Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems

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

Detecting similarities in virtual machine behavior for cloud monitoring using smoothed histograms

“…Furthermore, we analyze the impact of variability in workload intensity on the final clustering results. Finally, we compare the performance of the SH-based technique with that of alternative VM clustering approaches proposed in previous studies [3,4].…”

“…Recent studies [3,4] of the authors propose alternative techniques to automatically cluster VMs in cloud systems depending on their behavior. In [3] we exploit the correlation coefficients between resource usage time series to determine VM similarities, while in [4] we rely on cluster ensemble techniques.…”

“…To the best of our knowledge, the automatic clustering of VMs with similar behavior is a problem only recently analyzed in [3,4]. In [3] clustering is based on the correlation coefficients among resource usage, which leads to highly sensitive performance with respect to the length of resource usage time series.…”

“…In [3] clustering is based on the correlation coefficients among resource usage, which leads to highly sensitive performance with respect to the length of resource usage time series. In [4] the authors exploit an approach based on the Bhattacharyya distance requiring a separate clustering step for every VM resource, thus resulting in a non-negligible computational cost of the clustering process. On the other hand, the technique proposed in this paper outperforms the previous attempts in terms of both quality and computational cost of the clustering solution.…”

A New Method for Virtual Machine Cluster Scheduling

“…Based on the background of the cloud environment access of the digital library, Ma Xiao-ting constructed the deployment strategy of the virtual machine from the view of resource restriction. He emphasized the relationship between virtual machine deployment and the diversification of user requirements, and constructed the performance vector to characterize the resource situation, which was the basis for the virtual machine deployment [5]. Zhuang Wei combined the two issues of resource and load, and extracted their properties.…”