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Machine learning models for predicting timely virtual machine live migration

Lookup NU author(s): Osama Alrajeh, Professor Matthew ForshawORCiD, Dr Nigel ThomasORCiD

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Abstract

Virtual machine (VM) consolidation is among the key strategic approaches that can be employed to reduce energy consumption in large computing infrastructure. However, live migration of VMs is not a trivial operation and consequently not all VMs can be easily consolidated in all circumstances. In this paper we present experiments attempting to live migrate the Kernel-based VM (KVM) executing workload form the SPECjvm2008 benchmark. In order to understand what factors in- fluence live migration we investigate three machine learning models to predict successful live migration using different training and evaluation sets drawn from our experimental data.

Publication metadata

Author(s): Alrajeh O, Forshaw M, Thomas N

Editor(s): Philipp Reinecke and Antinisca Di Marco

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: Computer Performance Engineering: 14th European Workshop (EPEW 2017)

Year of Conference: 2017

Pages: 169-183

Online publication date: 13/08/2017

Acceptance date: 02/04/2016

ISSN: 0302-9743

Publisher: Springer

URL: https://doi.org/10.1007/978-3-319-66583-2_11

DOI: 10.1007/978-3-319-66583-2_11

Library holdings: Search Newcastle University Library for this item

Series Title: Lecture Notes in Computer Science

ISBN: 9783319665825

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