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On the Feasibility of Simulation-Driven Portfolio Scheduling for Cyberinfrastructure Runtime Systems

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Job Scheduling Strategies for Parallel Processing (JSSPP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13592))

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Abstract

Runtime systems that automate the execution of applications on distributed cyberinfrastructures need to make scheduling decisions. Researchers have proposed many scheduling algorithms, but most of them are designed based on analytical models and assumptions that may not hold in practice. The literature is thus rife with algorithms that have been evaluated only within the scope of their underlying assumptions but whose practical effectiveness is unclear. It is thus difficult for developers to decide which algorithm to implement in their runtime systems.

To obviate the above difficulty, we propose an approach by which the runtime system executes, throughout application execution, simulations of this very execution. Each simulation is for a different algorithm in a scheduling algorithm portfolio, and the best algorithm is selected based on simulation results. The main objective of this work is to evaluate the feasibility and potential merit of this portfolio scheduling approach, even in the presence of simulation inaccuracy, when compared to the traditional one-algorithm approach. We perform this evaluation via a case study in the context of scientific workflows. Our main finding is that portfolio scheduling can outperform the best one-algorithm approach even in the presence of relatively large simulation inaccuracies.

This manuscript has been authored in part by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The publisher acknowledges the US government license to provide public access under the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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Notes

  1. 1.

    https://wfcommons.org/instances.

  2. 2.

    https://wrench-project.org.

  3. 3.

    https://simgrid.org.

  4. 4.

    https://wfcommons.org/format.

  5. 5.

    https://github.com/wrench-project/jsspp2022_submission_data.

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Acknowledgments

This work is funded by NSF contracts #2106059 and #2106147: “Collaborative Research: OAC Core: Simulation-driven runtime resource management for distributed workflow applications”; and partially funded by NSF contracts #2103489 and #2103508. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725. Finally, we thank the NSF Chameleon Cloud for providing time grants to access their resources.

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Authors and Affiliations

  1. Information and Computer Sciences Department, University of Hawaii, Honolulu, HI, USA

    Henri Casanova & Yick Ching Wong

  2. Information Sciences Institute, University of Southern California, Marina Del Rey, CA, USA

    Loïc Pottier

  3. National Center for Computational Sciences, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Rafael Ferreira da Silva

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  1. Henri Casanova
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  2. Yick Ching Wong
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  4. Rafael Ferreira da Silva
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Correspondence to Henri Casanova .

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  1. CESNET, Prague, Czech Republic

    Dalibor Klusáček

  2. Polytechnic University of Catalonia, Barcelona, Spain

    Corbalán Julita

  3. Apple, Cupertino, CA, USA

    Gonzalo P. Rodrigo

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Casanova, H., Wong, Y.C., Pottier, L., da Silva, R.F. (2023). On the Feasibility of Simulation-Driven Portfolio Scheduling for Cyberinfrastructure Runtime Systems. In: Klusáček, D., Julita, C., Rodrigo, G.P. (eds) Job Scheduling Strategies for Parallel Processing. JSSPP 2022. Lecture Notes in Computer Science, vol 13592. Springer, Cham. https://doi.org/10.1007/978-3-031-22698-4_1

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  • DOI: https://doi.org/10.1007/978-3-031-22698-4_1

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