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A genetic-based fault-tolerant routing strategy for multiprocessor networks

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Parallel and Distributed Processing (IPPS 1999)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1586))

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Abstract

We have investigated the adaptation of AI-based search techniques as topology-independent fault-tolerant routing strategies on multiprocessor networks [9]. The results showed that these search techniques are suitable for adaptation, as fault-tolerant routing strategies with the exception that the routes obtained were non-minimal. In this research, we investigate the adaptation of a genetic-heuristic algorithm combination as a fault-tolerant routing strategy. Our results show that such a hybrid strategy results in a viable fault-tolerant routing strategy, which produces minimal or near-minimal routes with a corresponding significant reduction in the number of redundant node traversals. Under certain fault conditions, this new hybrid routing strategy outperforms the purely heuristic ones.

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Authors and Affiliations

  1. School of Applied Science, Nanyang Technological University, Nanyang Avenue, 639798, Singapore

    Peter K. K. Loh & Venson Shaw

Authors
  1. Peter K. K. Loh
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  2. Venson Shaw
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Editor information

José Rolim Frank Mueller Albert Y. Zomaya Fikret Ercal Stephan Olariu Binoy Ravindran Jan Gustafsson Hiroaki Takada Ron Olsson Laxmikant V. Kale Pete Beckman Matthew Haines Hossam ElGindy Denis Caromel Serge Chaumette Geoffrey Fox Yi Pan Keqin Li Tao Yang G. Chiola G. Conte L. V. Mancini Domenique Méry Beverly Sanders Devesh Bhatt Viktor Prasanna

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© 1999 Springer-Verlag

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Loh, P.K.K., Shaw, V. (1999). A genetic-based fault-tolerant routing strategy for multiprocessor networks. In: Rolim, J., et al. Parallel and Distributed Processing. IPPS 1999. Lecture Notes in Computer Science, vol 1586. Springer, Berlin, Heidelberg . https://doi.org/10.1007/BFb0097903

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  • DOI: https://doi.org/10.1007/BFb0097903

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-65831-3

  • Online ISBN: 978-3-540-48932-0

  • eBook Packages: Springer Book Archive

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