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Stochastic VM Multiplexing for Datacenter Consolidation

Authors: Bipin B. Nandi; Ansuman Banerjee; Sasthi C. Ghosh 0001; Nilanjan Banerjee;

Stochastic VM Multiplexing for Datacenter Consolidation

Abstract

Virtual machine (VM) placement for Datacenter (DC) consolidation is a challenging problem, particularly in the face of VM workload fluctuation. In this paper, we present a stochastic model for optimization of DC consolidation and propose intelligent strategies for statistical VM multiplexing on physical machines (PMs) to ensure optimal use of hardware resources, while providing a service guarantee. We have provided an optimal strategy by modeling and solving the problem as a stochastic integer programming problem followed by a more scalable strategy based on a greedy heuristic. Extensive simulation based experimental results show that the strategies are more efficient in resource utilization while providing bounded service guarantees, than the traditional way of VM placement without any consideration to workload fluctuation.

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    19
    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.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
Powered by OpenAIRE graph
Found an issue? Give us feedback
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!
19
Average
Top 10%
Top 10%
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