Quasi-hierarchical Evolutionary Algorithm for Flow Optimization in Survivable MPLS Networks

  • Michał Przewoźniczek
  • Krzysztof Walkowiak
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4707)


In this paper we address the problem of working paths optimization in survivable MPLS network. We focus on an existing facility network, in which only network flows can be optimized to provide network survivability using the local repair strategy. The main goal of our work is to develop an effective evolutionary algorithm (EA) for considered optimization problem. The novelty is that the proposed algorithm consists of two levels. The “high” level applies typical EA operators. The “low” level idea is based on the hierarchical algorithm idea. However, the presented approach is not a classical hierarchical algorithm. Therefore, we call the algorithm quasi-hierarchical. We present a precise description of the algorithm and results of simulations run over various networks.


evolutionary algorithm survivability MPLS 


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  1. 1.
    Elbaum, R., Sidi, M.: Topological Design of Local Area Networks Using Genetic Algorithms. IEEE/ATM Transactions On Networking 5, 766–778 (1996)CrossRefGoogle Scholar
  2. 2.
    Ericsson, M., Rescende, M., Pardalos, P.: A Genetic Algorithm for the weight setting problem in OSPF Routing. J. of Combinatorial Optimization 6, 299–333 (2002)zbMATHCrossRefGoogle Scholar
  3. 3.
    Gavish, B., Huntler, S.: An Algorithm for Optimal Route Selection in SNA Networks. IEEE Trans. Commun. COM-31(10), 1154–1160 (1983)CrossRefGoogle Scholar
  4. 4.
    Grover, W.: Mesh-based Survivable Networks: Options and Strategies for Optical, MPLS, SONET and ATM Networking. Prentice Hall, New Jersey (2004)Google Scholar
  5. 5.
    Pióro, M., Medhi, D.: Routing, Flow, and Capacity Design in Communication and Computer Networks. Morgan Kaufman, San Francisco (2004)zbMATHGoogle Scholar
  6. 6.
    Riedl, A.: A Versatile Genetic Algorithm for Network Planning. In: Proceedings of EUNICE’98, pp. 97–103 (1998)Google Scholar
  7. 7.
    Riedl, A.: A Hybrid Genetic Algorithm for Routing Optimization in IP Networks Utilizing Bandwidth and Delay Metrics. In: Proceedings of IPOM (2002)Google Scholar
  8. 8.
    Przewoźniczek, M., Walkowiak, K.: Evolutionary Algorithm for Congestion Problem in Connection-Oriented Networks. In: Gervasi, O., Gavrilova, M., Kumar, V., Laganà, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3483, pp. 802–811. Springer, Heidelberg (2005)Google Scholar
  9. 9.
    Przewoźniczek, M., Walkowiak, K.: Quasi-hierarchical Evolution Algorithm for Flow Assignment in Survivable Connection-Oriented Networks. International Journal of Applied Mathematics and Computer Science 4, 101–116 (2006)Google Scholar
  10. 10.
    Sharma, V., Hellstrand, F(ed.): Framework for MPLS-based Recovery. RFC 3469 (2003)Google Scholar
  11. 11.
    Walkowiak, K.: A New Method of Primary Routes Selection for Local Restoration. In: Mitrou, N.M., Kontovasilis, K., Rouskas, G.N., Iliadis, I., Merakos, L. (eds.) NETWORKING 2004. LNCS, vol. 3042, pp. 1024–1035. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Michał Przewoźniczek
    • 1
  • Krzysztof Walkowiak
    • 2
  1. 1.Faculty of Computer Science and Management, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 WroclawPoland
  2. 2.Chair of Systems and Computer Networks, Faculty of Electronics, Wroclaw University of Technology, Wybrzeze Wyspianskiego 27, 50-370 WroclawPoland

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