1. 10-Year History of Internet Delay 1 April 24, 2010, dklee@an.kaist.ac.kr DK Lee, Kenjiro Cho*, Gianluca Iannaccone**, Sue Moon CAIDA-WIDE-CASFI Joint Workshop April 24, 2010 Division of Computer Science, KAIST *IIJ Research Laboratory **Intel Research, Berkeley

2. For the Last Few Decades Many large-scale Internet measurements: – NLANR AMP, CAIDA’s Ark, DIMES, iPlane – UCSD network teloscope, RouteViews, RIPE RIS About the Internet evolution, we know that – Internet topology has been shrunken in terms of the average AS hop count (by network densification) – Dominant Internet traffic types have changed from web to peer-to-peer traffic April 24, 2010, dklee@an.kaist.ac.kr 2

3. What do we know about the overall Internet delay performance?

4. What We Know About Internet Delay Transmission delay – Improved with faster link speed Propagation delay – Improved with new undersea cables Queuing and processing delay – Improved with faster devices Routing Issues – Loops or detours from VPNs, overlays – Delays can be Improved with new AS peering practices April 24, 2010, dklee@an.kaist.ac.kr 4

5. Then, Has the Internet grown shorter in delay?

6. What are the basic rules that govern the long-term dynamics of the Internet delay?

7. Talk Outline Has the Internet delay gotten better or worse? Two main methodologies: – Path stitching – Random sampling of the Internet host pairs Data sets Preliminary results – Delay distributions from 2004 to 2009 April 24, 2010, dklee@an.kaist.ac.kr 7

8. Reasons for No Authoritative Statement About the Internet-wide Delay “Random sampling” has not been feasible – No measurement system with access to every AS and subnet of the Internet – No rigorous method to address bias in Internet sampling Only a selective set of statistics has been possible – Stability, variation, and abrupt changes of delay as a path statistic have been well studied April 24, 2010, dklee@an.kaist.ac.kr 8

9. Path Stitching for Random Sampling April 24, 2010, dklee@an.kaist.ac.kr 9 Internet-wide path and round-trip delay estimation between any pair of Internet hosts by recycling existing data – Keep database of end-to-end measurement data segmented by the AS – Identifies relevant segments efficiently – Produces path and round-trip delay estimates, by stitching segments together

10. Path Segment Repository Indexing the path segments by the AS number :A: Intra-domain segments of A : :B: Intra-domain segments of B : A::B Inter-domain segments between A and B : :A: + A::B + :B: = Router-level paths from A to B : March 15, 2010, dklee@an.kaist.ac.kr 10 ABC traceroute outputs: AS path: a1a1 a2a2 a3a3 a4a4 b1b1 b2b2 b3b3 c1c1 c2c2 c3c3 a1a1 a2a2 a3a3 a4a4 b1b1 b2b2 b3b3

11. Overview of Path Stitching Question: Answers: April 24, 2010, dklee@an.kaist.ac.kr 11 a ?c A C B Step 1. IP-to-AS mapping Step 2. AS path inference :A: rtt A :C: rtt C :B: rtt B A::B rtt AB Step 3. Path stitching :A::B::C: B::C rtt BC Router-level paths and RTT from a to c ? Path = RTT = rtt A + rtt AB + rtt B + rtt BC + rtt C

12. Results of Path Stitching We evaluate the Internet-wide coverage and accuracy of the estimated results – More than 70% of pairs are covered by the algorithm – 80% of pairs have absolute errors less than 20msec – Median absolute error is less than 5msec Reference: DK Lee, Keon Jang, Changhyun Lee, Gianluca Iannaccone, Sue Moon, “Internet-wide Path and Delay Estimation from Existing Measurements”, IEEE INFOCOM 2010 Mini-conference April 24, 2010, dklee@an.kaist.ac.kr 12

13. Survey Design: Select a Set of Host Pairs to Examine Random sampling design with size n – Internet consists of N unique pairs of /24 IP prefixes – Extract all routable /24 prefixes from BGP table – Randomly select n=10,000 pairs Fraction of responded pairs with path stitching – 67% in 2004 and 65% in 2009 April 24, 2010, dklee@an.kaist.ac.kr 13

14. Sampling Errors for the Population Median – (1) Confidence Interval (CI) for the population median estimator: = q 0.5 ± In 2009/06, n = 10,000 = 211.6 ± 4.9 msec In 2009/06, n = 100,000 = 213.0 ± 1.3 msec April 24, 2010, dklee@an.kaist.ac.kr 14

15. Sampling Errors for the Population Median – (2) April 24, 2010, dklee@an.kaist.ac.kr 15 Sample size n=100,000 is very accurate

16. Sampling Errors for the Population Median – (3) April 24, 2010, dklee@an.kaist.ac.kr 16 Results for the sample size n>=10,000 are almost identical

17. Data Sets: E2E Measurements + Routing Data End-to-end path and delay measurement – Traceroute measurements CAIDA Ark project (from 1998~) NLANR’s AMP project (from 1999~) Routing information – BGP routing tables University of Oregon, RouteViews (from 1997~) RIPE RIS (from 1999~) April 24, 2010, dklee@an.kaist.ac.kr 17

18. Data Processing Oveview For each YYYY/MM, we process: April 24, 2010, dklee@an.kaist.ac.kr 18 Queries Path and delay estimations for queries

