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?
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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?
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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)
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5. Approach
We start from BGP routes from all available RouteViews and RIPE monitors during
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
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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)
Snapshot
Samples
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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
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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
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9. Global Internet trends
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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
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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
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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
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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)
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14. Classification of ASes in “species” based on business type & function
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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
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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
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17. 4/16/2017

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
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19. Evolution of Internet transit: the customer’s perspective
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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
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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
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22. Customer activity by region
Initially most active customers were in North America
After , customers in Europe have been more active
Due to increased availability of providers?
More competitive market?
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23. Evolution of Internet transit: the provider’s perspective
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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
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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
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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
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27. Evolution of Internet peering (conjectures)
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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
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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
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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
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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
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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
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34. Activity of AS types ECs are least active (most inert)
CAHPs show high rewiring activity after 2001
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35. Attractors and Repellers
A few providers (50-60) account for 50% of total attractiveness
Similar for repulsiveness
Heavy hitters called “Attractors” and “Repellers”
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