Telecommunication Systems

, Volume 48, Issue 1–2, pp 93–107 | Cite as

Robust load balancing under traffic uncertainty—tractable models and efficient algorithms

Article

Abstract

Routing configurations that have been optimized for a nominal traffic scenario often display significant performance degradation when they are subjected to real network traffic. These degradations are due to the inherent sensitivity of classical optimization techniques to changes in model parameters combined with the significant traffic variations caused by demand fluctuations, component failures and network reconfigurations. In this paper, we review important sources for traffic variations in data networks and describe tractable models for capturing the associated traffic uncertainty. We demonstrate how robust routing settings with guaranteed performance for all foreseen traffic variations can be effectively computed via memory efficient iterative techniques and polynomial-time algorithms. The techniques are illustrated on real data from operational IP networks.

Keywords

Robust routing Optimization Traffic engineering Traffic uncertainty 

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References

  1. 1.
  2. 2.
    Ahuja, R. K., Magnati, T. L., & Orlin, J. B. (1993). Network flows. New York: Prentice Hall. Google Scholar
  3. 3.
    Applegate, D., & Cohen, E. (2003). Making intra-domain routing robust to changing and uncertain traffic demands: Understanding fundamental tradeoffs. In Proc. ACM SIGCOMM, Karlsruhe, Germany, August 2003 (pp. 313–324). Google Scholar
  4. 4.
    Ben-Ameur, W., & Kerivin, H. (2005). Routing of uncertain demands. Optimization and Engineering, 6(3), 283–313. CrossRefGoogle Scholar
  5. 5.
    Ben-Tal, A., & Nemirovski, A. (1999). Robust solutions of uncertain linear programs. Operations Research Letters, 25(1), 1–13. CrossRefGoogle Scholar
  6. 6.
    Bermolen, P., Vaton, S., & Juva, I. (2006). Search for optimality in traffic matrix estimation: a rational approach by Cramer-Rao lower bounds. In Proc. NGI, Valencia, Spain, April 2006. Google Scholar
  7. 7.
    Cao, J., Davis, D., Vander Wiel, S., & Yu, B. (2000). Time-varying network tomography: router link data. Journal of American Statistical Association, 95, 1063–1075. CrossRefGoogle Scholar
  8. 8.
    Chew, V. (1966). Confidence, prediction and tolerance regions for the multivariate normal distribution. Journal of the American Statistical Association, 61(315), 605–617. CrossRefGoogle Scholar
  9. 9.
    Elwalid, A., Jin, C., Low, S., & Widjaja, I. (2001). MATE: MPLS adaptive traffic engineering. In Proc. IEEE INFOCOM, Anchorage, Alaska, USA, May 2001 (pp. 1300–1309). Google Scholar
  10. 10.
    Feldmann, A., Greenberg, A., Lund, C., Reingold, N., Rexford, J., & True, F. (2000). Deriving traffic demands for operational IP networks: methodology and experience. In Proc. ACM SIGCOMM, Stockholm, Sweden, August 2000 (pp. 257–270). Google Scholar
  11. 11.
    Fortz, B., & Thorup, M. (2002). Optimizing OSPF/IS-IS weights in a changing world. IEEE Journal on Selected Areas in Communications, 20(4), 756–767. CrossRefGoogle Scholar
  12. 12.
    Gallager, R. (1977). A minimum delay routing algorithm using distributed computation. IEEE Transactions on Communications, COM-25(1), 73–85. CrossRefGoogle Scholar
  13. 13.
  14. 14.
    Gunnar, A., & Johansson, M. (2007). Robust routing under BGP reroutes. In Proc. IEEE GLOBECOM, Washington DC, USA, November 2007. Google Scholar
  15. 15.
    Gunnar, A., & Johansson, M. (2009). Robust routing under statistical uncertainty: models and polynomial time algorithms. In Proc. NGI 2009, Aveiro, Portugal, July 2009. Google Scholar
  16. 16.
    Gunnar, A., Johansson, M., & Telkamp, T. (2004). Traffic matrix estimation on a global IP backbone—a comparison on real data. In Proc. ACM Internet Measurement Conference, Taormina, Sicily, Italy, October 2004 (pp. 149–160) Google Scholar
  17. 17.
    Halabi, S., & McPherson, D. (2001). Internet routing architectures. Cisco Press. Google Scholar
  18. 18.
    Johansson, M., & Gunnar, A. (2006). Data-driven traffic engineering: techniques, experiences and challenges. In Broadnets, San Jose, California, USA, October 2006. Google Scholar
  19. 19.
    Juva, I., Vaton, S., & Virtamo, J. (2006). Quick traffic matrix estimation based on link count covariances. In Proc. ICC, Istanbul, Turkey, June 2006. Google Scholar
