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Fuzzy Logic for controlling call admission in ATM networks

  • M. Fontaine
  • D. G. Smith
Chapter
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT)

Abstract

An essential function of ATM is Connection admission control (CAC). To achieve higher network utilisation, bursty calls are statistically multiplexed on a common link. We present a new CAC scheme based on fuzzy logic (FL) and artificial neural networks (ANNs) to exploit this key feature of ATM networks. Defining a hybrid intelligent system enables us to take advantage of both the learning capabilities of ANNs and the interpretability properties of FL. The ANN is used in the learning phase to automatically tune the fuzzy system (i.e. to define the fuzzy rules and the membership functions) whilst during the control phase, the fuzzy system forecasts the QoS values. The CAC procedure is able to determine on line the cell loss probability (CLP) that a connection will exhibit when accepted into the ATM network. Because of its possible hardware implementation as well as its adaptive and learning capabilities, this scheme constitutes a good candidate for a robust real-time CAC.

Keywords

ATM networks CAC Fuzzy Logic Artificial Neural Networks 

References

  1. Boyer, J. Gravey, A. and Sevilla, K. (1995) Resource allocation for worst case traffic in ATM networks. In First Workshop on ATM Traffic Management WATM’95, Paris, pages 3–19.Google Scholar
  2. Cheng, R.-G. (1996) Design of a fuzzy traffic controller for ATM networks. IEEE/ACM Transactions on Networking, 4 (3): 460–469.CrossRefGoogle Scholar
  3. Elwalid, A. Mitra, D. and Wentworth, R.H. (1995) A new approach for allocating buffers and bandwidth to heterogeneous, regulated traffic in an ATM node. IEEE JSAC, 13 (6): 1115–1127.Google Scholar
  4. Fontaine, M and Smith, D.G. (1996a) A neuro-fuzzy approach to connection admission control in ATM networks. In IEE Thirteenth UK teletraffic symposium, Glasgow.Google Scholar
  5. Fontaine, M and Smith, D.G. (1996b) Bandwidth allocation and connection admission control in ATM networks. Electronics and Communication Engineering Journal, 8 (4): 156–164.CrossRefGoogle Scholar
  6. Fontaine, M and Smith, D.G. (1997) Intelligent techniques and telecommunications networks. In European Symposium on Intelligent Techniques, Bari, Italy.Google Scholar
  7. Kosko, B. (1992) Neural Networks and Fuzzy Systems: a dynamical systems approach to machine intelligence. Prentice-Hall International Editions.zbMATHGoogle Scholar
  8. Mendel, J.M. (1995) Fuzzy logic systems for engineering: a tutorial. Proceedings of the IEEE, 83 (3): 345–377.CrossRefGoogle Scholar
  9. Mitrou, N.M Kontovasilis, K.P. Kroner, H. and Iversen, V.B. (1994) Statistical multiplexing, bandwidth allocation strategies and connection admission control in ATM networks. European Transactions on Telecommunications, 5 (2): 161–175.CrossRefGoogle Scholar
  10. Muller, C. Magill, E.H. Prosser, P. and Smith, D.G. (1993) Artificial intelligence in Telecommunications. In IEEE Globecom’93, Houston.Google Scholar
  11. Nauck, D. and Kruse, R. (1993) A fuzzy neural network learning fuzzy control rules and membership functions by fuzzy error backpropagation. In IEEE ICNN’93, San Francisco.Google Scholar
  12. Reeve, J.M. and Mars, P. (1996) A review of non-symbolic artificial intelligence techniques for network management and control. In IEE Thirteenth UK teletraffic symposium, Glasgow.Google Scholar
  13. Roberts, J.W. (1991) Variable-Bit-Rate traffic control in B-ISDN. IEEE Communications Magazine, pages 50–56.Google Scholar
  14. Takagi, T and Sugeno, M. (1983) Derivation of fuzzy control rules from human operator’s control actions. In Proc. of the IFAC Symp. on Fuzzy Information, Knowledge Representation and Decision Analysis, pages 55–60.Google Scholar
  15. Witters, J. Nielsen, A.B. Elvang, R. Kroeze, J. Petterson, H. Aarstad, E. and Renger, T. (1994) Results of experiments on traffic control using real applications. Technical report, Race project R 2061Google Scholar
  16. Zadeh, L.A. (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE transactions on Systems, Man, and Cybernetics, 3 (1): 28–44.zbMATHMathSciNetCrossRefGoogle Scholar

Copyright information

© IFIP 1998

Authors and Affiliations

  • M. Fontaine
    • 1
  • D. G. Smith
    • 1
  1. 1.University of StrathclydeDepartment of Electronic & Electrical EngineeringUK

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