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Solving Segment Routing Problems with Hybrid Constraint Programming Techniques

  • Renaud Hartert
  • Pierre Schaus
  • Stefano Vissicchio
  • Olivier Bonaventure
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9255)

Abstract

Segment routing is an emerging network technology that exploits the existence of several paths between a source and a destination to spread the traffic in a simple and elegant way. The major commercial network vendors already support segment routing, and several Internet actors are ready to use segment routing in their network. Unfortunately, by changing the way paths are computed, segment routing poses new optimization problems which cannot be addressed with previous research contributions. In this paper, we propose a new hybrid constraint programming framework to solve traffic engineering problems in segment routing. We introduce a new representation of path variables which can be seen as a lightweight relaxation of usual representations. We show how to define and implement fast propagators on these new variables while reducing the memory impact of classical traffic engineering models. The efficiency of our approach is confirmed by experiments on real and artificial networks of big Internet actors.

Keywords

Traffic engineering Segment routing Constraint programming Large neighborhood search 

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References

  1. 1.
    Awduche, D., Chiu, A., Elwalid, A., Widjaja, I., Xiao, X.: Overview and principles of internet traffic engineering-rfc3272. IETF (2002)Google Scholar
  2. 2.
    Barnhart, C., Hane, C.A., Vance, P.H.: Using branch-and-price-and-cut to solve origin-destination integer multicommodity flow problems. Operations Research 48(2), 318–326 (2000)CrossRefGoogle Scholar
  3. 3.
    Beasley, J.E., Christofides, N.: An algorithm for the resource constrained shortest path problem. Networks 19(4), 379–394 (1989)zbMATHMathSciNetCrossRefGoogle Scholar
  4. 4.
    Bent, R., Hentenryck, P.V.: A two-stage hybrid algorithm for pickup and delivery vehicle routing problems with time windows. Computers & Operations Research 33(4), 875–893 (2006)zbMATHCrossRefGoogle Scholar
  5. 5.
    Briggs, P., Torczon, L.: An efficient representation for sparse sets. ACM Letters on Programming Languages and Systems (LOPLAS) 2(1–4), 59–69 (1993)CrossRefGoogle Scholar
  6. 6.
    Uslar, M., Specht, M., Rohjans, S., Trefke, J., Gonzalez, J.M.V.: Introduction. In: Uslar, M., Specht, M., Rohjans, S., Trefke, J., Vasquez Gonzalez, J.M. (eds.) The Common Information Model CIM. POWSYS, vol. 2, pp. 3–48. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  7. 7.
    Davie, B., Rekhter, Y.: MPLS: technology and applications. Morgan Kaufmann Publishers Inc. (2000)Google Scholar
  8. 8.
    le Clément de Saint-Marcq, V., Schaus, P., Solnon, C., Lecoutre, C.: Sparse-sets for domain implementation. In: CP workshop on Techniques for Implementing Constraint programming Systems (TRICS), pp. 1–10 (2013)Google Scholar
  9. 9.
    Desrochers, M., Soumis, F.: A generalized permanent labeling algorithm for the shortest path problem with time windows. INFOR Information Systems and Operational Research (1988)Google Scholar
  10. 10.
    Dooms, G., Deville, Y., Dupont, P.E.: CP(Graph): Introducing a Graph Computation Domain in Constraint Programming. In: van Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 211–225. Springer, Heidelberg (2005) CrossRefGoogle Scholar
  11. 11.
    Figueiredo, G.B., da Fonseca, N.L.S., Monteiro, J.A.S.: A minimum interference routing algorithm. In: ICC, pp. 1942–1947 (2004)Google Scholar
  12. 12.
    Filsfils, C., et al.: Segment Routing Architecture. Internet draft, draft-filsfils-spring-segment-routing-00, work in progress (2014)Google Scholar
  13. 13.
    Filsfils, C., et al.: Segment Routing Use Cases. Internet draft, draft-filsfils-spring-segment-routing-use-cases-00, work in progress (2014)Google Scholar
  14. 14.
    Filsfils, C., et al.: Segment Routing with MPLS data plane. Internet draft, draft-filsfils-spring-segment-routing-mpls-01, work in progress (2014)Google Scholar
  15. 15.
    Fortz, B., Thorup, M.: Internet traffic engineering by optimizing OSPF weights. In: Proc. INFOCOM (March 2000)Google Scholar
  16. 16.
    Frei, C., Faltings, B.V.: Resource Allocation in Networks Using Abstraction and Constraint Satisfaction Techniques. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 204–218. Springer, Heidelberg (1999) CrossRefGoogle Scholar
  17. 17.
