An Assignment Model on Traffic Matrix Estimation
It is important to acquire accurate knowledge of traffic matrices of networks for many traffic engineering or network management tasks. Direct measurement of the traffic matrices is difficult in large scale operational IP networks. One approach is to estimate the traffic matrices statistically from easily measured data. The performance of the statistical methods is limited due to they rely on the limited information and require large amount of computation, which limits the convergence of such computation. In this paper, we present an alternative approach to traffic matrix estimation. This method uses assignment model. The model is based on the link characters and includes a fast algorithm. The algorithm combines statistical and optimized tomography. The algorithm is evaluated by simulation and the simulation results show that our algorithm is robust, fast, flexible, and scalable.
KeywordsAssignment Model Traffic Demand Traffic Engineering Traffic Matrix Link Load
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- 1.Tebaldi, C., West, M.: Bayesian Inference of Network Traffic Using Link Count Data. J. of the American Statistical Association, 557–573 (June 1998)Google Scholar
- 2.Feldmann, A., Greenberg, A., Lund, C., Reingold, N., Rexford, J., True, F.: Deriving Traffic Demand for Operational IP Networks: Methodology and Experience. In: Proceedings of ACM SIGSOMM 2000, Computer Communication Review, vol. 30(4) (2000)Google Scholar
- 3.Vardi, Y.: Network Tomography: Estimating Source-Destination Traffic Intensities from Link Data. J. of the American Statistical Association, 365–377 (1996)Google Scholar
- 5.Goldschmidt, O.: ISP Backbone Traffic Inference Methods to Support Traffic Engineering. In: Internet Statistics and Metrics Analysis (ISMA) Workshop, San Diego, CA (December 2000)Google Scholar
- 6.Medina, A., Taft, N., Salamatian, K., Bhattacharyya, S., Diot, C.: Traffic Matrix Estimation: Existing Techniques Compared and New Directions. In: ACM SIGCOMM, Pitsburgh, PA (2002)Google Scholar
- 7.Zhang, Y., Roughan, M., Duffield, N., Greenberg, A.: Fast Accurate Computation of Large-Scale IP Traffic Matrices from Link Loads. ACM SIGMETRICS (2003)Google Scholar
- 8.Zhang, Y., Roughan, M., Lund, C., Donoho, D.: An Information-Theoretic Approach to Traffic Matrix Estimation. ACM SIGCOMM (August 2003)Google Scholar
- 9.Abrahamsson, T.: Estimation of Origin-Destination Matrices using Traffic Counts-A. Literature Survey. Technical Report IR-98021, International Institute for Applied Systems. Analysis (1998)Google Scholar
- 10.Medina A., Salamatian K., Taft N., Matta I., Diot C.: A Two-step Statistical Approach for Inferring Network - Traffic Demands Medina (2004), www.cs.bu.edu/techreports/ps/2004-011-two-step-tm-inference.ps
- 11.Nucci, A., Cruz, R., Taft, N., Diot, C.: Design of IGP link weight changes for estimation of traffic matrices. In: Proc. IEEE INFOCOM, Hong Kong (March 2004)Google Scholar
- 12.Bell, M.G.H., Lan, W.H.K., Ploss, G., Inaudi, D.: Stochastic User Equilibrium Assignment and Iterative Balancing. In: Aganzo, C.F.D. (ed.) Transportation and Traffic Theory, pp. 427–440. Elsevier Science Publishers, Amsterdam (1993)Google Scholar