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http://hdl.handle.net/2080/2100
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DC Field | Value | Language |
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dc.contributor.author | Kumar, D | - |
dc.contributor.author | Sahoo, Bibhudatta | - |
dc.date.accessioned | 2014-03-05T09:46:33Z | - |
dc.date.available | 2014-03-05T09:46:33Z | - |
dc.date.issued | 2014-01 | - |
dc.identifier.citation | International Journal of Artificial Intelligent Systems and Machine Learning, vol.6 no 1, January 2014, pp.32-38. | en |
dc.identifier.issn | 0974 – 9667 | - |
dc.identifier.issn | 0974 – 9543 | - |
dc.identifier.uri | http://hdl.handle.net/2080/2100 | - |
dc.description | Copyright belongs to CiiT | en |
dc.description.abstract | Minimizing 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.extent | 1951351 bytes | - |
dc.format.mimetype | application/pdf | - |
dc.language.iso | en | - |
dc.publisher | CiiT | en |
dc.subject | Cloud Computing | en |
dc.subject | task consolidation | en |
dc.subject | energy aware | en |
dc.subject | virtual machine | en |
dc.subject | energy-efficient resource allocation | en |
dc.subject | resource utilization | en |
dc.title | Energy Efficient Heuristic Resource Allocation for Cloud Computing | en |
dc.type | Article | en |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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Final Heuristic January2004 journal.pdf | 1.91 MB | Adobe PDF | View/Open |
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