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 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.
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CITATION STYLE
Alrajeh, O., Forshaw, M., & Thomas, N. (2017). Machine learning models for predicting timely virtual machine live migration. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10497 LNCS, pp. 169–183). Springer Verlag. https://doi.org/10.1007/978-3-319-66583-2_11
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