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Metrics for Parallel Job Scheduling and Their Convergence

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Job Scheduling Strategies for Parallel Processing (JSSPP 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2221))

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Abstract

The arrival process of jobs submitted to a parallel system is bursty, leading to fluctuations in the load at many time scales. In particular, rare events of extreme load may occur. Such events lead to an increase in the standard deviation of performance metrics, and thus delay the convergence of simulations used to evaluate the scheduling. Different performance metrics have been proposed in an effort to reduce this variability, and indeed display different rates of convergence. However, there is no single metric that outperforms the others under all conditions. Rather, the convergence of different metrics depends on the system being studied.

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Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, The Hebrew University, 91904, Jerusalem, Israel

    Dror G. Feitelson

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  1. Dror G. Feitelson
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Editor information

Editors and Affiliations

  1. The Hebrew University School of Computer Science and Engineering, 91904, Jerusalem, Israel

    Dror G. Feitelson

  2. Laboratory for Computer Science, Massachusetts Institute of Technology, 02139, Cambridge, MA, USA

    Larry Rudolph

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© 2001 Springer-Verlag Berlin Heidelberg

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Feitelson, D.G. (2001). Metrics for Parallel Job Scheduling and Their Convergence. In: Feitelson, D.G., Rudolph, L. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2001. Lecture Notes in Computer Science, vol 2221. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45540-X_11

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  • DOI: https://doi.org/10.1007/3-540-45540-X_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42817-6

  • Online ISBN: 978-3-540-45540-0

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