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Learning ISP Policies from Traceroute Data

Learning ISP Policies from Traceroute Data. Michael Cafarella Daniel Lowd December 8, 2004. Internet Routing. Rocketfuel (SMWA, ’02). Maps networks with traceroutes. Path Inflation (SMA ’03). After learning topology, use traces to determine inter-ISP router policies: Early Exit Late Exit

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Learning ISP Policies from Traceroute Data

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  1. Learning ISP Policies from Traceroute Data Michael Cafarella Daniel Lowd December 8, 2004

  2. Internet Routing

  3. Rocketfuel (SMWA, ’02) • Maps networks with traceroutes

  4. Path Inflation (SMA ’03) • After learning topology, use traces to determine inter-ISP router policies: • Early Exit • Late Exit • Idiosyncratic engineered routes • Others? • PI compares true distance against measured time in ISPs • Can we use ML to describe policies in greater detail?

  5. Data • Each trace creates a record of machines encountered, and RTTs to reach them • Data is filthy Probe: 1:ner-routes.bbnplanet.net:ha Network: 216.138.90.0/24 Target: 216.138.90.94 Reasons: updown Time: Thu Dec 27 20:17:23 2001 TargetAS: 3356 [AS1:ner-routes.bbnplanet.net:ha] 1 e0-1-5.burlma1-ops1.bbnplanet.net (4.2.34.162) 0 msec 4 msec 0 msec 2 s7-0-4.bstnma1-cr7.bbnplanet.net (4.0.3.181) 0 msec 0 msec 4 msec 3 so-3-1-0.bstnma1-nbr1.bbnplanet.net (4.24.4.225) 4 msec 4 msec 0 msec …

