A Framework for Robust Traffic Engineering Using Evolutionary Computation

  • Vitor Pereira
  • Miguel Rocha
  • Paulo Cortez
  • Miguel Rio
  • Pedro Sousa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7943)


In current network infrastructures, several management tasks often require significant human intervention and can be of high complexity, having to consider several inputs to attain efficient configurations. In this perspective, this work presents an optimization framework able to automatically provide network administrators with efficient and robust routing configurations. The proposed optimization tool resorts to techniques from the field of Evolutionary Computation, where Evolutionary Algorithms (EAs) are used as optimization engines to solve the envisaged NP-hard problems. The devised methods focus on versatile and resilient aware Traffic Engineering (TE) approaches, which are integrated into an autonomous optimization framework able to assist network administrators. Some examples of the supported TE optimization methods are presented, including preventive, reactive and multi-topology solutions, taking advantage of the EAs optimization capabilities.


Robust Traffic Engineering Evolutionary Computation Network Resilience Autonomous Configuration 


  1. 1.
    Lee, K., Lim, F., Ong, B.: Building Resilient IP Networks. Cisco Press (2012)Google Scholar
  2. 2.
    Fortz, B.: Internet Traffic Engineering by Optimizing OSPF Weights. In: Proc. of IEEE INFOCOM, pp. 519–528 (2000)Google Scholar
  3. 3.
    Cariden Technologies. Building Traffic Matrices: Introduction to MATE Flow Collection. White Paper - Version 2 (October 2012)Google Scholar
  4. 4.
    Davy, A., Botvich, D., Jennings, B.: An Efficient Process for Estimation of Network Demand for QoS-aware IP Network Planning. In: Parr, G., Malone, D., Ó Foghlú, M. (eds.) IPOM 2006. LNCS, vol. 4268, pp. 120–131. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Ericsson, M., Resende, M., Pardalos, P.: A Genetic Algorithm for the Weight Setting Problem in OSPF Routing. Combinatorial Optimization 6, 299–333 (2002)MathSciNetzbMATHCrossRefGoogle Scholar
  6. 6.
    Sousa, P., Rocha, M., Rio, M., Cortez, P.: Efficient OSPF Weight Allocation for Intra-domain QoS Optimization. In: Parr, G., Malone, D., Ó Foghlú, M. (eds.) IPOM 2006. LNCS, vol. 4268, pp. 37–48. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  7. 7.
    Rocha, M., Sousa, P., Cortez, P., Rio, M.: Quality of Service Constrained Routing Optimization Using Evolutionary Computation. Applied Soft Computing 11(1), 356–364 (2011)CrossRefGoogle Scholar
  8. 8.
    Sousa, P., Cortez, P., Rio, M., Rocha, M.: Traffic Engineering Approaches Using Multicriteria Optimization Techniques. In: Masip-Bruin, X., Verchere, D., Tsaoussidis, V., Yannuzzi, M. (eds.) WWIC 2011. LNCS, vol. 6649, pp. 104–115. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Medina, A., et al.: Traffic Matrix Estimation: Existing Techniques and New Directions. Computer Communication Review 32(4), 161–176 (2002)CrossRefGoogle Scholar
  10. 10.
    Moy, J.: OSPF Version 2. RFC 2328 (Standard) (April 1998)Google Scholar
  11. 11.
    Fortz, B., Thorup, M.: Optimizing OSPF/IS-IS Weights in a Changing World. IEEE Journal on Selected Areas in Communications 20(4), 756–767 (2002)CrossRefGoogle Scholar
  12. 12.
    Dijkstra, E.: A Note on Two Problems in Connexion with Graphs. Numerische Mathematik 1(1), 269–271 (1959)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Medina, A., Lakhina, A., Matta, I., Byers, J.: BRITE: Universal Topology Generation from a User’s Perspective. Technical report 2001-003 (January 2001)Google Scholar
  14. 14.
    Feldmann, A., et al.: Deriving Traffic Demands for Operational IP Networks: Methodology and Experience. IEEE/ACM Trans. on Net. 9(3), 265–280 (2001)CrossRefGoogle Scholar
  15. 15.
    Cortez, P., Rio, M., Rocha, M., Sousa, P.: Multiscale Internet Traffic Forecasting using Neural Networks and Time Series Methods. Expert Systems 29(2), 143–155 (2012)Google Scholar
  16. 16.
    Fortz, B., Thorup, M.: Robust Optimization of OSPF/IS-IS Weights. In: Proceedings of the International Network Optimization Conference, pp. 225–230 (2003)Google Scholar
  17. 17.
    Sqalli, M., Sait, S., Asadullah, S.: OSPF Weight Setting Optimization for Single Link Failures. Int. Journal of Computer Networks & Comm. 3(1), 168–183 (2011)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2013

Authors and Affiliations

  • Vitor Pereira
    • 1
  • Miguel Rocha
    • 2
  • Paulo Cortez
    • 3
  • Miguel Rio
    • 4
  • Pedro Sousa
    • 1
  1. 1.Centro Algoritmi/Dep. of InformaticsUniversity of MinhoPortugal
  2. 2.CCTC/Dep. of InformaticsUniversity of MinhoPortugal
  3. 3.Centro Algoritmi/Dep. of Information SystemsUniversity of MinhoPortugal
  4. 4.Dep. of Electronic and Electrical EngineeringUCLUK

Personalised recommendations