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

Balancing Accuracy and Execution Time for Similar Virtual Machines Identification in IaaS Cloud / Canali, Claudia; Lancellotti, Riccardo. - ELETTRONICO. - (2014), pp. 137-142. ((Intervento presentato al convegno 23rd IEEE International WETICE Conference, WETICE 2014 tenutosi a Parma, Italy nel June 2014 [10.1109/WETICE.2014.57].

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

CANALI, Claudia;LANCELLOTTI, Riccardo
2014

Abstract

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
23rd IEEE International WETICE Conference, WETICE 2014
Parma, Italy
June 2014
137
142
Canali, Claudia; Lancellotti, Riccardo
Balancing Accuracy and Execution Time for Similar Virtual Machines Identification in IaaS Cloud / Canali, Claudia; Lancellotti, Riccardo. - ELETTRONICO. - (2014), pp. 137-142. ((Intervento presentato al convegno 23rd IEEE International WETICE Conference, WETICE 2014 tenutosi a Parma, Italy nel June 2014 [10.1109/WETICE.2014.57].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1049116
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