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Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems

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Abstract

Cloud computing has recently emerged as a new paradigm to provide computing services through large-size data centers where customers may run their applications in a virtualized environment. The advantages of cloud in terms of flexibility and economy encourage many enterprises to migrate from local data centers to cloud platforms, thus contributing to the success of such infrastructures. However, as size and complexity of cloud infrastructures grow, scalability issues arise in monitoring and management processes. Scalability issues are exacerbated because available solutions typically consider each virtual machine (VM) as a black box with independent characteristics, which is monitored at a fine-grained granularity level for management purposes, thus generating huge amounts of data to handle. We claim that scalability issues can be addressed by leveraging the similarity between VMs in terms of resource usage patterns. In this paper, we propose an automated methodology to cluster similar VMs starting from their resource usage information, assuming no knowledge of the software executed on them. This is an innovative methodology that combines the Bhattacharyya distance and ensemble techniques to provide a stable evaluation of similarity between probability distributions of multiple VM resource usage, considering both system- and network-related data. We evaluate the methodology through a set of experiments on data coming from an enterprise data center. We show that our proposal achieves high and stable performance in automatic VMs clustering, with a significant reduction in the amount of data collected which allows to lighten the monitoring requirements of a cloud data center.

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Notes

  1. R project home page: http://www.r-project.org/.

  2. Python home page: http://www.python.org/.

  3. Bourne shell home page: http://www.gnu.org/software/bash/.

  4. Cacti home page: http://www.cacti.net.

  5. Munin home page: http://munin-monitoring.org/.

  6. Ganglia Monitoring System home page: http://ganglia.sourceforge.net/.

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Authors and Affiliations

  1. Department of Engineering “Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy

    Claudia Canali & Riccardo Lancellotti

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  1. Claudia Canali
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  2. Riccardo Lancellotti
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Correspondence to Claudia Canali.

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Canali, C., Lancellotti, R. Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems. Autom Softw Eng 21, 319–344 (2014). https://doi.org/10.1007/s10515-013-0134-y

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