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European Conference on Parallel Processing

Euro-Par 2012: Euro-Par 2012 Parallel Processing pp 116–127Cite as

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Job Scheduling Using Successive Linear Programming Approximations of a Sparse Model

Job Scheduling Using Successive Linear Programming Approximations of a Sparse Model

  • Stephane Chretien19,
  • Jean-Marc Nicod20,
  • Laurent Philippe20,
  • Veronika Rehn-Sonigo20 &
  • …
  • Lamiel Toch20 
  • Conference paper
  • 2971 Accesses

  • 2 Citations

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7484)

Abstract

In this paper we tackle the well-known problem of scheduling a collection of parallel jobs on a set of processors either in a cluster or in a multiprocessor computer. For the makespan objective, i.e., the completion time of the last job, this problem has been shown to be NP-Hard and several heuristics have already been proposed to minimize the execution time. We introduce a novel approach based on successive linear programming (LP) approximations of a sparse model. The idea is to relax an integer linear program and use ℓ p norm-based operators to force the solver to find almost-integer solutions that can be assimilated to an integer solution. We consider the case where jobs are either rigid or moldable. A rigid parallel job is performed with a predefined number of processors while a moldable job can define the number of processors that it is using just before it starts its execution. We compare the scheduling approach with the classic Largest Task First list based algorithm and we show that our approach provides good results for small instances of the problem. The contributions of this paper are both the integration of mathematical methods in the scheduling world and the design of a promising approach which gives good results for scheduling problems with less than a hundred processors.

Keywords

  • Schedule Problem
  • Completion Time
  • Integer Linear Program
  • Compress Sensing
  • Virtual Machine Migration

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Author information

Authors and Affiliations

  1. Department of Mathematics, Université de Franche-Comté, Besançon, France

    Stephane Chretien

  2. FEMTO-ST Institute, UMR CNRS / UFC / ENSMM / UTBM, Besançon, France

    Jean-Marc Nicod, Laurent Philippe, Veronika Rehn-Sonigo & Lamiel Toch

Authors
  1. Stephane Chretien
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  2. Jean-Marc Nicod
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  3. Laurent Philippe
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  4. Veronika Rehn-Sonigo
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  5. Lamiel Toch
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Editor information

Editors and Affiliations

  1. University of Patras, Computer Technology Institute and Press “Diophantus”,, N. Kazantzaki, 26504, Rio, Greece

    Christos Kaklamanis

  2. University of Patras, University Building B, 26504, Rio, Greece

    Theodore Papatheodorou

  3. Computer Technology Institute and Press “Diophantus”, University of Patras, N. Kazantzaki, 26504, Rio, Greece

    Paul G. Spirakis

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© 2012 Springer-Verlag Berlin Heidelberg

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Chretien, S., Nicod, JM., Philippe, L., Rehn-Sonigo, V., Toch, L. (2012). Job Scheduling Using Successive Linear Programming Approximations of a Sparse Model. In: Kaklamanis, C., Papatheodorou, T., Spirakis, P.G. (eds) Euro-Par 2012 Parallel Processing. Euro-Par 2012. Lecture Notes in Computer Science, vol 7484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32820-6_14

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  • DOI: https://doi.org/10.1007/978-3-642-32820-6_14

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