1. 1 ISMA 2000 1 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY ISP Backbone Traffic Inference Methods to Support Traffic Engineering Olivier Goldschmidt Senior Network Consultant

2. 2 ISMA 2000 2 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY 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. 3 ISMA 2000 3 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY 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. 4 ISMA 2000 4 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY 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. 5 ISMA 2000 5 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Problem Description Objective of traffic inference is to "guess" end to end aggregate traffic using limited information.

6. 6 ISMA 2000 6 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Inputs to the model Deterministic Information Measured Information Usage Information

7. 7 ISMA 2000 7 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY DETERMINISTIC INFORMATION Network Topology Types of routers and links Routing paths between end points

8. 8 ISMA 2000 8 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY MEASURED INFORMATION Baselining Information on network interfaces using SNMP Partial RMON/RMON2 information using selective probes (NetFlow or IP account.)

9. 9 ISMA 2000 9 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY USAGE INFORMATION Data that can be correlated with the traffic on the network Allows to derive additional constraints on the network traffic.

10. 10 ISMA 2000 10 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Ingress-Egress points Internal routers WAN Link

11. 11 ISMA 2000 11 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY 3 3 3 3 Assume that reading are symmetric. Interface flow reading

12. 12 ISMA 2000 12 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY 3 3

13. 13 ISMA 2000 13 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY 2 2 1 1

14. 14 ISMA 2000 14 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY CONSTRAINTS

15. 15 ISMA 2000 15 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY 3 2 2 1 1 3 3 3

16. 16 ISMA 2000 16 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY PSEUDO-INVERSE METHOD

17. 17 ISMA 2000 17 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY LINEAR PROGRAMMING METHOD

18. 18 ISMA 2000 18 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY 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. 19 ISMA 2000 19 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY EXAMPLE Assume these are the true traffic demands 10 Notice that all interface flows are equal to 20

20. 20 ISMA 2000 20 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY If all objective coefficients are equal to 1 If objective function is maximized 0 20 If objective function is minimized 20 0 0

21. 21 ISMA 2000 21 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY But if coefficient are equal to the number of hops of demand route Is a solution whether objective function is maximized or minimized 10 2 1 1

22. 22 ISMA 2000 22 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY 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. 23 ISMA 2000 23 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Test Results 60 Routers 114 WAN Links 529 Traffic demands Bandwidth from 0 to 256 Kbps NETWORK

24. 24 ISMA 2000 24 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY 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. 25 ISMA 2000 25 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Objective coefficients = number of hops

26. 26 ISMA 2000 26 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Objective coefficients = number of hops

27. 27 ISMA 2000 27 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY All objective coefficient = 1

28. 28 ISMA 2000 28 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Netflow turned on on five random routers

29. 29 ISMA 2000 29 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Netflow turned on on five most used routers

30. 30 ISMA 2000 30 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Netflow turned on on ten random routers

31. 31 ISMA 2000 31 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Comparison of different results

32. 32 ISMA 2000 32 Backbone Traffic Inference MAKE SYSTEMS THE NETWORK RESOURCE PLANNING COMPANY Conclusions Objective coefficients in LP need to be scaled Turning NetFlow on a few selected interfaces can greatly improve the traffic estimates.