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Supercomputer Scheduling with Combined Evolutionary Techniques

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Metaheuristics for Scheduling in Distributed Computing Environments

Part of the book series: Studies in Computational Intelligence ((SCI,volume 146))

Summary

Scheduling is a very important problem in many real-world scenarios. In the case of supercomputers it is even more important because available resources are limited and expensive. The optimal use of supercomputer facilities is a critical question. We have found that the definitions of traditional scheduling problems do not provide an appropriate description for Supercomputer Scheduling (SCS). Thus, a new definition for this kind of problems is proposed. The research already done in the field of other scheduling problems can be modified to be applied in this new scenario. Nevertheless, new techniques can also be developed. Thus, we have proposed a theoretical framework to combine multi evolutionary algorithms called Multiple Offspring Sampling (MOS). We have used this approach to combine multiple codings and genetic operators in this scheduling problem. To summarise: first, we introduce a formal definition of supercomputer scheduling; second, we propose Multiple Offspring Sampling formalism; and third, we have carried out an experimental test to compare the performance of this formalism to solve SCS problems against traditional (non-combinatorial) techniques and single genetic algorithms.

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Authors and Affiliations

  1. CeSViMa, Department of Computer Architecture and Technology, Universidad Politécnica de Madrid, Campus de Montegancedo S/N, Madrid, 28660, Spain

    A. LaTorre, J. M. Peña, V. Robles & P. de Miguel

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  1. A. LaTorre
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  2. J. M. Peña
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  3. V. Robles
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  4. P. de Miguel
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Fatos Xhafa Ajith Abraham

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LaTorre, A., Peña, J.M., Robles, V., de Miguel, P. (2008). Supercomputer Scheduling with Combined Evolutionary Techniques. In: Xhafa, F., Abraham, A. (eds) Metaheuristics for Scheduling in Distributed Computing Environments. Studies in Computational Intelligence, vol 146. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69277-5_4

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  • DOI: https://doi.org/10.1007/978-3-540-69277-5_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69260-7

  • Online ISBN: 978-3-540-69277-5

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