    Gervet, C.: Conjunto: Constraint logic programming with finite set domains. ILPS 94, 339–358 (1994)Google Scholar
  18. 18.
    Glover, F.: Tabu search: A tutorial. Interfaces 20(4), 74–94 (1990)CrossRefGoogle Scholar
  19. 19.
    Inc., Gurobi Optimization. Gurobi optimizer reference manual (2015)Google Scholar
  20. 20.
    Kodialam, M., Lakshman, T.V.: Minimum interference routing with applications to mpls traffic engineering. In: Proceedings of the Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies, INFOCOM 2000, vol. 2, pp. 884–893. IEEE (2000)Google Scholar
  21. 21.
    Mairy, J.B., Deville, Y., Van Hentenryck, P.: Reinforced adaptive large neighborhood search. In: The Seventeenth International Conference on Principles and Practice of Constraint Programming (CP 2011), p. 55 (2011)Google Scholar
  22. 22.
    Nucci, A., Papagiannaki, K.: Design, Measurement and Management of Large-Scale IP Networks - Bridging the Gap between Theory and Practice. Cambridge University Press (2008)Google Scholar
  23. 23.
    RFC3031 ŒTF. Multiprotocol label switching architecture (2001)Google Scholar
  24. 24.
    OscaR Team. OscaR: Scala in OR (2012). https://bitbucket.org/oscarlib/oscar
  25. 25.
    Ouaja, W., Richards, B.: A hybrid multicommodity routing algorithm for traffic engineering. Networks 43(3), 125–140 (2004)zbMATHMathSciNetCrossRefGoogle Scholar
  26. 26.
    Pacino, D., Van Hentenryck, P.: Large neighborhood search and adaptive randomized decompositions for flexible jobshop scheduling. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence, vol. 3, pp. 1997–2002. AAAI Press (2011)Google Scholar
  27. 27.
    Shaw, P., Furnon, V.: Propagation Guided Large Neighborhood Search. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 468–481. Springer, Heidelberg (2004) CrossRefGoogle Scholar
  28. 28.
    Previdi, S., et al.: IPv6 Segment Routing Header (SRH). Internet draft, draft-previdi-6man-segment-routing-header-00, work in progress (2014)Google Scholar
  29. 29.
    Quinn, P., Nadeau, T.: Service function chaining problem statement. draft-ietf-sfc-problem-statement-07 (work in progress) (2014)Google Scholar
  30. 30.
    Quoitin, B., Van den Schrieck, V., Francois, P., Bonaventure, O.: IGen: Generation of router-level Internet topologies through network design heuristics. In: ITC (2009)Google Scholar
  31. 31.
    Deering, S.: RFC2460 and R Hinden. Internet protocolGoogle Scholar
  32. 32.
    Ropke, S., Pisinger, D.: An adaptive large neighborhood search heuristic for the pickup and delivery problem with time windows. Transportation Science 40(4), 455–472 (2006)CrossRefGoogle Scholar
  33. 33.
    Giralt, L.R., Creemers, T., Tourouta, E., Colomer, J.R., et al.: A global constraint model for integrated routeing and scheduling on a transmission network (2001)Google Scholar
  34. 34.
    Rossi, F., Van Beek, P., Walsh, T.: Handbook of constraint programming. Elsevier Science (2006)Google Scholar
  35. 35.
    Roughan, M.: Simplifying the synthesis of internet traffic matrices. SIGCOMM Comput. Commun. Rev. 35(5), 93–96 (2005)CrossRefGoogle Scholar
  36. 36.
    Schaus, P.: Variable objective large neighborhood search (Submitted to CP13, 2013)Google Scholar
  37. 37.
    Schaus, P., Hartert, R.: Multi-Objective Large Neighborhood Search. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 611–627. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  38. 38.
    Shaw, P.: Using Constraint Programming and Local Search Methods to Solve Vehicle Routing Problems. In: Maher, M.J., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 417–431. Springer, Heidelberg (1998) CrossRefGoogle Scholar
  39. 39.
    Simonis, H.: Constraint applications in networks. Handbook of Constraint Programming 2, 875–903 (2006)CrossRefGoogle Scholar
  40. 40.
    Spring, N., Mahajan, R., Wetherall, D., Anderson, T.: Measuring isp topologies with rocketfuel. IEEE/ACM Trans. Netw., 12(1) (2004)Google Scholar
  41. 41.
    Van Hentenryck, P., Deville, Y., Teng, C.-M.: A generic arc-consistency algorithm and its specializations. Artificial Intelligence 57(2), 291–321 (1992)zbMATHMathSciNetCrossRefGoogle Scholar
  42. 42.
    Xia, Q., Simonis, H.: Primary/Secondary Path Generation Problem: Reformulation, Solutions and Comparisons. In: Lorenz, P., Dini, P. (eds.) ICN 2005. LNCS, vol. 3420, pp. 611–619. Springer, Heidelberg (2005) CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Renaud Hartert
    • 1
  • Pierre Schaus
    • 1
  • Stefano Vissicchio
    • 1
  • Olivier Bonaventure
    • 1
  1. 1.UCLouvain, ICTEAMLouvain-la-NeuveBelgium

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