System-level performance phase characterization for on-demand resource provisioning
- 1 January 2007
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15525244,p. 434-439
- https://doi.org/10.1109/clustr.2007.4629261
Abstract
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.Keywords
This publication has 24 references indexed in Scilit:
- Application Resource Demand Phase Analysis and Prediction in Support of Dynamic Resource ProvisioningPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2007
- Improving Machine Virtualization with 'Hotplug Memory'Published by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- Pinpointing Representative Portions of Large Intel® Itanium® Programs with Dynamic InstrumentationPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Long-Term Workload Phases: Duration Predictions and Applications to DVFSIEEE Micro, 2005
- Virtual Workspaces: Achieving Quality of Service and Quality of Life in the GridScientific Programming, 2005
- A case for grid computing on virtual machinesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2004
- Xenoservers: accountable execution of untrusted programsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Automatically characterizing large scale program behaviorPublished by Association for Computing Machinery (ACM) ,2002
- Memory hierarchy reconfiguration for energy and performance in general-purpose processor architecturesPublished by Association for Computing Machinery (ACM) ,2000
- Data clusteringACM Computing Surveys, 1999