Automated Clustering of VMs for Scalable Cloud Monitoring and Management

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Provided by: University of Missouri-St. Louis
Topic: Cloud
Format: PDF
The size of modern datacenters supporting cloud computing represents a major challenge in terms of monitoring and management of system resources. Available solutions typically consider every Virtual Machine (VM) as a black box each with independent characteristics and face scalability issues by reducing the number of monitoring resource samples, considering in most cases only average CPU utilization of VMs sampled at a very coarse time granularity. The authors claim that better management without compromising scalability could be achieved by clustering together VMs that show similar behavior in terms of resource utilization. In this paper, they propose an automated methodology to cluster VMs depending on the utilization of their resources, assuming no knowledge of the services executed on them.
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