing: Heuristic Algorithmic Approaches for Energy Efficient Resource Allocation - Couverture souple

Kumar, Dilip; Sahoo, Bibhudatta

 
9783659563041: Energy Efficient Resource Allocation in Cloud Computing: Heuristic Algorithmic Approaches for Energy Efficient Resource Allocation
Couverture souple
ISBN 10 : 3659563048 ISBN 13 : 9783659563041
Editeur : LAP LAMBERT Academic Publishing, 2014

Synopsis

These heuristic algorithms operate in two phases, selection of task from the task pool, followed by selection of cloud resource. A set of ten greedy heuristics for resource allocation using the greedy paradigm has been used, that operates in two stages. At each stage a particular input is selected through a selection procedure. Then a decision is made regarding the selected input, whether to include it into the partially constructed optimal solution. The selection procedure can be realized using a 2-phase heuristic. In particular, we have used 'FcfsRand', 'FcfsRr','FcfsMin','FcfsMax', 'MinMin', 'MedianMin', 'MaxMin', 'MinMax', 'MedianMax', and 'MaxMax'. The simulation results indicate in the favor of MaxMax. The novel genetic algorithm framework has been proposed for task scheduling to minimize the energy consumption in cloud computing infrastructure. The performance of the proposed GA resource allocation strategy has been compared with Random and Round Robin scheduling.

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Présentation de l'éditeur

These heuristic algorithms operate in two phases, selection of task from the task pool, followed by selection of cloud resource. A set of ten greedy heuristics for resource allocation using the greedy paradigm has been used, that operates in two stages. At each stage a particular input is selected through a selection procedure. Then a decision is made regarding the selected input, whether to include it into the partially constructed optimal solution. The selection procedure can be realized using a 2-phase heuristic. In particular, we have used 'FcfsRand', 'FcfsRr','FcfsMin','FcfsMax', 'MinMin', 'MedianMin', 'MaxMin', 'MinMax', 'MedianMax', and 'MaxMax'. The simulation results indicate in the favor of MaxMax. The novel genetic algorithm framework has been proposed for task scheduling to minimize the energy consumption in cloud computing infrastructure. The performance of the proposed GA resource allocation strategy has been compared with Random and Round Robin scheduling.

Biographie de l'auteur

Dilip Kumar is Assistant Professor in the Department of Computer Science & Engineering, NIT Jamshedpur, INDIA. His technical interests include Cloud computing, Image processing, and Optimization.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
LAP LAMBERT Academic Publishing
Date d'édition
2014
Langue
anglais
ISBN 10 
3659563048
ISBN 13 
9783659563041
Reliure
Broché
Nombre de pages
92
Coordonnées du fabricant
non disponible
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