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Case Study

Probabilistic Upper Bounds for Urgent Applications Nick Trebon and Pete Beckman University of Chicago and Argonne National Lab, USA. Case Study. Normal Priority Experiment. Elevated Priority Experiments. Conclusions.

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Case Study

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  1. Probabilistic Upper Bounds for Urgent ApplicationsNick Trebon and Pete BeckmanUniversity of Chicago and Argonne National Lab, USA Case Study Normal Priority Experiment Elevated Priority Experiments Conclusions In order to evaluate the individual phase and composite bounding methodologies, a case study was performed using the FLASH scientific application on the TeraGrid. While FLASH is not a typical urgent computation due to the lack of a deadline constraint, it is a complex and well-known scientific code. The first experiment examined the performance of the bounds for a normal policy (i.e., no SPRUCE). The next two experiments examined the performance of the bounds for the next-to-run and preemption policies. Only the UC/ANL resource was part of this experiment. The 0.95 quantile was targeted for each individual phase, resulting in an 0.815 composite target quantile. Preliminary approach to generating empirical-based upper bounds on total turnaround times for urgent applications performs well for elevated priority experiments. The overprediction in the normal priority queue phase is most likely caused by skew in batch history. SPRUCE elevated priorities provide users with individual and composite bounds that are both accurate and correct. The composite bounds can be used to guide urgent computing users in selecting a resource with greater confidence that their deadlines will be met. Performance of Composite Bounds for Normal Priority (Target Quantile: 0.815) Performance of Composite Bounds for Next-to-Run and Preemption Policies (Target Quantile: 0.815) Case Study Computational Resources ***Note difference in scales*** Each individual phase targeted the 0.95 quantile. Thus, the composite quantile was 0.815. Because Flash does not have any file staging requirements, these were artificially added. The requirements were loosely modeled after Linked Environment for Atmospheric Discovery (LEAD) workflow. LEAD is a project that, in 2007, teamed with SPRUCE in order to perform real-time, on-demand, dynamically adaptive forecasts. Success Rate for Individual and Composite Phases Success Rate for Individual and Composite Phases for Elevated Priorities References N. Trebon, “Deadline-based grid resource selection for urgent computing,” Master’s thesis, University of Chicago, Chicago, IL Jun. 2008. SPRUCE: http://www.spruce.uchicago.edu Overprediction Rate for Individual and Composite Phases Overprediction Rate for Individual and Composite Phases for Elevated Priorities Case Study File Staging Requirements

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