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dc.contributor.authorSharma, N.K.-
dc.contributor.authorRam Mohana Reddy, Guddeti-
dc.date.accessioned2020-03-30T10:22:24Z-
dc.date.available2020-03-30T10:22:24Z-
dc.date.issued2017-
dc.identifier.citationProceedings - 2016 IEEE International Conference on Cloud Computing in Emerging Markets, CCEM 2016, 2017, Vol., , pp.73-77en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8533-
dc.description.abstractIn this paper, a new novel Improved Genetic Algorithm (IGA) is proposed to determine the near optimal solution for multi-objective resources allocation at the green cloud data center of smart grid. However, instead of randomly generating the initial chromosomes for crossover and mutation operations the modified first decreasing (MFD) technique generates better solution for the initial population. The proposed work saves the energy consumption, minimizes the resource wastage, and reduce the algorithm's computation time at the cloud data center. The Cloud-sim simulator based experimental results show that our proposed approach improves the performance of the data center in terms of energy efficiency and average resources utilization when compared to the state-of-the-art VMs allocation approaches i.e. First Fit, Modified First Decreasing (MFD) and, Grouping Genetic Algorithm (GGA). � 2016 IEEE.en_US
dc.titleMulti-Objective Resources Allocation Using Improved Genetic Algorithm at Cloud Data Centeren_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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