A comparison of techniques to detect similarities in cloud virtual machines. (2016)

• Record Type: Journal Article
• Authors: Canali, Claudia; Lancellotti, Riccardo
• Is Part Of: International journal of grid and utility computing. Volume 7:Number 2(2016)
• Page Start: 152
• Page End: 162
• Publication Date: 2016
• Subjects: cloud computing -- VM clustering -- virtual machines -- cloud monitoring -- Kullback-Leibler divergence -- mixture of Gaussians -- similarity detection -- modelling; Electronic data processing -- Distributed processing -- Periodicals; Electronic commerce -- Management -- Computer programs -- Periodicals; 004.605
• Languages: English
• ISSNs: 1741-847X

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.