Please use this identifier to cite or link to this item: http://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/6303
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dc.contributor.authorRana, Harsh-
dc.contributor.authorGhrera, S.P [Guided by]-
dc.date.accessioned2022-09-22T07:15:45Z-
dc.date.available2022-09-22T07:15:45Z-
dc.date.issued2017-
dc.identifier.urihttp://ir.juit.ac.in:8080/jspui/jspui/handle/123456789/6303-
dc.description.abstract“Cloud Computing” has significantly made its landmark in the field of information technology. A concept which initially stood nebulous is now used as a synonym for internet. It has paved a way to increase capacity as well as add capabilities on the fly without investing in new infrastructure, training new personnel, or licensing new software.“Cloud computing in broad sense can be understood as a connected pool of computing resources (including software and hardware) which can eventually be delivered as a service over a network (in broad sense the Internet)”. Resources are available to the user in utility-style infrastructure. However management of resources in a dynamic fashion stands a vast area for researchers. These resources are precisely available at certain fixed times and also for fixed intervals of time. Thus Scheduling of these resources constitutes a major part of resource management. An “optimized” scheduling of resources is required to maintain separation between users of the resources. In this paper, we discuss various resource scheduling strategies that have been and are being implemented in “cloud computing” environments. We also propose a comparison among the characteristics features of these resource scheduling algorithms.en_US
dc.language.isoenen_US
dc.publisherJaypee University of Information Technology, Solan, H.P.en_US
dc.subjectGenetic algorithmen_US
dc.subjectCloud componentsen_US
dc.subjectAnt colony opt.en_US
dc.titleCloud Resource Optimisation Comparison of Probabilistic Optimization Algorithmsen_US
dc.typeProject Reporten_US
Appears in Collections:B.Tech. Project Reports

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