Investigating Deadline-Driven Scheduling Policy via Simulation with East

Investigating Deadline-Driven Scheduling Policy via Simulation with East

Justin M. Wozniak (University of Notre Dame and Argonne National Laboratory, USA) and Aaron Striegel (University of Notre Dame, USA)
DOI: 10.4018/978-1-60566-370-8.ch008


Opportunistic techniques have been widely used to create economical computation infrastructures and have demonstrated an ability to deliver heterogeneous computing resources to large batch applications, however, batch turnaround performance is generally unpredictable, negatively impacting human experience with widely shared computing resources. Scheduler prioritization schemes can effectively boost the share of the system given to particular users, but to gain a relevant benefit to user experience, whole batches must complete on a predictable schedule, not just individual jobs. Additionally, batches may contain a dependency structure that must be considered when predicting or controlling the completion time of the whole workflow; the slowest or most volatile prerequisite job determines performance. In this chapter, a probabilistic policy enforcement technique is used to protect deadline guarantees against grid resource unpredictability as well as bad estimates. Methods to allocate processors to a common workflow subcase, barrier scheduling, are also presented.

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