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Monte Carlo Search Algorithms for Network Traffic Engineering

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Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track (ECML PKDD 2021)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12978))

Abstract

The aim of Traffic Engineering is to provide routing configurations in networks such that the used resources are minimized while maintaining a high level of quality of service (QoS). Among the optimization problems arising in this domain, we address in this paper the one related to setting weights in networks that are based on shortest path routing protocols (OSPF, IS-IS). Finding weights that induce efficient routing paths (e.g. that minimize the maximum congested link) is a computationally hard problem.

We propose to use Monte Carlo Search for the first time for this problem. More specifically we apply Nested Rollout Policy Adaptation (NRPA). We also extend NRPA with the force_exploration algorithm to improve the results. In comparison to other algorithms NRPA scales better with the size of the instance and can be easily extended to take into account additional constraints (cost utilization, delay, ...) or linear/non-linear optimization criteria. For difficult instances the optimum is not known but a lower bound can be computed. NRPA gives results close to the lower bound on a standard dataset of telecommunication networks.

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Author information

Authors and Affiliations

  1. Orange Labs, Châtillon, France

    Chen Dang & Morgan Chopin

  2. Université Paris-Dauphine, PSL Research University, CNRS, UMR 7243, LAMSADE, 75016, Paris, France

    Chen Dang, Cristina Bazgan & Tristan Cazenave

  3. Sorbonne Université, CNRS, UMR 7606, LIP6, 75005, Paris, France

    Pierre-Henri Wuillemin

Authors
  1. Chen Dang
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  2. Cristina Bazgan
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  3. Tristan Cazenave
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  4. Morgan Chopin
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  5. Pierre-Henri Wuillemin
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Corresponding author

Correspondence to Chen Dang .

Editor information

Editors and Affiliations

  1. Facebook AI, Seattle, WA, USA

    Yuxiao Dong

  2. Torre Telefonica, Barcelona, Spain

    Nicolas Kourtellis

  3. Bielefeld University, CITEC, Bielefeld, Germany

    Barbara Hammer

  4. Basque Center for Applied Mathematics, Bilbao, Spain

    Jose A. Lozano

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Dang, C., Bazgan, C., Cazenave, T., Chopin, M., Wuillemin, PH. (2021). Monte Carlo Search Algorithms for Network Traffic Engineering. In: Dong, Y., Kourtellis, N., Hammer, B., Lozano, J.A. (eds) Machine Learning and Knowledge Discovery in Databases. Applied Data Science Track. ECML PKDD 2021. Lecture Notes in Computer Science(), vol 12978. Springer, Cham. https://doi.org/10.1007/978-3-030-86514-6_30

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  • DOI: https://doi.org/10.1007/978-3-030-86514-6_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86513-9

  • Online ISBN: 978-3-030-86514-6

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