The growing size and complexity of cloud systems determine scalability issues for resource monitoring and management. While most existing solutions con- sider each Virtual Machine (VM) as a black box with independent characteristics, we embrace a new perspective where VMs with similar behaviors in terms of resource usage are clustered together. We argue that this new approach has the potential to address scalability issues in cloud monitoring and management. In this paper, we propose a technique to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. This innovative technique models VMs behavior exploiting the probability histogram of their resources usage, and performs smoothing-based noise reduction and selection of the most relevant information to consider for the clustering process. Through extensive evaluation, we show that our proposal achieves high and stable performance in terms of automatic VM clustering, and can reduce the monitoring requirements of cloud systems.

Detecting Similarities in Virtual Machine Behavior for Cloud Monitoring using Smoothed Histograms / Lancellotti, Riccardo; Canali, Claudia. - In: JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING. - ISSN 0743-7315. - STAMPA. - 74:8(2014), pp. 2757-2769. [10.1016/j.jpdc.2014.02.006]

Detecting Similarities in Virtual Machine Behavior for Cloud Monitoring using Smoothed Histograms

LANCELLOTTI, Riccardo;CANALI, Claudia
2014

Abstract

The growing size and complexity of cloud systems determine scalability issues for resource monitoring and management. While most existing solutions con- sider each Virtual Machine (VM) as a black box with independent characteristics, we embrace a new perspective where VMs with similar behaviors in terms of resource usage are clustered together. We argue that this new approach has the potential to address scalability issues in cloud monitoring and management. In this paper, we propose a technique to cluster VMs starting from the usage of multiple resources, assuming no knowledge of the services executed on them. This innovative technique models VMs behavior exploiting the probability histogram of their resources usage, and performs smoothing-based noise reduction and selection of the most relevant information to consider for the clustering process. Through extensive evaluation, we show that our proposal achieves high and stable performance in terms of automatic VM clustering, and can reduce the monitoring requirements of cloud systems.
2014
74
8
2757
2769
Detecting Similarities in Virtual Machine Behavior for Cloud Monitoring using Smoothed Histograms / Lancellotti, Riccardo; Canali, Claudia. - In: JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING. - ISSN 0743-7315. - STAMPA. - 74:8(2014), pp. 2757-2769. [10.1016/j.jpdc.2014.02.006]
Lancellotti, Riccardo; Canali, Claudia
File in questo prodotto:
File Dimensione Formato  
jpdc14.pdf

Open access

Descrizione: articolo
Tipologia: Versione dell'autore revisionata e accettata per la pubblicazione
Dimensione 443.77 kB
Formato Adobe PDF
443.77 kB Adobe PDF Visualizza/Apri
Pubblicazioni consigliate

Licenza Creative Commons
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

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1000321
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 7
  • ???jsp.display-item.citation.isi??? 6
social impact