Identifying resources for cloud garbage collection

Abstract

Infrastructure as a Service (IaaS) clouds provide users with the ability to easily and quickly provision servers. A recent study found that one in three data center servers continues to consume resources without producing any useful work. A number of techniques have been proposed to identify such unproductive instances. However, those approaches adopt the strategy to identify idle cloud instances based on resource utilization. Resource utilization as indicator alone could be misleading, which is especially true for enterprise cloud environment. In this paper, we present Pleco, a tool that detects unproductive instances in IaaS clouds. Pleco captures dependency information between users and cloud instances by constructing a weighted reference model based on application knowledge. To handle cases of insufficient application knowledge, Pleco also supplements its dependency results with a machine learning model trained on resource utilization data. Pleco gives a confidence level and justification for each identified unproductive instances. Cloud administrators can then take different actions according to the information provided by Pleco. Pleco is lightweight and requires no modification to existing applications.