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European Conference on Parallel Processing

Euro-Par 2011: Euro-Par 2011: Parallel Processing Workshops pp 419–428Cite as

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A Genetic Algorithm with Communication Costs to Schedule Workflows on a SOA-Grid

A Genetic Algorithm with Communication Costs to Schedule Workflows on a SOA-Grid

  • Jean-Marc Nicod30,
  • Laurent Philippe30 &
  • Lamiel Toch30 
  • Conference paper
  • 1312 Accesses

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7155)

Abstract

In this paper we study the problem of scheduling a collection of workflows, identical or not, on a SOA (Service Oriented Architecture) grid . A workflow (job) is represented by a directed acyclic graph (DAG) with typed tasks. All of the grid hosts are able to process a set of typed tasks with unrelated processing costs and are able to transmit files through communication links for which the communication times are not negligible. The goal of our study is to minimize the maximum completion time (makespan) of the workflows. To solve this problem we propose a genetic approach. The contributions of this paper are both the design of a Genetic Algorithm taking the communication costs into account and its performance analysis.

Keywords

  • Genetic Algorithm
  • Directed Acyclic Graph
  • Communication Cost
  • Communication Link
  • Precedence Constraint

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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References

  1. Beaumont, O., Legrand, A., Marchal, L., Robert, Y.: Assessing the impact and limits of steady-state scheduling for mixed task and data parallelism on heterogeneous platforms. In: HeteroPar 2004, pp. 296–302 (2004)

    Google Scholar 

  2. Caron, E., Desprez, F.: Diet: A scalable toolbox to build network enabled servers on the grid. IJHPCA 20(3), 335–352 (2006)

    Google Scholar 

  3. Casanova, H.: Modeling large-scale platforms for the analysis and the simulation of scheduling strategies. In: APDCM 2004 (2004)

    Google Scholar 

  4. Casanova, H., Legrand, A., Quinson, M.: Simgrid: A generic framework for large-scale distributed experiments. In: UKSIM 2008, pp. 126–131 (2008)

    Google Scholar 

  5. Daoud, M., Kharma, N.: GATS 1.0: A Novel GA-based Scheduling Algorithm for Task Scheduling on Heterogeneous Processor Nets. In: Genetic And Evolutionary Computation Conference (2005)

    Google Scholar 

  6. Diakité, S., Marchal, L., Nicod, J.-M., Philippe, L.: Steady-State for Batches of Identical Task Trees. In: Sips, H., Epema, D., Lin, H.-X. (eds.) Euro-Par 2009. LNCS, vol. 5704, pp. 203–215. Springer, Heidelberg (2009)

    CrossRef  Google Scholar 

  7. Diakité, S., Nicod, J.-M., Philippe, L.: Comparison of batch scheduling for identical multi-tasks jobs on heterogeneous platforms. In: PDP 2008, Toulouse, France, pp. 374–378 (2008)

    Google Scholar 

  8. Deelman, E., et al.: Pegasus: a framework for mapping complex scientific workflows onto distributed systems. Scientific Programming Journal 13, 219–237 (2005)

    Google Scholar 

  9. Braun, T.-D., et al.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. JPDC 61, 810–837 (2001)

    Google Scholar 

  10. Goh, C.K., Teoh, E.J., Tan, K.C.: A hybrid evolutionary approach for heterogeneous multiprocessor scheduling. Soft Comput. 13, 833–846 (2009)

    CrossRef  Google Scholar 

  11. Kwok, Y., Ahmad, I.: Dynamic critical-path scheduling: An effective technique for allocating task graphs to multi-processors. In: PDS, pp. 506 – 521 (1996)

    Google Scholar 

  12. Kwok, Y.-K., Ahmad, I.: Static Scheduling Algorithms for Allocating Task Graphs to Multiprocessors. ACM Computing Surveys 31(4), 406–471 (1999)

