Machine Learning based optimised live virtual machine migration over WAN Links

Journal article


Arif, M, Kiani, A and Qadir, J (2017). Machine Learning based optimised live virtual machine migration over WAN Links. Telecommunication Systems. 64 (2), pp. 245-257.
AuthorsArif, M, Kiani, A and Qadir, J
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%.

Year2017
JournalTelecommunication Systems
Journal citation64 (2), pp. 245-257
PublisherSpringer Verlag
ISSN1018-4864
Digital Object Identifier (DOI)doi:10.1007/s11235-016-0173-3
Publication dates
Print01 Feb 2017
Publication process dates
Deposited11 Jan 2019
Accepted25 May 2016
Accepted author manuscript
License
CC BY 4.0
Permalink -

https://openresearch.lsbu.ac.uk/item/8708v

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