A Genetic Algorithm Based on Cell Loss for Dynamic Routing in ATM Networks

  • P. Cortes
  • J. Muñuzuri
  • J. Larrañeta
  • L. Onieva

Abstract

New B-ISDN will have to ensure a high standard of quality of service measured as delay in the communications, guarantee of safety communications and of course no loss of information. Although the routing optimization problem has been previously dealt with in the bibliography, not many works have been presented according to the routing in ATM networks using cell loss as the overall criterion. Here a new model based on cell loss is presented for an ATM network with matrix switches and queues at the end. A quickly evaluated genetic algorithm is proposed to solve the model.

Keywords

Cell Loss Asynchronous Transfer Mode Demand Scenario Switching Fabric Virtual Path 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aboelela, E. and Douligeris, C. (1999) Fuzzy generalized network approach for solving an optimization model for routing in B-ISDN. Telecommunication Systems 12, 237–263.CrossRefGoogle Scholar
  2. 2.
    Amiri, A. and Pirkul, H. (1999) Routing and capacity assignment in backbone communication networks under time varying traffic conditions. European Journal of Operational Research 117, 15–29.MATHCrossRefGoogle Scholar
  3. 3.
    Chou, L-D and Wu, J.L. (1998) Bandwidth allocation of virtual paths using neural networks based genetic algorithms. IEE Proceedings on Communications Vol 145, No.1,33–39.Google Scholar
  4. 4.
    Dahl, G., Martin, A. and Stoer, M. (1999) Routing through virtual paths in layered telecommunication networks. Operations Research 47, 693–702.MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Ghanwani, A. (1998) Neural and delay based heuristics for the Steiner problem in networks. European Journal of Operational Research 108, 241–265.MATHCrossRefGoogle Scholar
  6. 6.
    Giroux, N. and Ganti, S. (1999) Quality of Service in ATM Networks (Prentice Hall).Google Scholar
  7. 7.
    Grefenstette, J.J. (1987) Incorporating problem-specific knowledge into genetic algorithms, in: L. Davis, ed., Genetic Algorithms and their Applications (Morgan Kaufinann, Los Angeles).Google Scholar
  8. 9.
    Lee, M-J and Vee, J-R (1994) An algorithm for optimal minimax routing in ATM networks. Annals of Operations Research 49, 185–206.MATHCrossRefGoogle Scholar
  9. 10.
    Medova, E. (1998) Chance-constrained stochastic programming for integrated services network management. Annals of Operations Research 81,213–229.MathSciNetMATHCrossRefGoogle Scholar
  10. 11.
    Pitts, J.M. and Schormans, J.A. (1996) Introduction to ATM Design and Performance (Wiley&Sons).Google Scholar
  11. 12.
    Wang, C. and Weissler, P.N. (1995) The use of artificial neural networks for optimal message routing. IEEE Network, March-April,16–24.Google Scholar

Copyright information

© Springer-Verlag London 2002

Authors and Affiliations

  • P. Cortes
    • 1
  • J. Muñuzuri
    • 1
  • J. Larrañeta
    • 1
  • L. Onieva
    • 1
  1. 1.Escuela Superior Ingenieros. Grupo Ingeniería OrganizaciónSeville UniversitySevillaSpain

Personalised recommendations