Ten Years in the Evolution of the Internet Ecosystem

1. Ten Years in the Evolution of the Internet Ecosystem Amogh Dhamdhere Constantine Dovrolis College of Computing Georgia Tech

2. Motivation • How did the Internet AS ecosystem grow during the last decade? • Is growth more important than rewiring? • Is the population of transit providers increasing or decreasing? • Diversification or consolidation of transit market? • Given that the Internet grows in size, does the average AS-path length also increase?

3. Motivation (cont’) Which ASes engage in aggressive multihoming? What is the preferred type of transit provider for different AS customer types? Which ASes tend to adjust their set of providers most often? Are there regional differences in how the Internet evolves? Where is the Internet heading towards? 4/2/2014

4. Previous work • Lots of previous work in describing the structure and growth of the Internet graph • The focus was mostly graph-theoretic in nature, studying static snapshots of the (inferred) topology • Heavy-tailed degree distribution, clustering, small-world properties, and evolutionary models such as preferential attachment, etc • We focus on how the topology has been changing over time • Most relevant work: Siganos-Faloutsos^2 (TR ’01), Magoni-Pansiot (CCR‘01), Leskovec et al (KDD‘06), Oliveira et al. (Sigcomm’07) • More importantly, the Internet is much more than a graph • We need to consider business properties of ASes (“nodes”) and the semantics of AS relations (“links”) • Most relevant works: Chang/Jamin/Willinger (Sigcomm workshop:03, Infocom:06)

5. Approach • We start from BGP routes from all available RouteViews and RIPE monitors during 1997-2007 • Focus on primary links (filter transient appearance of backup links) • Not described in this talk • Classify ASes based on their business function • Enterprise ASes, small transit providers, large transit providers, access providers, content providers, etc • Classify inter-AS relations as “transit” (antagonistic) and “peering” (symbiotic) • Measure and characterize evolutionary trends of: • Global Internet • Each AS-species • Relation between species

6. Issue-1: remove backup/transient links Each snapshot of the Internet topology captures 3 months 40 snapshots – 10 years Perform “majority filtering” to remove backup and transient links from topology For each snapshot, collect several “topology samples” interspersed over a period of 3 weeks Consider an AS-path only if it appears in the majority of the topology samples Otherwise, the AS-path includes links that were active for less than 11 days (probably backup or transient links) Samples Snapshot 4/2/2014

7. Issue-2: variable set of BGP monitors • Some observed link births may be links revealed due to increased monitor set • Similarly for observed link deaths • We calculated error bounds for link births and deaths • Relative error < 10% for CP links • See paper for details

8. Issue-3: visibility of ASes, Customer-Provider (CP) and Peering (PP) links • Number of ASes and CP links is robust to number of monitors • But we cannot reliably estimate the number of PP links

9. Global Internet trends

10. Internet growth • Number of CP links and ASes showed initial exponential growth until mid-2001 • Followed by linear growth until today • Change in trajectory followed stock market crash in North America in mid-2001

11. Transit (CP) vs Peering (PP) relations • The fraction of peering links has been increasing steadily • But remember: this is just a lower bound • At least 20% of inter-AS links are of PP type today

12. The Internet gets larger but not longer • Average path length remains almost constant at 4 hops • Average multihoming degree of providers increases faster than that of stubs • Densification at core much more important than at edges

13. Rewiring is more important than growth • Most new links are due to internal rewiring and not birth (75% currently) • Most dead links are due to internal rewiring and not death (almost 90% currently)

14. Classification of ASes in “species” based on business type & function

15. Classification of ASes based on business function • Four AS types: • Enterprise customers (EC) • Small Transit Providers (STP) • Large Transit Providers (LTP) • Content, Access and Hosting Providers (CAHP) • Classification based on customer and peering degrees • Classification based on decision-trees • 80-85% accurate

16. Evolution of AS types • LTPs: constant population (top-30 ASes in terms of customers) • Slow growth of STPs (30% increase since 2001) • EC and CAHP populations produce most growth • Since 2001: EC growth factor 2.5, CAHP growth factor 1.5

18. Regional distribution of AS types • Based on “whois” registration entry for each AS • Europe is catching up with North America w.r.t the population of ECs and LTPs • CAHPs have always been more in Europe • More STPS in Europe since 2002

19. Evolution of Internet transit: the customer’s perspective

20. How common is multihoming among AS species? • CAHPs have increased their multihoming degree significantly • On the average, 8 providers for CAHPs today • Multihoming degree of ECs has been almost constant (average < 2) • Densification of the Internet occurs at the core

21. Who prefers large vs small transit providers? • After 2004, ECs prefer STPs than LTPs • Mainly driven by lower prices or regional constraints? • CAHPs connect to LTPs and STPs with same probability

22. Customer activity by region • Initially most active customers were in North America • After 2004-05, customers in Europe have been more active • Due to increased availability of providers? • More competitive market?

23. Evolution of Internet transit: the provider’s perspective

24. Attractiveness (repulsiveness) of transit providers • Attractiveness of provider X: fraction of new CP links that connect to X • Repulsiveness, defined similarly • Both metrics some positive correlation with customer degree • Preferential attachment and preferential detachment of rewired links

25. Evolution of attractors and repellers • A few providers (50-60) account for 50% of total attractiveness (attractors) • The total number of attractors and repellers increases • The Internet is NOT heading towards oligopoly of few large players • LTPs dominate set of attractors and repellers • CAHPs are increasingly present however

26. Correlation of attractiveness and repulsiveness • Timeseries of attractiveness and repulsiveness for each provider • Calculate cross-correlation at different lags • Most significant correlation values at lags 1,2 and 3 • Attractiveness precedes repulsiveness by 3-9 months

27. Evolution of Internet peering (conjectures)

28. Evolution of Internet Peering • ECs and STPs have low peering frequency • Aggressive peering by CAHPs after 2003 • Open peering policies to reduce transit costs

29. Which AS pairs like to peer? • Peering by CAHPs has increased significantly • CAHPs try to get close to sources/destinations of content • Peering by LTPs has remained almost constant (or declined) • “Restrictive” peering by LTPs

30. Conclusions Where is the Internet heading towards? Initial exponential growth up to mid-2001, followed by linear growth phase Average path length practically constant Rewiring more important than growth Need to classify ASes according to business type ECs contribute most of the overall growth Increasing multihoming degree for STPs, LTPs and CAHPs Densification at core CAHPs are most active in terms of rewiring, while ECs are least active 4/2/2014

31. Conclusions Where does the Internet head toward? • Positive correlations between attractiveness & repulsiveness of provider and its customer degree • Strong attractiveness precedes strong repulsiveness by period of 3-9 months • Number of attractors and repellers between shows increasing trend • The Internet market will soon be larger in Europe than in North America • In terms of number of transit providers and CAHPs • Providers from Europe increasingly feature in the set of attractors and repellers

32. Extra slides

33. Rewiring is more common at the Internet core Jaccard distance: measures the difference between two graphs Non-stub ASes (ISPs mostly) are more aggressive in terms of rewiring 4/2/2014

34. Activity of AS types ECs are least active (most inert) CAHPs show high rewiring activity after 2001 4/2/2014

35. Attractors and Repellers • A few providers (50-60) account for 50% of total attractiveness • Similar for repulsiveness • Heavy hitters called “Attractors” and “Repellers”