Empirical-based probabilistic upper bounds for urgent computing applications

Scientific simulation and modeling often aid in making critical decisions in such diverse fields as city planning, severe weather prediction and influenza modeling. In some of these situations the computations operate under strict deadlines, after which point the results may have very little value. In these cases of urgent computing, it is imperative that these computations begin execution as quickly as possible. The special priority and urgent compute environment (SPRUCE) is a framework designed to enable these high priority computations to quickly access computational grid resources through elevated batch queue priority. However, participating resources are allowed to decide locally how to respond to urgent requests. For instance, some may offer next-to-run status while others may preempt currently executing jobs to clear off the necessary nodes. However, the user is still faced with the problem of resource selection - namely, which resource (and corresponding urgent computing policy) provides the best probability of meeting a given deadline? This paper introduces a set of methodologies and heuristics aimed at generating an empirical-based probabilistic upper bound on the total turnaround time for an urgent computation. These upper bounds can then be used to guide the user in selecting a resource with greater confidence that their deadline will be met.