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Traffic Matrix Estimation in Non-Stationary Environments

Traffic Matrix Estimation in Non-Stationary Environments. Presented by R. L. Cruz Department of Electrical & Computer Engineering University of California, San Diego Joint work with Antonio Nucci Nina Taft Christophe Diot NISS Affiliates Technology Day on Internet Tomography

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Traffic Matrix Estimation in Non-Stationary Environments

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  1. Traffic Matrix Estimation in Non-Stationary Environments Presented by R. L. Cruz Department of Electrical & Computer Engineering University of California, San Diego Joint work with Antonio Nucci Nina Taft Christophe Diot NISS Affiliates Technology Day on Internet Tomography March 28, 2003

  2. The Traffic Matrix Estimation Problem • Formulated in Y. Vardi, “Network Tomography: Estimating Source-Destination Traffic From Link Data,” JASA, March 1995, Vol. 91, No. 433, Theory & Methods

  3. The Traffic Matrix Estimation Problem Xj Yi ingress egress Xj PoP (Point of Presence) Y = A X “Traffic Matrix” Link Measurement Vector Routing Matrix

  4. The Traffic Matrix Estimation Problem • Importance of Problem: capacity planning, routing protocol configuration, load balancing policies, failover strategies, etc. • Difficulties in Practice • missing data • synchronization of measurements (SNMP) • Non-Stationarity (our focus here) • long convergence time needed to obtain estimates

  5. What is Non-Stationary? • Traffic Itself is Non-Stationary

  6. What is Non-Stationary? • Also, Routing is Non-Stationary • e.g. Due to Link Failures • Essence of Our Approach • Purposely reconfigure routing in order to help estimate traffic matrix • More information leads to more accurate estimates • Effectively increases rank of A • We have developed algorithms to reconfigure the routing for this purpose (beyond the scope of this talk)

  7. Outline of Remainder of Talk • Describe the “Stationary” Method • Stationary traffic, non-stationary routing • Stationary traffic assumption is reasonable if we always measure traffic at the same time of day (e.g. “peak period” of a work day) • Briefly Describe the “Non-Stationary” Method • Both non-stationary traffic and non-stationary routing • More complex but allows estimates to be obtained much faster

  8. Network and Measurement Model • Network with L links, N nodes, P=N(N-1) OD pair flows • K measurement intervals, 1 ≤ k ≤ K • Y(k) is the link count vector at time k: (L x 1) • A(k) is the routing matrix at time k: (L x P) • X(k) is the O-D pair traffic vector at time k: (P x 1) • X(k) = (x1(k) , x2(k) , … xP(k))T • Y(k) = A(k) X(k) Y(k) and A(k) can be truncated to reflect missing and redundant data

  9. Traffic Model: Stationary Case • X(k) is the O-D pair traffic vector at time k: (P x 1) X(k) = (x1(k) , x2(k) , … xP(k))T X(k) = X + W(k) • W(k) : “Traffic Fluctuation Vector • Zero mean, covariance matrix B • B = diag(X)

  10. Matrix Notation Linear system of equations: where: [LK] [LK][P] [LK][KP] [P] [KP] Choose Routing Configurations such that Rank(A) = P

  11. Traffic matrix Estimation-Stationary Case Y = AX + CW • Initial Estimate: Use Psuedo-Inverse of A:- does not require statistics of W (covariance B) • Gauss-Markov Theorem: Assume B is known • - Unbiased, minimum variance estimate- Coincides with Maximum Likelihood Estimate • if W is Gaussian

  12. Traffic matrix Estimation-Stationary Case Y = AX + CW • Minimum Estimation Error: (assumes B is known)

  13. Traffic matrix Estimation-Stationary Case • Recall we assume B = cov(W) satisfiesB = diag(X) • Set • Recursion for Estimates:

  14. Traffic matrix Estimation-Stationary Case • Our estimate is a solution to the equation: • Open questions for fixed point equation: • Existence of Solution?- Uniqueness? • Is solution an un-biased estimate?

  15. N=10 nodes, L=24 links and P=90 connections. Three set of OD pairs with mean x equal to: 500 Mbps, 2 Gbps and 4 Gbps. Gaussian Traffic Fluctuations: Numerical Example-Stationary case

  16. Stationary case: b=1 Samples/Snapshot=1

  17. Stationary case: b=1 Samples/Snapshot=1

  18. Stationary case: b=1 Samples/Snapshot=15

  19. Stationary case: b=1 Samples/Snapshot=15

  20. Stationary case: b=1.4 Samples/Snapshot=1

  21. Stationary case: b=1.4 Samples/Snapshot=1

  22. Stationary case: b=1.4 Samples/Snapshot=15

  23. Stationary case: b=1.4 Samples/Snapshot=15

  24. Stationary and Non-Stationary traffic • 20 snapshots / 4 samples per snapshot / 5 min per sample • Stationary Approach: 20 min per day (same time) / 20 days • Non-Stationary Approach: aggregate all the samples in • one window time large 400 min (7 hours)

  25. Traffic Model: Non-Stationary Case • Each OD pair is cyclo-stationary: • Each OD pair is modeled as: • Fourier series expansion:

  26. Mean estimation Results-Non Stationary case • Three set of OD pairs • where are linear independent Gaussian variables with:

  27. Non Stationary case: b=1 Link Count

  28. Non Stationary case: b=1 Mean estimation

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