Machine learning based optimized live virtual machine migration over WAN links

Moiz Arif (), Adnan K. Kiani () and Junaid Qadir ()
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Moiz Arif: National University of Sciences & Technology (NUST)
Adnan K. Kiani: National University of Sciences & Technology (NUST)
Junaid Qadir: National University of Sciences & Technology (NUST)

Telecommunication Systems: Modelling, Analysis, Design and Management, 2017, vol. 64, issue 2, No 2, 245-257

Abstract: Abstract Live virtual machine migration is one of the most promising features of data center virtualization technology. Numerous strategies have been proposed for live migration of virtual machines on local area networks. These strategies work perfectly in their respective domains with negligible downtime. However, these techniques are not suitable to handle live migration over wide area networks and results in significant downtime. In this paper we have proposed a Machine Learning based Downtime Optimization (MLDO) approach which is an adaptive live migration approach based on predictive mechanisms that reduces downtime during live migration over wide area networks for standard workloads. The main contribution of our work is to employ machine learning methods to reduce downtime. Machine learning methods are also used to introduce automated learning into the predictive model and adaptive threshold levels. We compare our proposed approach with existing strategies in terms of downtime observed during the migration process and have observed improvements in downtime of up to 15 %.

Keywords: Live migration; Wide area network; Virtual machine; Hypervisor (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s11235-016-0173-3

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