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Workshop on Job Scheduling Strategies for Parallel Processing

JSSPP 2017: Job Scheduling Strategies for Parallel Processing pp 103–121Cite as

Towards Efficient Resource Allocation for Distributed Workflows Under Demand Uncertainties

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10773))

Abstract

Scheduling of complex scientific workflows on geographically distributed resources is a challenging problem. Selection and scheduling of a subset of available resources to meet a given demand in a cost efficient manner is the first step of this complex process. In this paper, we develop a method to compute cost-efficient selection and scheduling of resources under demand uncertainties. Building on the techniques of Sample Average Approximation and Genetic Algorithms, we demonstrate that our method can lead up to \(24\%\) improvement in costs when demand uncertainties are explicitly considered. We present the results from our preliminary work in the context of a high energy physics application, the Belle II experiments, and believe that the work will equally benefit other scientific workflows executed on distributed resources with demand uncertainties. The proposed method can also be extended to include uncertainties related to resource availability and network performance.

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Notes

  1. 1.

    HEP SPEC is based on SPEC CPU 2006. Further information is available from http://w3.hepix.org/benchmarks/doku.php/.

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Acknowledgment

This work was supported by the Integrated End-to-end Performance Prediction and Diagnosis for Extreme Scientific Workflows (IPPD) Project. IPPD is funded by the U. S. Department of Energy Awards FWP-66406 and DE-SC0012630 at the Pacific Northwest National Laboratory. Pacific Northwest National Laboratory is operated by Battelle for the DOE under Contract DE-AC05-76RL01830. The work of Luis de la Torre was supported in part by the U.S. Department of Energy, Office of Science, Office of Workforce Development for Teachers and Scientists (WDTS) under the Visiting Faculty Program (VFP).

Author information

Authors and Affiliations

  1. Pacific Northwest National Laboratory, Richland, WA, USA

    Ryan D. Friese, Mahantesh Halappanavar, Arun V. Sathanur, Malachi Schram & Darren J. Kerbyson

  2. Universidad Metropolitana, San Juan, PR, USA

    Luis de la Torre

Authors
  1. Ryan D. Friese
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  2. Mahantesh Halappanavar
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  3. Arun V. Sathanur
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  4. Malachi Schram
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  5. Darren J. Kerbyson
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  6. Luis de la Torre
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Corresponding author

Correspondence to Ryan D. Friese .

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Editors and Affiliations

  1. CESNET, Prague, Czech Republic

    Dalibor Klusáček

  2. Google, Mountain View, California, USA

    Walfredo Cirne

  3. Google, Seattle, Washington, USA

    Narayan Desai

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Friese, R.D., Halappanavar, M., Sathanur, A.V., Schram, M., Kerbyson, D.J., de la Torre, L. (2018). Towards Efficient Resource Allocation for Distributed Workflows Under Demand Uncertainties. In: Klusáček, D., Cirne, W., Desai, N. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2017. Lecture Notes in Computer Science(), vol 10773. Springer, Cham. https://doi.org/10.1007/978-3-319-77398-8_6

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  • DOI: https://doi.org/10.1007/978-3-319-77398-8_6

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  • Online ISBN: 978-3-319-77398-8

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