Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2723
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dc.contributor.authorMishra, Sambit Kumar-
dc.contributor.authorSahoo, Bibhudatta-
dc.contributor.authorJena, Sanjay Kumar-
dc.date.accessioned2017-07-03T11:51:08Z-
dc.date.available2017-07-03T11:51:08Z-
dc.date.issued2017-06-
dc.identifier.citation5th International Conference on Advanced Computing, Networking, and Informatics(ICACNI), NIT Goa, Goa, India, 1-3 June 2017en_US
dc.identifier.urihttp://hdl.handle.net/2080/2723-
dc.descriptionCopyright for this paper belongs to proceeding publisheren_US
dc.description.abstractCloud computing system is a progression of distributed system that has been adopted by worldwide scientifically and commercially. For optimal utilization of clouds potential power, effective and efficient algorithms are expected, which will select best resources from available cloud resources for different applications. This allocation of user requests to the cloud resources can optimize several parameters like energy consumption, makespan, throughput, etc. In this paper, we have proposed a learning automata based algorithm to minimize the makespan of the cloud system and also to increase the resource utilization that holds secured resource allocation. We have simulated our algorithm, AOALA with the help of CloudSim simulator in a heterogeneous environment. During the comparison of the algorithm, we provide a finite set of tasks to the AOALA algorithm once and estimate the makespan of the system. We have compared our proposed technique (AOALA), i.e., with learning automata and without learning automata (random allocation algorithm), and shows the system performance.en_US
dc.subjectCloud Computingen_US
dc.subjectDVFSen_US
dc.subjectLearning Automataen_US
dc.subjectMakespanen_US
dc.subjectResourceen_US
dc.subjectTask Allocation;en_US
dc.titleA Secure VM Consolidation in Cloud Using Learning Automataen_US
dc.typeArticleen_US
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