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dc.contributor.authorKanagala, K.
dc.contributor.authorSekaran, K.C.
dc.date.accessioned2020-03-30T09:58:43Z-
dc.date.available2020-03-30T09:58:43Z-
dc.date.issued2013
dc.identifier.citationProceedings of the International Conference on Cloud Computing Technology and Science, CloudCom, 2013, Vol.2, , pp.345-348en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/7258-
dc.description.abstractElasticity is one of the key governing properties of cloud computing that has major effects on cost and performance directly. Most of the popular Infrastructure as a Service (IaaS) providers such as Amazon Web Services (AWS), Windows Azure, Rack space etc. work on threshold-based auto-scaling. In current IaaS environments there are various other factors like "Virtual Machine (VM)-turnaround time", "VM-stabilization time" etc. that affect the newly started VM from start time to request servicing time. If these factors are not considered while auto-scaling, then they will have direct effect on Service Level Agreement (SLA) implementations and users' response time. Therefore, these thresholds should be a function of load trend, which makes VM readily available when needed. Hence, we developed an approach where the thresholds adapt in advance and these thresholds are functions of all the above mentioned factors. Our experimental results show that our approach gives the better response time. � 2013 IEEE.en_US
dc.titleAn approach for dynamic scaling of resources in enterprise clouden_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

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