  20. 20.
    Juva, I., Susitaival, R., Peuhkuri, M., & Aalto, S. (2007). Effects of spatial aggregation on the characteristics of origin-destination pair traffic in funet. In Proc. NEW2AN, St. Petersburg, Russia, September 2007. Google Scholar
  21. 21.
    Kandula, S., Katabi, D., Davie, B., & Charny, A. (2005). Walking the tightrope: Responsive yet stable traffic engineering. In Proc. ACM SIGCOMM, Philadelphia, Pennsylvania, USA, August 2005 (pp. 253–264). Google Scholar
  22. 22.
    Katabi, D., Handley, M., & Rohrs, C. (2002). Internet congestion control for future high bandwidth-delay product environments. In Proc. of ACM SIGCOMM, Pittsburgh, Pennsylvania, USA, August 2002 (pp. 89–102). Google Scholar
  23. 23.
    Lobo, M. S., Vandenberghe, L., Boyd, S., & Lebert, H. (1998). Applications of second-order cone programming. Linear Algebra and Its Applications, 284, 193–228. CrossRefGoogle Scholar
  24. 24.
    Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., & Diot, C. (2002). Traffic matrix estimation: existing techniques and new directions. In Proc. ACM SIGCOMM, Pittsburgh, Pennsylvania, USA, August 2002 (pp. 161–174). Google Scholar
  25. 25.
    Papagiannaki, K., Taft, N., Zhang, Z., & Diot, C. (2003). Long-term forecasting of Internet backbone traffic: Observations and initial models. In Proc. IEEE INFOCOM, San Francisco, California, USA, April 2003 (pp. 1178–1188). Google Scholar
  26. 26.
    Pioro, M., & Medhi, D. (2004). Routing, flow and capacity design in communication and computer networks. San Mateo: Morgan Kaufmann. Google Scholar
  27. 27.
    Roughan, M., Thorup, M., & Zhang, Y. (2003). Traffic engineering with estimated traffic matrices. In Proc. ACM Internet measurement conference, Miami Beach, Florida, USA, October 2003 (pp. 248–258). Google Scholar
  28. 28.
    Soule, A., Lakhina, A., Taft, N., Papagiannaki, K., Salamatian, K., Nucci, A., Crovella, M., & Diot, C. (2005). Traffic matrices: balancing measurements, inference and modeling. In Proc. ACM SIGMETRICS, June 2005. Google Scholar
  29. 29.
    Sridharan, A., Guérin, R., Diot, C., & Bhattacharyya, S. (2004). The impact of traffic granuarity of robustness of traffic aware routing. Technical report, University of Pennsylvania. Google Scholar
  30. 30.
    Tebaldi, C., & West, M. (1998). Bayesian inference on network traffic using link count data. Journal of the American Statistical Association, 93(442), 557–576. CrossRefGoogle Scholar
  31. 31.
    Teixeira, R., Griffin, T., Voelker, G., & Shaikh, A. (2004). Network sensitivity to hot potato disruptions. In Proc. ACM SIGCOMM, Portland, Oregon , USA, August 2004 (pp. 231–244). Google Scholar
  32. 32.
    Teixeira, R., Shaikh, A., Griffin, T., & Rexford, J. (2004). Dynamics of hot-potato routing in IP networks. In Proc. ACM SIGMETRICS, New York, USA, June 2004 (pp. 307–319). Google Scholar
  33. 33.
    Teixeira, R., Duffield, N., Rexford, J., & Roughan, M. (2005). Traffic matrix reloaded: the impact of routing changes. In Proc. passive active measurements, Boston, Massachusetts, USA, April 2005. Google Scholar
  34. 34.
    Uhlig, S., Quoitin, B., Balon, S., & Lepropre, J. (2006). Providing public intradomain traffic matrices to the research community. ACM SIGCOMM Computer Communication Review, 36(1), 83–86. CrossRefGoogle Scholar
  35. 35.
    Vardi, Y. (1996). Network tomography: Estimating source-destination traffic intensities from link data. Journal of the American Statistical Association, 91(433), 365–377. CrossRefGoogle Scholar
  36. 36.
    Vutukury, S., & Garcia-Luna-Aceves, J. J. (1999). A simple approximation to minimum-delay routing. In Proc. ACM SIGCOMM, Cambridge, Massachusetts, USA, August 1999 (pp. 227–238). Google Scholar
  37. 37.
    Wang, H., Xie, H., Qiu, L., Yang, Y., Zhang, Y., & Greenberg, A. (2006). Cope: traffic engineering in dynamic networks. In Proc. ACM SIGCOMM, Pisa, Italy, 2006 (pp. 99–110). Google Scholar
  38. 38.
    Zhang, Y., Roughan, M., Duffield, N., & Greenberg, A. (2003). Fast accurate computation of large-scale IP traffic matrices from link loads. In Proc. ACM SIGMETRICS, San Diego, California, USA, June 2003 (pp. 206–217). Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Swedish Institute of Computer ScienceKistaSweden
  2. 2.School of Electrical EngineeringKTHStockholmSweden

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