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MPLS Online Routing Optimization Using Prediction

  • Abutaleb Abdelmohdi Turky
  • Andreas Mitschele-Thiel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5425)

Abstract

This paper presents an efficient enhancement to the online routing algorithms for the computation of Labeled Switching Paths (LSPs) in Multiprotocol Label Switching (MPLS) based networks. To achieve that, an adaptive predictor is used to predict the future link loads. Then the predicted values are incorporated in the link weights formula. Our contribution is to propose a new idea that depends on the knowledge of the future link loads to achieve a routing that can be done much more efficiently. According to the non-linear nature of traffic, we use a Feed Forward Neural Network (FFNN) to build an accurate traffic predictor that is able to capture the actual traffic behaviour. We study two performance parameters: the rejection ratio and the percentage of accepted bandwidth in different load conditions. Our proposed algorithm in general, reduces the rejection ratio of requests and achieves higher throughput when compared to CSPF and WSP algorithms.

Keywords

MPLS traffic engineering routing neural network 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Abutaleb Abdelmohdi Turky
    • 1
  • Andreas Mitschele-Thiel
    • 1
  1. 1.Integrated HW/SW Systems GroupIlmenau University of TechnologyIlmenauGermany

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