  6. Data Cleaning [AS1:ner-routes.bbnplanet.net:ha] 1 e0-1-5.burlma1-ops1.bbnplanet.net (4.2.34.162) 0 ms 4 ms 2 s7-0-4.bstnma1-cr7.bbnplanet.net (4.0.3.181) 4 ms 4 ms 3 so-3-1-0.bstnma1-nbr1.bbnplanet.net (4.24.4.225) 4 ms 4 *** 5 *** 6 p1-0.nycmny1-cr10.bbnplanet.net (4.24.8.170) 8 ms 7 pos4-10.core1.NewYork1.Level3.net (209.244.160.141) [AS 3356] 12 ms 12 ms 8 ms 8 64.159.17.65 12 ms ... 11 gige6-0.ipcolo2.SanFrancisco1.Level3.net (209.244.14.46) [AS 3356] 88 ms 8 4 ms 84 ms 12 unknown.Level3.net (209.246.20.6) [AS 3356] 88 ms 88 ms 13 64.63.16.3 [AS 3356] 88 ms 84 ms * [END] [AS1:ner-routes.bbnplanet.net:ha] 1 e0-1-5.burlma1-ops1.bbnplanet.net (4.2.34.162) 4 msec 0 msec 4 msec 2 s7-0-4.bstnma1-cr7.bbnplanet.net (4.0.3.181) 4 msec 4 msec 4 msec 3 so-3-1-0.bstnma1-nbr1.bbnplanet.net (4.24.4.225) 4 msec 0 msec 4 msec 4 so-7-0-0.bstnma1-nbr2.bbnplanet.net (4.24.10.218) 4 msec 4 msec 4 msec 5 p9-0.nycmny1-nbr2.bbnplanet.net (4.24.6.50) 8 msec 12 msec 12 msec 6 p1-0.nycmny1-cr10.bbnplanet.net (4.24.8.170) 8 msec 8 msec 8 msec 7 pos4-10.core1.NewYork1.Level3.net (209.244.160.141) [AS 3356] 12 msec 12 mse c 8 msec 8 ae0-53.mp1.NewYork1.Level3.net (64.159.17.65) [AS 3356] 12 msec 8 msec 12 ms Ec ... 11 gige6-0.ipcolo2.SanFrancisco1.Level3.net (209.244.14.46) [AS 3356] 88 msec 8 4 msec 84 msec 12 unknown.Level3.net (209.246.20.6) [AS 3356] 88 msec 88 msec 88 msec 13 64.63.16.3 [AS 3356] 88 msec 84 msec * [END] [AS1:ner-routes.bbnplanet.net:ha] 1 e0-1-5.burlma1-ops1.bbnplanet.net (4.2.34.162) 4 msec 0 msec 4 msec 2 s7-0-4.bstnma1-cr7.bbnplanet.net (4.0.3.181) 4 msec 4 msec 4 msec 3 so-3-1-0.bstnma1-nbr1.bbnplanet.net (4.24.4.225) 4 msec 0 msec 4 msec 4 so-7-0-0.bstnma1-nbr2.bbnplanet.net (4.24.10.218) 4 msec 4 msec 4 msec 5 p9-0.nycmny1-nbr2.bbnplanet.net (4.24.6.50) 8 msec 12 msec 12 msec 6 p1-0.nycmny1-cr10.bbnplanet.net (4.24.8.170) 8 msec 8 msec 8 msec 7 pos4-10.core1.NewYork1.Level3.net (209.244.160.141) [AS 3356] 12 msec 12 mse c 8 msec 8 64.159.17.65 12 msec 8 msec 12 ms Ec ... 11 gige6-0.ipcolo2.SanFrancisco1.Level3.net (209.244.14.46) [AS 3356] 88 msec 8 4 msec 84 msec 12 unknown.Level3.net (209.246.20.6) [AS 3356] 88 msec 88 msec 88 msec 13 64.63.16.3 [AS 3356] 88 msec 84 msec * [END] [AS1:ner-routes.bbnplanet.net:ha] 1 e0-1-5.burlma1-ops1.bbnplanet.net (4.2.34.162) 4 msec 0 msec 4 msec 2 s7-0-4.bstnma1-cr7.bbnplanet.net (4.0.3.181) 4 msec 4 msec 4 msec 3 so-3-1-0.bstnma1-nbr1.bbnplanet.net (4.24.4.225) 4 msec 0 msec 4 msec 4 *** 5 *** 6 p1-0.nycmny1-cr10.bbnplanet.net (4.24.8.170) 8 msec 8 msec 8 msec 7 pos4-10.core1.NewYork1.Level3.net (209.244.160.141) [AS 3356] 12 msec 12 mse c 8 msec 8 64.159.17.65 12 msec 8 msec 12 ms Ec ... 11 gige6-0.ipcolo2.SanFrancisco1.Level3.net (209.244.14.46) [AS 3356] 88 msec 8 4 msec 84 msec 12 unknown.Level3.net (209.246.20.6) [AS 3356] 88 msec 88 msec 88 msec 13 64.63.16.3 [AS 3356] 88 msec 84 msec * [END] [AS1:ner-routes.bbnplanet.net:ha] 1 e0-1-5.burlma1-ops1.bbnplanet.net (4.2.34.162) 4 ms 0 ms 4 ms 2 s7-0-4.bstnma1-cr7.bbnplanet.net (4.0.3.181) 4 ms 4 ms 4 ms 3 so-3-1-0.bstnma1-nbr1.bbnplanet.net (4.24.4.225) 4 ms 0 ms 4 ms 4 *** 5 *** 6 p1-0.nycmny1-cr10.bbnplanet.net (4.24.8.170) 8 ms 8 ms 8 ms 7 pos4-10.core1.NewYork1.Level3.net (209.244.160.141) [AS 3356] 12 ms 12 ms 8 ms 8 64.159.17.65 12 ms 8 ms 12 ms ... 11 gige6-0.ipcolo2.SanFrancisco1.Level3.net (209.244.14.46) [AS 3356] 88 ms 8 4 ms 84 ms 12 unknown.Level3.net (209.246.20.6) [AS 3356] 88 ms 88 ms 88 ms 13 64.63.16.3 [AS 3356] 88 ms 84 ms * [END] [AS1:ner-routes.bbnplanet.net:ha] 1 e0-1-5.burlma1-ops1.bbnplanet.net 4.2.34.162 0 ms 4 ms 2 s7-0-4.bstnma1-cr7.bbnplanet.net 4.0.3.181 4 ms 4 ms 3 (4.24.4.225)so-3-1-0.bstnma1-nbr1.bbnplanet.net 4 ms 4 *** 5 *** 6 p1-0.nycmny1-cr10.bbnplanet.net [4.24.8.170] 8 ms 7 pos4-10.core1.NewYork1.Level3.net (209.244.160.141) [AS 3356] 12 ms 12 ms 8 ms 8 64.159.17.65 12 ms ... 11 gige6-0.ipcolo2.SanFrancisco1.Level3.net (209.244.14.46) [AS 3356] 88 ms 8 4 ms 84 ms 12 unknown.Level3.net (209.246.20.6) [AS 3356] 88 ms 88 ms 13 64.63.16.3 [AS 3356] 88 ms 84 ms * [END] [AS1:ner-routes.bbnplanet.net:ha] 1 e0-1-5.burlma1-ops1.bbnplanet.net (4.2.34.162) 4 ms 0 ms 4 ms 2 s7-0-4.bstnma1-cr7.bbnplanet.net (4.0.3.181) 4 ms 4 ms 4 ms 3 so-3-1-0.bstnma1-nbr1.bbnplanet.net (4.24.4.225) 4 ms 0 ms 4 ms 4 *** 5 *** 6 p1-0.nycmny1-cr10.bbnplanet.net (4.24.8.170) 8 ms 8 ms 8 ms 7 pos4-10.core1.NewYork1.Level3.net (209.244.160.141) [AS 3356] 12 ms 12 ms 8 ms 8 64.159.17.65 12 ms 8 ms 12 ms ... 11 gige6-0.ipcolo2.SanFrancisco1.Level3.net (209.244.14.46) [AS 3356] 88 ms 8 4 ms 84 ms 12 unknown.Level3.net (209.246.20.6) [AS 3356] 88 ms 88 ms 88 ms 13 64.63.16.3 [AS 3356] 88 ms 84 ms * [END] • Unknown DNS • Missing hops • ms/msec • Inconsistent spacing • Missing RTTs • Inconsistent formatting • Etc, etc, etc

