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A Genetic Algorithm for the Weight Setting Problem in OSPF Routing

Author

Listed:
  • M. Ericsson

    (Royal Institute of Technology (KTH))

  • M.G.C. Resende

    (Information Sciences Research)

  • P.M. Pardalos

    (University of Florida)

Abstract

With the growth of the Internet, Internet Service Providers (ISPs) try to meet the increasing traffic demand with new technology and improved utilization of existing resources. Routing of data packets can affect network utilization. Packets are sent along network paths from source to destination following a protocol. Open Shortest Path First (OSPF) is the most commonly used intra-domain Internet routing protocol (IRP). Traffic flow is routed along shortest paths, splitting flow at nodes with several outgoing links on a shortest path to the destination IP address. Link weights are assigned by the network operator. A path length is the sum of the weights of the links in the path. The OSPF weight setting (OSPFWS) problem seeks a set of weights that optimizes network performance. We study the problem of optimizing OSPF weights, given a set of projected demands, with the objective of minimizing network congestion. The weight assignment problem is NP-hard. We present a genetic algorithm (GA) to solve the OSPFWS problem. We compare our results with the best known and commonly used heuristics for OSPF weight setting, as well as with a lower bound of the optimal multi-commodity flow routing, which is a linear programming relaxation of the OSPFWS problem. Computational experiments are made on the AT&T Worldnet backbone with projected demands, and on twelve instances of synthetic networks.

Suggested Citation

  • M. Ericsson & M.G.C. Resende & P.M. Pardalos, 2002. "A Genetic Algorithm for the Weight Setting Problem in OSPF Routing," Journal of Combinatorial Optimization, Springer, vol. 6(3), pages 299-333, September.
    • Handle: RePEc:spr:jcomop:v:6:y:2002:i:3:d:10.1023_a:1014852026591
    DOI: 10.1023/A:1014852026591
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    References listed on IDEAS

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    1. James C. Bean, 1994. "Genetic Algorithms and Random Keys for Sequencing and Optimization," INFORMS Journal on Computing, INFORMS, vol. 6(2), pages 154-160, May.
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    1. Ayşegül Altın & Bernard Fortz & Mikkel Thorup & Hakan Ümit, 2013. "Intra-domain traffic engineering with shortest path routing protocols," Annals of Operations Research, Springer, vol. 204(1), pages 65-95, April.
    2. Julliany S. Brandão & Thiago F. Noronha & Celso C. Ribeiro, 2016. "A biased random-key genetic algorithm to maximize the number of accepted lightpaths in WDM optical networks," Journal of Global Optimization, Springer, vol. 65(4), pages 813-835, August.
    3. José Fernando Gonçalves & Mauricio G. C. Resende, 2011. "A parallel multi-population genetic algorithm for a constrained two-dimensional orthogonal packing problem," Journal of Combinatorial Optimization, Springer, vol. 22(2), pages 180-201, August.
    4. Xi Zhang & Zhili Zhou & Dong Cheng, 2017. "Efficient path routing strategy for flows with multiple priorities on scale-free networks," PLOS ONE, Public Library of Science, vol. 12(2), pages 1-16, February.
    5. Silva, Allyson & Aloise, Daniel & Coelho, Leandro C. & Rocha, Caroline, 2021. "Heuristics for the dynamic facility location problem with modular capacities," European Journal of Operational Research, Elsevier, vol. 290(2), pages 435-452.
    6. Mauricio Resende, 2012. "Biased random-key genetic algorithms with applications in telecommunications," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 130-153, April.
    7. Changyong Zhang, 2017. "An origin-based model for unique shortest path routing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 68(8), pages 935-951, August.
    8. Line Y. Becerra Sánchez & Jhon J. Padilla Aguilar, 2019. "An approach to support traffic engineering in IPv6 networks based on IPv6 facilities," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(1), pages 11-27, September.
    9. Peter Broström & Kaj Holmberg, 2006. "Multiobjective design of survivable IP networks," Annals of Operations Research, Springer, vol. 147(1), pages 235-253, October.
    10. Gonçalves, José Fernando & Resende, Mauricio G.C., 2015. "A biased random-key genetic algorithm for the unequal area facility layout problem," European Journal of Operational Research, Elsevier, vol. 246(1), pages 86-107.
    11. Andrade, Carlos E. & Toso, Rodrigo F. & Gonçalves, José F. & Resende, Mauricio G.C., 2021. "The Multi-Parent Biased Random-Key Genetic Algorithm with Implicit Path-Relinking and its real-world applications," European Journal of Operational Research, Elsevier, vol. 289(1), pages 17-30.
    12. L. A. C. Roque & D. B. M. M. Fontes & F. A. C. C. Fontes, 2014. "A hybrid biased random key genetic algorithm approach for the unit commitment problem," Journal of Combinatorial Optimization, Springer, vol. 28(1), pages 140-166, July.
    13. Carlos A. S. Oliveira, 2005. "An Algorithm for the Maximum Likelihood Problem on Evolutionary Trees," Journal of Combinatorial Optimization, Springer, vol. 10(1), pages 61-75, August.
    14. Gonçalves, J.F. & Mendes, J.J.M. & Resende, M.G.C., 2008. "A genetic algorithm for the resource constrained multi-project scheduling problem," European Journal of Operational Research, Elsevier, vol. 189(3), pages 1171-1190, September.
    15. Ricardo Silva & Mauricio Resende & Panos Pardalos, 2014. "Finding multiple roots of a box-constrained system of nonlinear equations with a biased random-key genetic algorithm," Journal of Global Optimization, Springer, vol. 60(2), pages 289-306, October.
    16. R. M. A. Silva & M. G. C. Resende & P. M. Pardalos, 2015. "A Python/C++ library for bound-constrained global optimization using a biased random-key genetic algorithm," Journal of Combinatorial Optimization, Springer, vol. 30(3), pages 710-728, October.

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