Paul Tune
Matthew Roughan
ABSTRACT
Traffic matrices are used in many network engineering tasks, for instance optimal network design. Unfortunately, measurements of these matrices are error-prone, a problem that is exacerbated when they are extrapolated to provide the predictions used in planning. Practical network design and management should consider sensitivity to such errors, but although robust optimisation techniques exist, it seems they are rarely used, at least in part because of the difficulty in generating an ensemble of admissible traffic matrices with a controllable error level. We address this problem in our paper by presenting a fast and flexible technique of generating synthetic traffic matrices. We demonstrate the utility of the method by presenting a methodology for robust network design based on adaptation of the mean-risk analysis concept from finance.
- D. Applegate and E. Cohen. Making intra-domain routing robust to changing and uncertain traffic demands: Understanding fundamental tradeoffs. In ACM SIGCOMM, pages 313--324, August 2003. Google Scholar
Digital Library
- G. E. P. Box and N. R. Draper. Empirical Model-Building and Response Surfaces. Wiley, 1987. Google ScholarDigital Library
- R. S. Cahn. Wide Area Network Design. Morgan Kaufman, 1998.Google Scholar
- W. E. Deming and F. F. Stephan. On a least squares adjustment of a sampled frequency table when the expected marginal totals are known. Ann. Math. Stat, 11(4):427--444, 1940.Google ScholarCross Ref
- B. Eriksson, R. Durairajan, and P. Barford. RiskRoute: A framework for mitigating network outage threats. In CoNEXT '13, pages 405--416, December 2013. Google ScholarDigital Library
- V. Erramilli, M. Crovella, and N. Taft. An independent-connection model for traffic matrices. In ACM IMC 2006, pages 251--256, October 2006. Google ScholarDigital Library
- A. Feldmann, A. Greenberg, C. Lund, N. Reingold, J. Rexford, and F. True. Deriving demands for operational IP networks: Methodology and experience. IEEE/ACM Trans. Networking, 9:265--280, June 2001. Google ScholarDigital Library
- S. E. Fienberg. An iterative procedure for estimation in contingency tables. Ann. Math. Stat., 41(3):907--917, 1970.Google ScholarCross Ref
- B. Fortz and M. Thorup. Optimizing OSPF/IS--IS weights in a changing world. IEEE J. Selected Areas in Communications, 20(4):756--767, May 2002. Google ScholarDigital Library
- D. D. Lee and H. S. Seung. Algorithms for non-negative matrix factorization. In NIPS, pages 556--562, 2000.Google ScholarDigital Library
- G. Liang, B. Yu, and N. Taft. A fast lightweight approach to origin-destination IP traffic estimation using partial measurements. IEEE/ACM Trans. Networking, 14:2634--2648, June 2006.Google ScholarCross Ref
- H. Markowitz. Portfolio selection. The Journal of Finance, 7(1):77--91, March 1952.Google Scholar
- A. Medina, N. Taft, K. Salmatian, S. Bhattacharyya, and C. Diot. Traffic matrix estimation: Existing techniques and new directions. In ACM SIGCOMM, 2002. Google ScholarDigital Library
- D. Mitra and Q. Wang. Stochastic traffic engineering for demand uncertainty and risk-aware network revenue management. IEEE/ACM Trans. Networking, 13(2):221--233, April 2005. Google ScholarDigital Library
- NLANR. Abilene Trace Data. http://pma.nlanr.net/Special/ipls3.html.Google Scholar
- I. Norros. On the use of fractional Brownian motion in the theory of connectionless networks. IEEE Journal on Selected Areas in Communications, 13(6):953--961, 1995. Google ScholarDigital Library
- A. Nucci, A. Sridharan, and N. Taft. The problem of synthetically generating IP traffic matrices: Initial recommendations. SIGCOMM Comput. Commun. Rev., 35:19--32, July 2005. Google ScholarDigital Library
- M. Roughan. Simplifying the synthesis of Internet traffic matrices. SIGCOMM Comput. Commun. Rev., 35(5):93--96, 2005. Google ScholarDigital Library
- M. Roughan. Robust network planning. In C. R. Kalmanek, S. Misra, and R. Yang, editors, The Guide to Reliable Internet Services and Applications, chapter 5, pages 137--177. Springer, 2010.Google Scholar
- M. Roughan and J. Gottlieb. Large-scale measurement and modeling of backbone Internet traffic. In SPIE ITCom, Boston, 2002.Google Scholar
- M. Roughan, A. Greenberg, C. Kalmanek, M. Rumsewicz, J. Yates, and Y. Zhang. Experience in measuring backbone traffic variability: Models, metrics, measurements and meaning. In ACM IMW, 2002. Google ScholarDigital Library
- M. Roughan, M. Thorup, and Y. Zhang. Traffic engineering with estimated traffic matrices. In ACM IMC 2003, pages 248--258, October 2003. Google ScholarDigital Library
- R. Sinkhorn and P. Knopp. Concerning nonnegative matrices and doubly stochastic matrices. Pacific Journal of Mathematics, 21(2), 1967.Google ScholarCross Ref
- A. Soule, A. Nucci, R. Cruz, E. Leonardi, and N. Taft. How to identify and estimate the largest traffic matrix elements in a dynamic environment. SIGMETRICS Perform. Eval. Rev., 32(1):73--84, June 2004. Google ScholarDigital Library
- R. Teixera, N. Duffield, J. Rexford, and M. Roughan. Traffic matrix reloaded: Impact of routing changes. In Workshop on Passive and Active Measurements, 2005. Google ScholarDigital Library
- P. Tune and M. Roughan. Internet traffic matrices: A primer. In H. Haddadi and O. Bonaventure, editors, Recent Advances in Networking, Vol. 1. ACM SIGCOMM, August 2013.Google Scholar
- P. Tune and M. Roughan. Network-design sensitivity analysis. Technical report, School of Mathematical Sciences, University of Adelaide, January 2014.Google Scholar
- Y. Vardi. Network Tomography: Estimating source-destination traffic intensities from link data. J. Am. Statist. Assoc., 91:365--377, 1996.Google ScholarCross Ref
- Y. Zhang, M. Roughan, N. Duffield, and A. Greenberg. Fast accurate computation of large-scale IP traffic matrices from link loads. In ACM SIGMETRICS 2003, pages 206--217, 2003. Google ScholarDigital Library
- Y. Zhang, M. Roughan, C. Lund, and D. Donoho. Estimating Point-to-Point and Point-to-Multipoint traffic matrices: An information-theoretic approach. IEEE/ACM Trans. Networking, 13(5):947--960, October 2005. Google ScholarDigital Library
- R. Zhang-Shen and M. McKeown. Designing a predictable Internet backbone with Valiant load-balancing. In Proceedings of the 13th International Conference on Quality of Service (IWQoS), pages 178--192, 2005. Google ScholarDigital Library
Index Terms
- Network-design sensitivity analysis
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Network-design sensitivity analysis
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