System-level performance phase characterization for on-demand resource provisioning

The thrust of this paper is to profile the execution phases of applications, which helps optimize the efficiency of the underlying resources. Here we present a novel system-level application-resource-demand phase analysis and prediction approach in support of on-demand resource provisioning. The process we follow is to explore large-scale behavior of applicationspsila resource consumption, followed by analysis using a set of algorithms based on clustering. The phase profile, which learns from historical runs, is used to classify and predict future phase behavior. This process takes into consideration applicationspsilas resource consumption patterns, phase transition costs and penalties associated with service-level agreements (SLA) violations. Our experimental results with WorldCup98 replay web access logs show that prediction accuracies around 84% or larger for ten-phase cases can be achieved for network performance traces.