Balancing Accuracy and Execution Time for Similar Virtual Machines Identification in IaaS Cloud

Identification of VMs exhibiting similar behavior can improve scalability in monitoring and management of cloud data centers. Existing solutions for automatic VM clustering may be either very accurate, at the price of a high computational cost, or able to provide fast results with limited accuracy. Furthermore, the performance of most solutions may change significantly depending on the specific values of technique parameters. In this paper, we propose a novel approach to model VM behavior using Mixture of Gaussians (MoGs) to approximate the probability density function of resources utilization. Moreover, we exploit the Kullback-Leibler divergence to measure the similarity between MoGs. The proposed technique is compared against the state of the art through a set of experiments with data coming from a private cloud data center. Our experiments show that the proposed technique can provide high accuracy with limited computational requirements. Furthermore, we show that the performance of our proposal, unlike the existing alternatives, does not depend on any parameter.