Advertisement

Multitree-Multiobjective Multicast Routing for Traffic Engineering

  • Joel Prieto
  • Benjamín Barán
  • Jorge Crichigno
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 217)

Abstract

This paper presents a new traffic engineering multitree-multiobjective multicast routing algorithm (M-MMA) that solves for the first time the GMM model for Dynamic Multicast Groups. Multitree traffic engineering uses several trees to transmit a multicast demand from a source to a set of destinations in order to balance traffic load, improving network resource utilization. Experimental results obtained by simulations using eight real net-work topologies show that this new approach gets trade off solutions while simultaneously considering five objective functions. As expected, when M-MMA is compared to an equivalent singletree alternative, it accommodates more traffic demand in a high traffic saturated network.

Keywords

Destination Node Pareto Front Multicast Tree Multicast Group Traffic Demand 
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.

References

  1. 1.
    A. Tanenbaum: Computer Networks, Prentice Hall, 2003.Google Scholar
  2. 2.
    D. Awdeuche, J. Malcolm, J. Agogbua, M. O’Dell, and J. McManus: Requirements For Traffic Engineering Over MPLS. RFC 2702. 1999.Google Scholar
  3. 3.
    J. Crichigno, and B. Barán: Multiobjective Multicast Routing Algorithm. IEEE ICT’2004, Ceará, Brazil, 2004.Google Scholar
  4. 4.
    B. Barán, and J. Crichigno: A Multicast Routing Algorithm Using Multiobjective Optimization. IEEE ICT’2004, Ceará, Brazil, 2004.Google Scholar
  5. 5.
    J. Crichigno, and B Barán: Multiobjective multicast routing algorithm for traffic engineering. IEEE ICCCN 2004, Chicago USA.Google Scholar
  6. 6.
    J. Crichigno, F. Talavera, J. Prieto, and B. Barán: Enrutamiento Multicast utilizando Optimización Multiobjetivo. CACIC’2004, Buenos Aires, Argentina, 2004. pp. 147–158.Google Scholar
  7. 7.
    F. Talavera, J. Crichigno, and B. Barán: Policies for Dynamical MultiObjective Environment of Multicast Traffic Engineering. IEEE ICT 2005, South Africa.Google Scholar
  8. 8.
    Y. Donoso, R. Fabregat, and J. Marzo: Multi-Objective Optimization Algorithm for Multi-cast Routing with Traffic Engineering. IEEE ICN 2004.Google Scholar
  9. 9.
    R. Fabregat, Y. Donoso, J.L. Marzo, and A. Ariza: A Multi-Objective Multipath Routing Algorithm for Multicast Flows. SPECTS 2004.Google Scholar
  10. 10.
    R. Fabregat, Y. Donoso, F. Solano, and J.L. Marzo: Multitree Routing for Multicast Flows: A Genetic Algorithm Approach. CCIA 2004.Google Scholar
  11. 11.
    Y. Donoso, R. Fabregat, F. Solano, J. L. Marzo, and B. Barán: Generalized Multiobjective Multitree model for Dynamic Multicast Groups. IEEE ICC 2005, Seul Corea.Google Scholar
  12. 12.
    A. Roy, N. Banerjee, and S. Das: An efficient Multi-Objective QoS-Routing Algorithm for Wireless Multicasting. INFOCOM 2002.Google Scholar
  13. 13.
    X. Cui, C. Lin, and Y. Wei: A Multiobjective Model for QoS Multicast Routing Based on Genetic Algorithm. ICCNMC 2003.Google Scholar
  14. 14.
    E. Zitzler, and L. Thiele: Multiobjective Evolutionary Algorithms: A comparative Case Study and the Strength Pareto Approach. IEEE Trans. Evolutionary Computation, Vol. 3, No. 4, 1999, pp. 257–271.CrossRefGoogle Scholar
  15. 15.
    P. Texeira de Araújo, and G. Barbosa Oliveira: Algoritmos Genéticos Aplicados al Ruteamiento Multicast en Internet, Contemplando Requisitos de Calidad de Servicio e Ingeniería de Tráfico. VII Brazilian Symposium on Neural Networks (SBRN’02), 2002.Google Scholar
  16. 16.
    W. Zhengying, S. Bingxin, and Z. Erdun: Bandwidth-delay-constraint least cost multicast routing based on heuristic genetic algorithm. Computer Communications. 2001. Vol. 24. pp. 685–692.CrossRefGoogle Scholar
  17. 17.
    Y. Seok, Y. Lee, Y Choi, and C. Kim: Explicit Multicast Routing Algorithm for Con-strained Traffic Engineering. IEEE ISCC’02. Italia, 2002.Google Scholar
  18. 18.
    R. Fabregat, Y. Donoso, B. Barán, F. Solano, and J.L. Marzo: Multi-objective Optimization Scheme for Multicast Flows: a Survey, a Model and a MOEA Solution. IFIP/ LANC 2005.Google Scholar
  19. 19.
    Spring, R Mahajan, and D. Wetheral: Measuring ISP topologies with Rocketfuel. Proceedings of the ACM SIGCOMM’02 Conference, 2002.Google Scholar

Copyright information

© International Federation for Information Processing 2006

Authors and Affiliations

  • Joel Prieto
    • 1
  • Benjamín Barán
    • 1
    • 2
  • Jorge Crichigno
    • 1
  1. 1.Tte. Cantaluppi y VillalónCatholic University of AsunciónAsunciónParaguay
  2. 2.National Computer CentreNational University of AsunciónSan LorenzoParaguay

Personalised recommendations