Building Edge-Failure Resilient Networks

  • Chandra Chekuri
  • Anupam Gupta
  • Amit Kumar
  • Joseph (Seffi) Naor
  • Danny Raz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2337)


We consider the design of resilient networks that are fault tolerant against single link failures. Resilience against single link failures can be built into the network by providing backup paths, which are used when an edge failure occurs on a primary path. We consider several network design problems in this context: the goal is to provision primary and backup bandwidth while minimizing cost. Our models are motivated by current high speed optical networks and we provide approximation algorithms for the problems below.

The main problem we consider is that of backup allocation. In this problem, we are given an already provisioned (primary) network, and we want to reserve backup capacity on the edges of the underlying network so that all the demand can be routed even in the case of a single edge failure. We also consider a variant where the primary network has a tree topology and it is required that the restored network retains the tree topology. We then address the problem of simultaneous primary and backup allocation, in which we are given specifications of the traffic to be handled, and we want to both provision the primary as well as the backup network. We also investigate the online model where the primary network is not known in advance. Demands between source-sink pairs arrive online and the goal is to provide a primary path and set of backup edges that can be used for restoring a failure of any of the primary edges.


Network Design Problem Backup Path Primary Path Primary Network Cross Edge 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    A. Bremler-Barr, Y. Afek, E. Cohen, H. Kaplan and M. Merritt. Restoration by Path Concatenation: Fast Recovery of MPLS Paths. In Proceedings of the ACM PODC, pages 43–52, 2001.Google Scholar
  2. 2.
    A. Balakrishnan, T. Magnanti, and P. Mirchandani. Network Design. Annotated Bibliographies in Combinatorial Optimization, M. Dell’Amico, F. Maffioli, and S. Martello (eds.), John Wiley and Sons, New York, 311–334, 1997.Google Scholar
  3. 3.
    A. Balakrishnan, T. Magnanti, J. Sokol, and Y. Wang. Modeling and Solving the Single Facility Line Restoration Problem. Working Paper OR 327-98, Operations Research Center, MIT, 1998. To appear in Operations Research.Google Scholar
  4. 4.
    A. Balakrishnan, T. Magnanti, J. Sokol, and Y. Wang. Telecommunication Link Restoration Planning with Multiple Facility Types. To appear in Annals of Operations Research, volume “Topological Network Design in Telecommunications” edited by P. Kubat and J. M. Smith.Google Scholar
  5. 5.
    D. Bienstock and G. Muratore. Strong Inequalities for Capacitated Survivable Network Design Problems. Math. Programming, 89:127–147, 2001.MathSciNetCrossRefGoogle Scholar
  6. 6.
    G. Brightwell, G. Oriolo and F. B. Shepherd. Reserving resilient capacity in a network. In SIAM J. Disc. Math., 14(4), 524–539, 2001.MathSciNetzbMATHCrossRefGoogle Scholar
  7. 7.
    G. Brightwell, G. Oriolo and F. B. Shepherd. Reserving Resilient Capacity with Upper Bound Constraints. Manuscript.Google Scholar
  8. 8.
    G. Dahl and M. Stoer. A Cutting Plane Algorithm for Multicommodity Survivable Network Design Problems. INFORMS Journal on Computing, 10, 1–11, 1998.MathSciNetCrossRefGoogle Scholar
  9. 9.
    B. Davie and Y. Rekhter. MPLS: Technology and Applications. Morgan Kaufmann Publishers, 2000.Google Scholar
  10. 10.