  7. Methodology • Split data (at random) into sets A and B • Find 10 most-popular ISP pairs. For each pair: • Use set A to build topology model, with RTTs on links • Use set B to generate info on each border-router decision • Analyze decisions to deduce ISP policy <numBorderRouters> <routerChosen> [rtr-1] <shortestPath(src, rtr-1)> <shortestPath(rtr-1, dst)> [rtr-2] <shortestPath(src, rtr-2)> <shortestPath(rtr-2, dst)> ... [rtr-numBorderRouters:] ...

  8. Modeling policies • Want to learn functions from data that approximate each peering policy. • Good: most likely router. • Examples: perceptron, neural net, SVM, decision trees, rule sets, nearest neighbor • Better: probability distribution over all routers. • Examples: naïve Bayes, Bayesian network, maxent/logistic regression, MRF, CRF, kernel methods

  9. Logistic regression • Probabilistic • Continuous response (no discretization) • Discriminatively trained • But: only 2 classes (in this form)

  10. Generalizing logit • Each border router has a probability: • Normalize over all border routers:

  11. Deciphering lambdas • Weights determine relative cost of sourcerouter latency vs. routerdestination latency. • Router with the largest weighted latency is always most likely. • Scale of weights determines how skewed this distribution is. • Adding fixed value to all RTTs has no effect.

  12. Special cases • Random: • Early-exit: • Late-exit: • “Optimal”-exit: These will act as baselines in our experiments.

  13. Results: log likelihood

  14. Results: accuracy

  15. Case study: AT&T  Above.net • Our accuracy: 98.55% • Next best: 75.57% (opt-exit) • Learned weights: -6,000 and -4,000

  16. Case study: Ebone  Level3 • Our accuracy: 44.37% • Next best: 32.07% (late-exit) • Learned weights: +750 and –1,400

  17. Conclusion • Learning peering policies is hard • Each ISP pair can have a different policy • Policies may be complex, or arbitrary • Simple weight-based models solve some of this problem • More flexible than early/late-exit • Offer insight into routing tradeoffs • Don’t always work

  18. Future work • Additional features: • Geographical distance • Bandwidth • MLNs (Richardson & Domingos 2004): • Represent every router in the graph • Learns local and global policies at once • Can learn engineered routes as well

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