1. Probabilistic Upper Bounds for Urgent Applications Nick Trebon and Pete Beckman University of Chicago and Argonne National Lab, USA Case Study Elevated Priority Experiments Normal Priority Experiment 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. Case Study Computational Resources ResourceRequested CPUs Total CPUsAvailable Policies UC/ANL IA64 16124Normal, Next-to-Run, Preemption Mercury321,262Normal Tungsten322,560Normal Case Study File Staging Requirements TypeSize# of Repositories Input3.9 GB2 Output3.9 GB1 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. Performance of Composite Bounds for Normal Priority (Target Quantile: 0.815) Success Rate for Individual and Composite Phases Overprediction Rate for Individual and Composite Phases The first experiment examined the performance of the bounds for a normal policy (i.e., no SPRUCE). ***Note difference in scales*** Each individual phase targeted the 0.95 quantile. Thus, the composite quantile was 0.815. ResourceInputQueueExecutionOutputComposite UC/ANL100%97%100%83%97% Mercury99% 93%86%100% Tungsten88%99%96%77%99% ResourceInputQueueExecutionOutputComposite UC/ANL8%2,379%7%22%65% Mercury10%4,468%4%5%1,417% Tungsten11%4,481%13%-5%1,824% Performance of Composite Bounds for Next-to-Run and Preemption Policies (Target Quantile: 0.815) 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. Success Rate for Individual and Composite Phases for Elevated Priorities Overprediction Rate for Individual and Composite Phases for Elevated Priorities PolicyInputQueueExecutionOutputComposite Next-to-run100%92%91%92%94% Preemption98%94%93%85%95% ResourceInputQueueExecutionOutputComposite Next-to-Run8%46%5%41%11% Preemption6%75%4%7%6% References 1. N. Trebon, “Deadline-based grid resource selection for urgent computing,” Master’s thesis, University of Chicago, Chicago, IL Jun. 2008. 2.SPRUCE: http://www.spruce.uchicago.edu 1.Preliminary approach to generating empirical-based upper bounds on total turnaround times for urgent applications performs well for elevated priority experiments. 2.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. 3.The composite bounds can be used to guide urgent computing users in selecting a resource with greater confidence that their deadlines will be met.