19. We choose a set of host pairs in each year, and see the delay distribution

20. Delay distributions from 2004 to 2009 April 24, 2010, dklee@an.kaist.ac.kr 20

21. Delay distributions from 2004 to 2009 April 24, 2010, dklee@an.kaist.ac.kr 21

22. 2004 vs. 2009 Median delay: 164.0 msec  211.6 msec April 24, 2010, dklee@an.kaist.ac.kr 22

23. Median Delays Increase Constantly Delay distribution has gotten worse from 2004 to 2009, both at first/last mile and in the core IP/AS hop counts decreased end-to-end – IP hop counts: 14.8 (2004)  14.1 (2009) – AS hop counts: 3.77 (2004)  3.65 (2009) April 24, 2010, dklee@an.kaist.ac.kr 23

24. But what if we choose the same set of host pairs?

25. Delay distributions from 2004 to 2009 (For the Same Pairs) Only 2432 pairs are constantly responded from 2004 to 2009 April 24, 2010, dklee@an.kaist.ac.kr 25

26. Delay distributions from 2004 to 2009 (For the Same Pairs) April 24, 2010, dklee@an.kaist.ac.kr 26

27. 2004 vs. 2009 (For the Same Pairs) Median delay: 163.5 msec  156.931 msec April 24, 2010, dklee@an.kaist.ac.kr 27

28. Median Delays improved (For the Same Pairs) Delay distributions for the same set of sample host pairs remain almost identical or slightly improved from 2004 to 2009 IP/AS hop counts decreased April 24, 2010, dklee@an.kaist.ac.kr 28

29. Finding the corroborating Evidence for the observations IP address usage have expanded from 2004 to 2009 – /24 prefixes of those hosts in 2009 existed in 2004? – ASes of those hosts in 2009 existed in 2004? In sampled pairs in 2009, compared to 2004, 1729 ASes are disappeared, 2091 Ases are newly appeared. Network densification helps AS hop count to decrease. Does it also help IP hop count or delay to decrease? April 24, 2010, dklee@an.kaist.ac.kr 29

30. Other Challenges Analyzing the delay distribution in 1999 – Skitter’s old-format does not have hop-by-hop delays – NLANR AMP dataset is too small – RouteViews have very restricted number of peers Effect of non-response – Where does the missing 35% come from? Effect of measurement errors April 24, 2010, dklee@an.kaist.ac.kr 30

31. Conclusion We present the methodology for the Internet delay history reconstruction and analysis: – Path-stitching with existing measurements – Random sampling of the Internet host pairs Our approach is very feasible in showing insight about the overall Internet delay distribution April 24, 2010, dklee@an.kaist.ac.kr 31

32. Thank You! Any Questions? We are looking for other traceroute outputs and BGP table snapshots archived before 2000 April 24, 2010, dklee@an.kaist.ac.kr 32

33. Backup Slides “To get to the essence of things, one has to work long and hard” -- Vincent van Gogh

34. What If There Are Too few segments: Too many segments: April 24, 2010, dklee@an.kaist.ac.kr 34 A::B ?B::C :A: :C: :B: ?...

35. When There Are Too Few or No Segments

36. We Employ Approximations (i) Missing AS »No solutions (other than collecting more measurements. ) (ii) Missing inter-domain segment »Search for reverse path segments. (i.e., if we cannot find A::B, use B::A instead) (iii) Path segments do not rendezvous at the same address (i.e., the segment cannot be stitched) »Identify nearby segments (on the same router, PoP, Prefix) April 24, 2010, dklee@an.kaist.ac.kr 36 :A: :B: B::A X Z Y W A X::A::W = ?

37. When There Are Too Many Segments

38. We Apply Preference Rules Rank the list of candidate path segments – Eliminate candidates as many as possible while keeping the most accurate one. – Reflect the actual routing mechanism April 24, 2010, dklee@an.kaist.ac.kr 38 Source AS Destination AS Intermediate ASes...... Rule #1, 2, 3 Rule # 2, 3

39. Rule #1: Proximity Preference to the path segments that closest to the queried source and destination address April 24, 2010, dklee@an.kaist.ac.kr 39 Source AS a.b.c.1 a.b.c.2 a.b.1.1 d.b.1.2 x.y.z.1 x.y.z.2 Query: a.b.c.d --> x.y.z.w

40. Rule #2: Destianation-bound Preference to the segments from traceroutes with the same destination prefix April 24, 2010, dklee@an.kaist.ac.kr 40 Query: a.b.c.d --> x.y.z.w Source AS Original traceroutes traceroutes to x.y.z.1 traceroutes to u.v.w.1

41. Rule #3: Most Recent Preference to the most recent path segment April 24, 2010, dklee@an.kaist.ac.kr 41 Query: a.b.c.d --> x.y.z.w Source AS Original traceroutes traceroutes to x.y.z.1 YYYYMMDD-12:30:00 YYYYMMDD-10:30:00

42. Comparisons with iPlane – (1) April 24, 2010, dklee@an.kaist.ac.kr 42 CDF of absolute errors for pl-easy pairs Errors <= 20ms for 90% of pl-easy pairs

43. Comparisons with iPlane – (1) CDF of absolute errors for pl-hard pairs April 24, 2010, dklee@an.kaist.ac.kr 43 Very promising results: With accurate AS paths inference, errors <= 20ms for 80% of pl-hard pairs