Transcript slides - Caida

Ten Years in the Evolution of
the Internet Ecosystem
Amogh Dhamdhere
Constantine Dovrolis
College of Computing
Georgia Tech
Motivation
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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?
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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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Motivation (cont’)
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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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Previous work
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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
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We focus on how the topology has been changing over
time
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Heavy-tailed degree distribution, clustering, small-world properties,
and evolutionary models such as preferential attachment, etc
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
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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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Approach
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We start from BGP routes from all available
RouteViews and RIPE monitors during 1997-2007
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Classify ASes based on their business function
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Focus on primary links (filter transient appearance of backup
links)
Not described in this talk
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:
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Global Internet
Each AS-species
Relation between species
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Issue-1: remove backup/transient links
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Each snapshot of the Internet topology captures 3
months
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40 snapshots – 10 years
Perform “majority filtering” to remove backup and
transient links from topology
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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
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Issue-2: variable set of BGP monitors
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Some observed link births may be links revealed due to increased
monitor set
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Similarly for observed link deaths
We calculated error bounds for link births and deaths
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Relative error < 10% for CP links
See paper for details
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Issue-3: visibility of ASes, CustomerProvider (CP) and Peering (PP) links
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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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Global Internet trends
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Internet growth
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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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Transit (CP) vs Peering (PP) relations
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The fraction of peering links has been increasing steadily
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But remember: this is just a lower bound
At least 20% of inter-AS links are of PP type today
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The Internet gets larger but not longer
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Average path length remains almost constant at 4 hops
Average multihoming degree of providers increases faster than
that of stubs
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Densification at core much more important than at edges
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Rewiring is more important than growth
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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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Classification of ASes in “species”
based on business type & function
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Classification of ASes based on
business function
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Four AS types:
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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
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80-85% accurate
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Evolution of AS types
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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
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Since 2001: EC growth factor 2.5, CAHP growth factor 1.5
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Regional distribution of AS types
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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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Evolution of Internet transit:
the customer’s perspective
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How common is multihoming among AS
species?
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CAHPs have increased their multihoming degree significantly
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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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Who prefers large vs small transit
providers?
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After 2004, ECs prefer STPs than LTPs
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Mainly driven by lower prices or regional constraints?
CAHPs connect to LTPs and STPs with same probability
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Customer activity by region
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Initially most active customers were in North America
After 2004-05, customers in Europe have been more active
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Due to increased availability of providers?
More competitive market?
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Evolution of Internet transit:
the provider’s perspective
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Attractiveness (repulsiveness) of transit providers
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Attractiveness of provider X: fraction of new CP links that connect to X
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Repulsiveness, defined similarly
Both metrics some positive correlation with customer degree
Preferential attachment and preferential detachment of rewired links
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Evolution of attractors and repellers
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A few providers (50-60) account for 50% of total
attractiveness (attractors)
The total number of attractors and repellers increases
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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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Correlation of attractiveness and
repulsiveness
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Timeseries of attractiveness and repulsiveness for each provider
Calculate cross-correlation at different lags
Most significant correlation values at lags 1,2 and 3
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Attractiveness precedes repulsiveness by 3-9 months
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Evolution of Internet peering
(conjectures)
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Evolution of Internet Peering
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ECs and STPs have low peering frequency
Aggressive peering by CAHPs after 2003
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Open peering policies to reduce transit costs
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Which AS pairs like to peer?
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Peering by CAHPs has increased significantly
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CAHPs try to get close to sources/destinations of content
Peering by LTPs has remained almost constant (or declined)
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“Restrictive” peering by LTPs
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Conclusions
Where is the Internet heading towards?
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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
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Densification at core
CAHPs are most active in terms of rewiring, while ECs
are least active
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Conclusions
Where does the Internet head toward?
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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
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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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Extra slides
Rewiring is more common at the Internet core
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Jaccard distance: measures the difference between two graphs
Non-stub ASes (ISPs mostly) are more aggressive in terms of
rewiring
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Activity of AS types
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ECs are least active (most inert)
CAHPs show high rewiring activity after 2001
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Attractors and Repellers
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A few providers (50-60) account for 50% of total
attractiveness
Similar for repulsiveness
Heavy hitters called “Attractors” and “Repellers”
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