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.
A comparison of techniques to detect similarities in cloud virtual machines / Canali, Claudia; Lancellotti, Riccardo. - In: INTERNATIONAL JOURNAL OF GRID AND UTILITY COMPUTING. - ISSN 1741-847X. - STAMPA. - 7:2(2016), pp. 152-162. [10.1504/IJGUC.2016.077489]
A comparison of techniques to detect similarities in cloud virtual machines
CANALI, Claudia;LANCELLOTTI, Riccardo
2016
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.File | Dimensione | Formato | |
---|---|---|---|
IJGUC70208_Canali & Lancellotti.pdf
Accesso riservato
Tipologia:
Versione pubblicata dall'editore
Dimensione
844.52 kB
Formato
Adobe PDF
|
844.52 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I metadati presenti in IRIS UNIMORE sono rilasciati con licenza Creative Commons CC0 1.0 Universal, mentre i file delle pubblicazioni sono rilasciati con licenza Attribuzione 4.0 Internazionale (CC BY 4.0), salvo diversa indicazione.
In caso di violazione di copyright, contattare Supporto Iris