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IFIP International Conference on Autonomous Infrastructure, Management and Security

AIMS 2012: Dependable Networks and Services pp 26–37Cite as

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A Fuzzy Reinforcement Learning Approach for Pre-Congestion Notification Based Admission Control

A Fuzzy Reinforcement Learning Approach for Pre-Congestion Notification Based Admission Control

  • Stylianos Georgoulas20,
  • Klaus Moessner20,
  • Alexis Mansour20,
  • Menelaos Pissarides20 &
  • …
  • Panagiotis Spapis21 
  • Conference paper
  • 1164 Accesses

  • 3 Citations

Part of the Lecture Notes in Computer Science book series (LNCCN,volume 7279)

Abstract

Admission control aims to compensate for the inability of slow-changing network configurations to react rapidly enough to load fluctuations. Even though many admission control approaches exist, most of them suffer from the fact that they are based on some very rigid assumptions about the per-flow and aggregate underlying traffic models, requiring manual reconfiguration of their parameters in a “trial and error” fashion when these original assumptions stop being valid. In this paper we present a fuzzy reinforcement learning admission control approach based on the increasingly popular Pre-Congestion Notification framework that requires no a priori knowledge about traffic flow characteristics, traffic models and flow dynamics. By means of simulations we show that the scheme can perform well under a variety of traffic and load conditions and adapt its behavior accordingly without requiring any overly complicated operations and with no need for manual and frequent reconfigurations.

Keywords

  • Admission Control
  • Pre-Congestion Notification
  • Fuzzy Logic
  • Reinforcement Learning
  • Quality of Service
  • Autonomic Management

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Author information

Authors and Affiliations

  1. Centre for Communication Systems Research, Faculty of Engineering and Physical Sciences, University of Surrey, Guildford, Surrey, GU2 7XH, United Kingdom

    Stylianos Georgoulas, Klaus Moessner, Alexis Mansour & Menelaos Pissarides

  2. Department of Informatics and Telecommunications, National and Kapodistrian University of Athens, Panepistimiopolis Ilissia, Athens, 15784, Greece

    Panagiotis Spapis

Authors
  1. Stylianos Georgoulas
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  2. Klaus Moessner
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  3. Alexis Mansour
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  4. Menelaos Pissarides
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  5. Panagiotis Spapis
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Editor information

Editors and Affiliations

  1. Faculty of Electrical Engineering, Mathematics, and Computer Science, University of Twente, P.O. Box 217, 7500 AE, Enschede, The Netherlands

    Ramin Sadre

  2. Institute of Computer Science, Masaryk University, Botanická 68a, 602 00, Brno, Czech Republic

    Jiří Novotný & Pavel Čeleda & 

  3. Institut für Informatik (IFI), Universität Zürich, Binzmühlestraße 14, 8050, Zürich, Switzerland

    Martin Waldburger

  4. Institut für Informatik (IFI), Universität Zürich, Binzmühlestrasse 14, 8050, Zürich, Switzerland

    Burkhard Stiller

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© 2012 IFIP International Federation for Information Processing

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Cite this paper

Georgoulas, S., Moessner, K., Mansour, A., Pissarides, M., Spapis, P. (2012). A Fuzzy Reinforcement Learning Approach for Pre-Congestion Notification Based Admission Control. In: Sadre, R., Novotný, J., Čeleda, P., Waldburger, M., Stiller, B. (eds) Dependable Networks and Services. AIMS 2012. Lecture Notes in Computer Science, vol 7279. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30633-4_4

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  • DOI: https://doi.org/10.1007/978-3-642-30633-4_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30632-7

  • Online ISBN: 978-3-642-30633-4

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