Application Resource Demand Phase Analysis and Prediction in Support of Dynamic Resource Provisioning

Abstract

Profiling the execution phases of an application can lead to optimizing the utilization of the underlying resources. This is the thrust of this paper, which presents a novel system-level application resource demand phase analysis and prediction prototype to support on-demand resource provisioning. The phase profile learned from historical runs is used to classify and predict phase behavior using a set of algorithms based on clustering. The process takes into consideration application's resource consumption patterns, pricing schedules defined by the resource provider, and penalties associated with service-level agreement (SLA) violations.