    CrossRef  Google Scholar 

  13. Lenstra, J.K., Rinnooy Kan, A.H.G.: Complexity of scheduling under precedence constraints. Operations Research 26(1), 22–35 (1978)

    CrossRef  MathSciNet  MATH  Google Scholar 

  14. Mandal, A., Kennedy, K., Koelbel, C., Marin, G., Mellor-Crummey, J., Liu, B., Johnsson, L.: Scheduling strategies for mapping application workflows onto the grid. In: HPDC 2005, NC, Triangle Park, USA, pp. 125–134 (July 2005)

    Google Scholar 

  15. Tanaka, Y., Takemiya, H., Nakada, H., Sekiguchi, S.: Design, implementation and performance evaluation of gridrpc programming middleware for a large-scale computational grid. In: GRID 2004, pp. 298–305 (2004)

    Google Scholar 

  16. Taylor, I.-J., Deelman, E., Gannon, D.-B., Shields, M.: Workflows for e-Science (2007)

    Google Scholar 

  17. Topcuouglu, H., Hariri, S., Wu, M.: Performance-effective and low-complexity task scheduling for heterogeneous computing. In: PDS, pp. 260–274 (2002)

    Google Scholar 

  18. Zhao, H., Sakellariou, R.: Scheduling multiple DAGs onto heterogeneous systems. In: HCW 2006, Rhodes, Greece (2006)

    Google Scholar 

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Author information

Authors and Affiliations

  1. LIFC Laboratory, Université de Franche-Comté, Besançon, France

    Jean-Marc Nicod, Laurent Philippe & Lamiel Toch

Authors
  1. Jean-Marc Nicod
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  2. Laurent Philippe
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  3. Lamiel Toch
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Editor information

Editors and Affiliations

  1. Scilytics, Koellnerhofgasse 3/15A, 1010, Vienna, Austria

    Michael Alexander

  2. ICAR-CNR, Via P. Castellino, 111, 80131, Napoli, Italy

    Pasqua D’Ambra

  3. University of Amsterdam, 1090, Amsterdam, Netherlands

    Adam Belloum

  4. Innovative Computing Laboratory, The University of Tennessee, USA

    George Bosilca

  5. Department of Experimental Medicine and Clinic, University Magna Græcia, 88100, Catanzaro, Italy

    Mario Cannataro

  6. Computer Science Department, University of Pisa, Italy

    Marco Danelutto

  7. Second University of Naples, Italy

    Beniamino Di Martino

  8. TU München, Boltzmannstr. 3, 85748, Garching, Germany

    Michael Gerndt

  9. Equipe Runtime, INRIA Bordeaux Sud-Ouest, 33405, Talence Cedex, France

    Emmanuel Jeannot & Raymond Namyst & 

  10. Equipe HIEPACS, INRIA Bordeaux Sud-Ouest, 33405, Talence Cedex, France

    Jean Roman

  11. Oak Ridge National Laboratory, Computer Science and Mathematics Division, 37831-6164, Oak Ridge, TN, USA

    Stephen L. Scott

  12. Department of Scientific Computing, University of Vienna, Nordbergstr. 15/3C, 1090, Vienna, Austrial

    Jesper Larsson Traff

  13. Computer Science and Mathematics Division, Oak Ridge National Laboratory, 37831, Oak Ridge, TN, USA

    Geoffroy Vallée

  14. Technische Universität München, Germany

    Josef Weidendorfer

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© 2012 Springer-Verlag Berlin Heidelberg

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Cite this paper

Nicod, JM., Philippe, L., Toch, L. (2012). A Genetic Algorithm with Communication Costs to Schedule Workflows on a SOA-Grid. In: Alexander, M., et al. Euro-Par 2011: Parallel Processing Workshops. Euro-Par 2011. Lecture Notes in Computer Science, vol 7155. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29737-3_47

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  • DOI: https://doi.org/10.1007/978-3-642-29737-3_47

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  • Print ISBN: 978-3-642-29736-6

  • Online ISBN: 978-3-642-29737-3

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