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Network Planning for Stochastic Traffic Demands

  • Phuong Nga Tran
  • Bharata Dwi Cahyanto
  • Andreas Timm-Giel
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 125)

Abstract

Traffic in communication networks is not constant but fluctuates heavily, which makes the network planning task very challenging. Overestimating the traffic volume results in an expensive solution, while underestimating it leads to a poor Quality of Service (QoS) in the network.

In this paper, we propose a new approach to address the network planning problem under stochastic traffic demands. We first formulate the problem as a chance-constrained programming problem, in which the capacity constraints need to be satisfied in probabilistic sense. Since we do not assume a normal distribution for the traffic demands, the problem does not have deterministic equivalent and hence cannot be solved by the well-known techniques. A heuristic approach based on genetic algorithm is therefore proposed. The experiment results show that the proposed approach can significantly reduce the network costs compared to the peak-load-based approach, while still maintaining the robustness of the solution. This approach can be applied to different network types with different QoS requirements.

Keywords

Network planning stochastic traffic demands chance constrained programming genetic algorithm 

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References

  1. 1.
    Bienstock, D., Chopra, S., Gnlk, O., Tsai, C.Y.: Minimum cost capacity installation for multicommodity network flows. Mathematical Programming 81, 177–199 (1998)MathSciNetzbMATHGoogle Scholar
  2. 2.
  3. 3.
    Geant project, http://www.geant.net
  4. 4.
    Dantzig, G.B.: Linear programming under uncertainty. Management Science, 197–206 (1955)Google Scholar
  5. 5.
    Soyster, A.L.: Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research 21, 1154–1157 (1973)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Pavon-Marino, P., Garcia-Manrubia, B., Aparicio-Pardo, R.: Multihour network planning based on domination between sets of traffic matrices. Computer Networks 55(3), 665–675 (2011)CrossRefzbMATHGoogle Scholar
  7. 7.
    Aparicio-Pardo, R., Skorin-Kapov, N., Pavon-Marino, P., GarciaManrubia, B.: (Non)-reconfigurable virtual topology design under multihour traffic in optical networks. IEEE/ACM Transactions on Networking 20(5), 1567–1580 (2012)CrossRefGoogle Scholar
  8. 8.
    Duffield, N.G., Goyal, P., Greenberg, A.G., Mishra, P.P., Ramakrishnan, K.K., van der Merive, J.E.: A flexible model for resource management in virtual private networks. In: ACM SIGCOMM, Cambridge, USA, pp. 95–108 (September 1999)Google Scholar
  9. 9.
    Chekuri, C., Oriolo, G., Scutella, M.G., Shepherd, F.B.: Hardness of robust network design. Networks 50(1), 50–54 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Altin, A., Yaman, H., Pinar, M.C.: The robust network loading problem under polyhedral demand uncertainty: Formulation, polyhedral analysis, and computations. INFORMS Journal on Computing (2010)Google Scholar
  11. 11.
    Mattia, S.: The robust network loading problem with dynamic routing. Universita di Roma la Sapienza, Tech. Rep (2010)Google Scholar
  12. 12.
    Bertsimas, D., Sim, M.: The price of robustness. Operations Research, pp. 35–53 (2004)Google Scholar
  13. 13.
    Koster, A., Kutschka, M., Raack, C.: On the robustness of optimal network design. In: IEEE International Conference on Communications (ICC 2011), Kyoto, Japan (June 2011)Google Scholar
  14. 14.
    Poss, M., Raack, C.: Affine recourse for the robust network design problem: between static and dynamic routing. Zuse Institute Berlin, Tech. Rep (2011)Google Scholar
  15. 15.
    Belotti, P., Capone, A., Carello, G., Malucelli, F.: Multi-layer mpls network design: The impact of statistical multiplexing. Computer Networks 52(6), 1291–1307 (2008)CrossRefzbMATHGoogle Scholar
  16. 16.
    Altin, A., Amaldi, E., Belotti, P., Pinar, M.C.: Provisioning virtual private networks under traffic uncertainty. Networks 49(1), 100–155 (2007)MathSciNetCrossRefzbMATHGoogle Scholar
  17. 17.
    Charnes, A., Cooper, W.: Chance-constrained programming. Management Science, 73–79 (1959)Google Scholar
  18. 18.
    Dfn network, http://sndlib.zib.de

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2013

Authors and Affiliations

  • Phuong Nga Tran
    • 1
  • Bharata Dwi Cahyanto
    • 1
  • Andreas Timm-Giel
    • 1
  1. 1.Institute of Communication NetworksHamburg-Harburg University of TechnologyHamburgGermany

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