Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2100
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dc.contributor.authorKumar, D-
dc.contributor.authorSahoo, Bibhudatta-
dc.date.accessioned2014-03-05T09:46:33Z-
dc.date.available2014-03-05T09:46:33Z-
dc.date.issued2014-01-
dc.identifier.citationInternational Journal of Artificial Intelligent Systems and Machine Learning, vol.6 no 1, January 2014, pp.32-38.en
dc.identifier.issn0974 – 9667-
dc.identifier.issn0974 – 9543-
dc.identifier.urihttp://hdl.handle.net/2080/2100-
dc.descriptionCopyright belongs to CiiTen
dc.description.abstractMinimizing the energy consumption in cloud computing environment is one of the key research issues. Power consumed by computing resources and storage in cloud can be optimized through energy aware resource allocation. As the resource utilization by the tasks are directly relates to energy consumption, the task consolidation are being used to optimize the energy consumption. An energy efficient heuristic algorithm has been proposed and compared with three energy-aware task consolidation heuristics by varying number of tasks. The proposed task consolidation algorithm minimizes total energy consumed by the cloud computing system.en
dc.format.extent1951351 bytes-
dc.format.mimetypeapplication/pdf-
dc.language.isoen-
dc.publisherCiiTen
dc.subjectCloud Computingen
dc.subjecttask consolidationen
dc.subjectenergy awareen
dc.subjectvirtual machineen
dc.subjectenergy-efficient resource allocationen
dc.subjectresource utilizationen
dc.titleEnergy Efficient Heuristic Resource Allocation for Cloud Computingen
dc.typeArticleen
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