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Machine Learning Models for Predicting Timely Virtual Machine Live Migration

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


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© Springer International Publishing AG 2017. 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 influence 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

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

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

Year of Conference: 2017

Pages: 169-183

Online publication date: 13/08/2017

Acceptance date: 02/04/2016

Publisher: Springer Verlag


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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ISBN: 9783319665825