ISP Backbone Traffic Inference Methods to Support Traffic Engineering

1. ISP Backbone Traffic Inference Methods toSupport Traffic Engineering Olivier Goldschmidt Senior Network Consultant

2. Outline 1. Problem Description 2. Inputs to the Models 3. Constraints of the Models 4. Inference Methods: Pseudo-Inverse Method Linear Programming 5. Test Results 6. Conclusion and Open Issues

3. RATIONALE A major headache for Internet Service Providers is to estimate the end-to-end traffic volumes on their backbone network. Reliable traffic estimates between ingress and egress points are essential to traffic engineering purposes such as ATM PVC or LSP layout and sizing.

4. Problem Description An "easy" solution is to turn on NetFlow or IP-Accounting on all ingress and egress interfaces. But such solution is - Costly - Impractical

5. Problem Description Objective of traffic inference is to "guess" end to end aggregate traffic using limited information.

6. Inputs to the model Deterministic Information Measured Information Usage Information

7. DETERMINISTIC INFORMATION Network Topology Types of routers and links Routing paths between end points

8. MEASURED INFORMATION Baselining Information on network interfaces using SNMP Partial RMON/RMON2 information using selective probes (NetFlow or IP account.)

9. USAGE INFORMATION Data that can be correlated with the traffic on the network Allows to derive additional constraints on the network traffic.

10. WAN Link Ingress-Egress points Internal routers

11. 3 3 Assume that reading are symmetric. 3 3 Interface flow reading

12. 3 3

13. 1 2 1 2

14. CONSTRAINTS

15. 3 2 1 1 2 3 3 3

16. PSEUDO-INVERSE METHOD

17. LINEAR PROGRAMMING METHOD

18. OBJECTIVE FUNCTION COEFFICIENTS Choice of coefficients for the objective function will determine the precision of the end to end traffic estimates. Obvious choice is to set all coefficients to 1 and to maximize or to minimize the objective function But this choice is not neutral

19. 10 10 10 EXAMPLE Assume these are the true traffic demands Notice that all interface flows are equal to 20

20. 0 20 20 20 0 0 If all objective coefficients are equal to 1 If objective function is maximized If objective function is minimized

21. 10 2 10 10 1 1 But if coefficient are equal to the number of hops of demand route Is a solution whether objective function is maximized or minimized

22. Another advantage of the LP method Allows to add constraints that represent usage information. For instance constraint the very unlikely end-to-end traffic to be close to zero. Also known traffic from NetFlow or IP accounting readings can be included as constraints in the linear program.

23. Test Results NETWORK • 60 Routers • 114 WAN Links • 529 Traffic demands • Bandwidth from 0 to 256 Kbps

24. Test Results 1. Route the demands 2. Compute the resulting interface flows 3. Apply the Linear Programming method to estimate the end-to-end traffic demands 4. Compare those estimates with the original traffic demands in % of absolute difference |estimate-true value|/true value The following charts show % of demands with given relative error

25. Objective coefficients = number of hops

26. Objective coefficients = number of hops

27. All objective coefficient = 1

28. Netflow turned on on five random routers

29. Netflow turned on on five most used routers

30. Netflow turned on on ten random routers

31. Comparison of different results

32. Conclusions Objective coefficients in LP need to be scaled Turning NetFlow on a few selected interfaces can greatly improve the traffic estimates.