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Efficient VM Selection Heuristics for Dynamic VM Consolidation in Cloud Datacenters

Authors: Hammad Qaiser; Gao Shu;

Efficient VM Selection Heuristics for Dynamic VM Consolidation in Cloud Datacenters

Abstract

Dynamic Consolidation of Virtual Machines (VMs) in a cloud data center requires live migration of VMs from over-utilized hosts. A primary but relatively an ignored part of consolidation process is the efficient selection of Virtual Machines from an over-utilized host for migration. Two VM selection policies, Threshold Based Selection (TBS) and Capacity Based Selection (CBS), have been proposed in this paper. These policies are based on the idea of simultaneously minimizing multiple factors that contribute to the degradation of the quality of service due to consolidation. Three degrading factors considered in the policies are, the time duration of VM migrations, time duration hosts remain over-utilized and the total number of migrations required for consolidation. Cost functions, involving these degrading factors, have been provided which formed the bases for TBS and CBS. TBS is an efficient VM selection mechanism that focuses more on the time duration of VM migrations and the total number of migrations required for the consolidation process as a trade-off between the three degrading factors. On the other hand, CBS is another efficient mechanism with more emphasis on reducing the time duration for which hosts remain over-utilized. Experiment results obtained by using Cloudsim simulating toolkit have shown that our proposed policies outperformed conventional VM selection policies like MMT, MU, and RC on indicators such as energy consumption, SLA violations, and overall performance efficiency.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
2
Average
Average
Average
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