    N. G. Duffield, P. Goyal, A. G. Greenberg, P. P. Mishra, K.K. Ramakrishnan, and J. E. van der Merwe. A flexible model for resource management in virtual private networks. In Proceedings of the ACM SIGCOMM, Computer Communication Review, volume 29, pages 95–108, 1999.CrossRefGoogle Scholar
  11. 11.
    L. Fleischer, A. Meyerson, I. Saniee, F. B. Shepherd and A. Srinivasan. Near-optimal design of MPλS tunnels with shared recovery. DIMACS Mini-Workshop on Quality of Service Issues in the Internet, 2001.Google Scholar
  12. 12.
    A. Fumagalli and L. Valcarenghi. IP restoration vs. WDM Protection: Is there an Optimal Choice? IEEE Network, 14(6):34–41, November/December 2000.CrossRefGoogle Scholar
  13. 13.
    M. Goemans and J. Kleinberg. An improved approximation ratio for the minimum latency problem. In Proceedings of 7th ACM-SIAM SODA, pages 152–157, 1996.Google Scholar
  14. 14.
    N. Garg, G. Konjevod, and R. Ravi. A polylogarithmic approximation algorithm for the group Steiner tree problem. Journal of Algorithms, 37(1):66–84, 2000. (Preliminary version in: 9th Annual ACM-SIAM Symposium on Discrete Algorithms, pages 253–259, 1998).MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    M. X. Goemans, A. V. Goldberg, S. Plotkin, D. B. Shmoys, É. Tardos, and D. P. Williamson. Improved approximation algorithms for network design problems. In Proceedings of the 5th Annual ACM-SIAM Symposium on Discrete Algorithms, pages 223–232, 1994.Google Scholar
  16. 16.
    A. Gupta, A. Kumar, J. Kleinberg, R. Rastogi, and B. Yener. Provisioning a Virtual Private Network: A network design problem for multicommodity flow. In Proceedings of the 33rd Annual ACM Symposium on Theory of Computing, pages 389–398, 2001.Google Scholar
  17. 17.
    G. F. Italiano, R. Rastogi and B. Yener. Restoration Algorithms for Virutal Private Networks in the Hose Model. In Proceedings of Infocom 2002, to appear.Google Scholar
  18. 18.
    K. Jain. A factor 2 approximation algorithm for the generalized Steiner network problem. Combinatorica, 21(1):39–60, 2001. (Preliminary version in: 39th Annual Symposium on Foundations of Computer Science, pages 448–457, 1998).MathSciNetzbMATHCrossRefGoogle Scholar
  19. 19.
    M. Kodialam and T.V. Lakshman. Minimum Interference Routing with Applications to MPLS Traffic Engineering. Infocom 2000, pages 884–893, 2000.Google Scholar
  20. 20.
    M. Kodialam and T.V. Lakshman. Dynamic Routing of Bandwidth Guaranteed Tunnels with Restoration. Infocom 2000, pages 902–911, 2000.Google Scholar
  21. 21.
    R. Motwani, S. Phillips and E. Torng. Non-clairvoyant scheduling. Theoretical Computer Science, 130:17–47, 1994. 22. J. W. Suurballe. Disjoint paths in a network. Networks, 4: 125-145, 1974.MathSciNetzbMATHCrossRefGoogle Scholar
  22. 23.
    D. P. Williamson, M. X. Goemans, M. Mihail, and V. V. Vazirani. A primal-dual approximation algorithm for generalized Steiner network problems. Combinatorica, 15(3):435–454, 1995. (Preliminary version in: 25th Annual ACM Symposium on Theory of Computing, pages 708–717, 1993).MathSciNetzbMATHCrossRefGoogle Scholar
  23. 24.
    D. Zhou and S. Subramaniam. Survivability in Optical Network. IEEE Network, 14(6):16–23, November/December 2000.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Chandra Chekuri
    • 1
  • Anupam Gupta
    • 1
  • Amit Kumar
    • 2
  • Joseph (Seffi) Naor
    • 3
  • Danny Raz
    • 3
  1. 1.Lucent Bell LabsMurray Hill
  2. 2.Computer Science Dept.Cornell UniversityIthaca
  3. 3.Computer Science DepartmentTechnion, Israel Institute of TechnologyHaifaIsrael

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