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Erschienen in: Cluster Computing 2/2013

01.06.2013

Performance and energy modeling for live migration of virtual machines

verfasst von: Haikun Liu, Hai Jin, Cheng-Zhong Xu, Xiaofei Liao

Erschienen in: Cluster Computing | Ausgabe 2/2013

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Abstract

Live migration of virtual machine (VM) provides a significant benefit for virtual server mobility without disrupting service. It is widely used for system management in virtualized data centers. However, migration costs may vary significantly for different workloads due to the variety of VM configurations and workload characteristics. To take into account the migration overhead in migration decision-making, we investigate design methodologies to quantitatively predict the migration performance and energy consumption. We thoroughly analyze the key parameters that affect the migration cost from theory to practice. We construct application-oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level. This should be the first kind of work to estimate VM live migration cost in terms of both performance and energy in a quantitative approach. We evaluate the models using five representative workloads on a Xen virtualized environment. Experimental results show that the refined model yields higher than 90% prediction accuracy in comparison with measured cost. Model-guided decisions can significantly reduce the migration cost by more than 72.9% at an energy saving of 73.6%.

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Metadaten
Titel
Performance and energy modeling for live migration of virtual machines
verfasst von
Haikun Liu
Hai Jin
Cheng-Zhong Xu
Xiaofei Liao
Publikationsdatum
01.06.2013
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe 2/2013
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-011-0194-3

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