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Multiprocessor Scheduling with Support by Genetic Algorithms - based Learning Classifier System

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Parallel and Distributed Processing (IPDPS 2000)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1800))

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Abstract

The paper proposes using genetic algorithms - based learning classifier system (CS) to solve multiprocessor scheduling problem. After initial mapping tasks of a parallel program into processors of a parallel system, the agents associated with tasks perform migration to find an allocation providing the minimal execution time of the program. Decisions concerning agents’ actions are produced by the CS, upon a presentation by an agent information about its current situation. Results of experimental study of the scheduler are presented.

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References

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Author information

Authors and Affiliations

  1. Polish -Japanese Institute of Information Technologies, Koszykowa 86, 02-008, Warsaw, Poland

    Jerzy P. Nowacki & Franciszek Seredyński

  2. Polish Telecom, Sw. Barbary 2, 00-686, Warsaw, Poland

    Grzegorz Pycka

  3. Institute of Computer Science, Polish Academy of Sciences, Ordona 21, 01-237, Warsaw, Poland

    Franciszek Seredyński

Authors
  1. Jerzy P. Nowacki
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  2. Grzegorz Pycka
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  3. Franciszek Seredyński
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Editor information

Editors and Affiliations

  1. Centre Universitaire d’Informatique, Université de Genève, 24, rue Général Dufour, CH-1211, Genève 4, Switzerland

    José Rolim

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© 2000 Springer-Verlag Berlin Heidelberg

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Nowacki, J.P., Pycka, G., Seredyński, F. (2000). Multiprocessor Scheduling with Support by Genetic Algorithms - based Learning Classifier System. In: Rolim, J. (eds) Parallel and Distributed Processing. IPDPS 2000. Lecture Notes in Computer Science, vol 1800. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45591-4_82

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  • DOI: https://doi.org/10.1007/3-540-45591-4_82

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67442-9

  • Online ISBN: 978-3-540-45591-2

  • eBook Packages: Springer Book Archive

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