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

• We propose a technique to automatically cluster VMs with similar behavior in a cloud system.
• We exploit smoothed histograms to represent VM behavior.
• We propose a technique to automatically select useful information for VM behavior representation.
• We quantify the potential reduction in data collected for monitoring.
• We demonstrate that the proposed technique outperforms the state of the art.

The growing size and complexity of cloud systems determine scalability issues for resource monitoring and management. While most existing solutions consider 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.