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A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems

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Artificial Intelligence Applications in Information and Communication Technologies

Part of the book series: Studies in Computational Intelligence ((SCI,volume 607))

Abstract

Many studies in the past two decades focused on the problem of efficient resource management and job scheduling in large computational systems such as HPC clusters and Grids. For this purpose, the application of Artificial Intelligence-based methods such as metaheuristics has been proposed in many works. This chapter provides an overview of such works that involve metaheuristics and discusses why mainstream resource management and scheduling systems are instead using only a limited set of rather simple scheduling policies. We identify several reasons that are causing this situation, e.g., a common use of overly simplified problem definitions with rather naive job and machine models or an application of unrealistic optimization criteria. In order to solve aforementioned issues, this chapter proposes new complex and well designed approaches that involve the use of metaheuristic which periodically optimizes job scheduling plan using several real life based optimization criteria. Importantly, approaches described in this chapter are successfully used in practice, i.e., within a production job scheduler which manages the computing infrastructure of the Czech Centre for Education, Research and Innovation in ICT (CERIT Scientific Cloud).

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Notes

  1. 1.

    To avoid huge slowdowns of extremely short jobs, the minimal job runtime is bounded by some predefined time constant (e.g., 10 s), sometimes called a “threshold of interactivity” [10].

  2. 2.

    If a job is not the first in the queue, new jobs that arrive later may skip it in the queue. While such jobs do not delay the first job in the queue, they may delay all other jobs and the system cannot predict when a queued job will eventually run [4].

  3. 3.

    When required, the schedule can be recreated from scratch, e.g., due to a machine failure or early job completion as discussed in Sect. 3.2.1. Still, no optimization or evaluation is applied during this process.

  4. 4.

    Equalizing normalized users’ wait times is an analogy to the well known fair-share mechanism [17] which is commonly applied in production systems (see Sect. 3.3).

  5. 5.

    http://www.metacentrum.cz.

  6. 6.

    https://github.com/aleasimulator.

  7. 7.

    Except for CERIT-SC, all workloads come from the Parallel Workloads Archive [47]. CERIT-SC can be obtained at http://www.fi.muni.cz/~xklusac/workload/.

  8. 8.

    Other policies like Conservative backfilling using linear compression with RS optimization being disabled (CONS-L), First Come First Served (FCFS) [12], or Shortest Job First (SJF) [16] were also tested, but they performed poorly compared to other algorithms. Therefore, we do not present them in the figures for better visibility.

  9. 9.

    http://www.cerit-sc.cz.

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Acknowledgments

We highly appreciate the support of the Grant Agency of the Czech Republic under the grant No. P202/12/0306. The access to the MetaCentrum computing facilities and workloads provided under the program “Projects of Large Infrastructure for Research, Development, and Innovations” LM2010005 funded by the Ministry of Education, Youth, and Sports of the Czech Republic is highly appreciated.

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Authors and Affiliations

  1. Faculty of Informatics, Masaryk University, Brno, Czech Republic

    Dalibor Klusáček & Hana Rudová

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  1. Dalibor Klusáček
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Correspondence to Dalibor Klusáček .

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  1. Taif University, Taif, Saudi Arabia

    Yacine Laalaoui

  2. Faculty of Engineering and Computer Science, Concordia Univ.,Concordia Institute for Information Systems Engineering, Montreal, Québec, Canada

    Nizar Bouguila

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Klusáček, D., Rudová, H. (2015). A Metaheuristic for Optimizing the Performance and the Fairness in Job Scheduling Systems. In: Laalaoui, Y., Bouguila, N. (eds) Artificial Intelligence Applications in Information and Communication Technologies. Studies in Computational Intelligence, vol 607. Springer, Cham. https://doi.org/10.1007/978-3-319-19833-0_1

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  • DOI: https://doi.org/10.1007/978-3-319-19833-0_1

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