An Adaptive Technique to Model Virtual Machine Behavior for Scalable Cloud Monitoring

Supporting the emerging digital society is creating new challenges for cloud computing infrastructures, exacerbating scalability issues regarding the processes of resource monitoring and management in large cloud data centers. Recent research studies show that automatically clustering similar virtual machines running the same software component may improve the scalability of the monitoring process in IaaS cloud systems. However, to avoid misclassifications, the clustering process must take into account long time series (up to weeks) of resource measurements, thus resulting in a mechanism that is slow and not suitable for a cloud computing model where virtual machines may be frequently added or removed in the data center. In this paper, we propose a novel methodology that dynamically adapts the length of the time series necessary to correctly cluster each VM depending on its behavior. This approach supports a clustering process that does not have to wait a long time before making decisions about the VM behavior. The proposed methodology exploits elements of fuzzy logic for the dynamic determination of time series length. To evaluate the viability of our solution, we apply the methodology to a case study considering different algorithms for VMs clustering. Our results confirm that after just 1 day of monitoring we can cluster without misclassifications up to 80% of the VMs, while for the remaining 20% of the VMs longer observations are needed