Title: A comparison of techniques to detect similarities in cloud virtual machines

Authors: Claudia Canali; Riccardo Lancellotti

Addresses: Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena, Italy ' Department of Engineering "Enzo Ferrari", University of Modena and Reggio Emilia, Modena, Italy

Abstract: Scalability in monitoring and management of cloud data centres may be improved through the clustering of virtual machines (VMs) exhibiting similar behaviour. However, available solutions for automatic VM clustering present some important drawbacks that hinder their applicability to real cloud scenarios. For example, existing solutions show a clear trade-off between the accuracy of the VMs clustering and the computational cost of the automatic process; moreover, their performance shows a strong dependence on specific technique parameters. To overcome these issues, we propose a novel approach for VM clustering that uses Mixture of Gaussians (MoGs) together with the Kullback-Leiber divergence to model similarity between VMs. Furthermore, we provide a thorough experimental evaluation of our proposal and of existing techniques to identify the most suitable solution for different workload scenarios.

Keywords: cloud computing; VM clustering; virtual machines; cloud monitoring; Kullback-Leibler divergence; mixture of Gaussians; similarity detection; modelling.

DOI: 10.1504/IJGUC.2016.077489

International Journal of Grid and Utility Computing, 2016 Vol.7 No.2, pp.152 - 162

Received: 18 Oct 2014
Accepted: 02 Feb 2015
Published online: 04 Jul 2016 *

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