Case Study: Job Scheduling

Max Kuhn and Kjell Johnson
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Max Kuhn: Pfizer Global Research and Development, Division of Nonclinical Statistics
Kjell Johnson: Arbor Analytics

Chapter Chapter 17 in Applied Predictive Modeling, 2013, pp 445-460 from Springer

Abstract: Abstract High-performance computing (HPC) environments are used by many technology and research organizations to facilitate large-scale computations. HPC systems typically use a job scheduling software which prioritizes jobs for submissions, manages the computational resources, and initiates submitted jobs to maximize efficiency. To assist the scheduler, data on execution times were collected and are used to classify new jobs into one of four classes (very fast, fast, moderate or long). In this chapter we illustrate the model tuning and evaluation process in this context. Here we present the data splitting and modeling strategy (Section 17.1), model results (Section 17.2), and corresponding computing code (Section 17.3).

Keywords: Support Vector Machine; Execution Time; Random Forest; Linear Discriminant Analysis; Confusion Matrix (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-1-4614-6849-3_17

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DOI: 10.1007/978-1-4614-6849-3_17

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