Amogh Dhamdhere

Provider and Peer Selection in the Evolving Internet

Ecosystem

Committee:Dr. Constantine Dovrolis (advisor)Dr. Mostafa AmmarDr. Nick FeamsterDr. Ellen ZeguraDr. Walter Willinger (AT&T Labs-Research)

The Internet Ecosystem

27,000 autonomous networks independently operated and managed

The “Internet Ecosystem” Different types of networks Interact with each other and with “environment”

Network interactions Localized, in the form of interdomain links Competitive (customer-provider), symbiotic (peering)

Distributed optimizations by each network Select providers and peers to optimize utility function

The Internet ecosystem evolves04/19/23 2

High Level Questions

How does the Internet ecosystem evolve? What is the Internet heading towards?

Topology Economics Performance

How do the strategies used by networks affect their utility (profits/costs/performance)?

How do these individual strategies affect the global Internet?

04/19/23 3

Previous Work

Static graph properties No focus on how the graph

evolves

“Descriptive” modeling Match graph properties

e.g. degree distribution Homogeneity

Nodes and links all the same

Game theoretic, computational Restrictive assumptions

Dynamics of the evolving graph Birth/death Rewiring

“Bottom-up” Model the actions of

individual networks Heterogeneity

Networks with different incentives

Semantics of interdomain links

04/19/23 4

Our Approach

“Measure – Model – Predict”

04/19/23 5

Measure the evolution of the Internet EcosystemTopological, but focus on business types and rewiring

Model strategies and incentives of different network types

Predict the effects of provider and peer selection strategies

Outline

Measuring the Evolution of the Internet Ecosystem

The Core of the Internet: Provider and Peer Selection for Transit Providers

The Edge of the Internet: ISP and Egress Path Selection for Stub Networks

ISP Profitability and Network Neutrality

04/19/23 6

[IMC ’08]

[to be submitted]

[Netecon ’08]

[Infocom ‘06]

Motivation

How did the Internet ecosystem evolve 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 path length also increase? Where is the Internet heading?

04/19/23 7

Approach Focus on Autonomous Systems (ASes)

As opposed to networks without AS numbers Start from BGP routes from RouteViews and RIPE

monitors during 1997-2007 Focus on primary links

Classify ASes based on their business function Enterprise ASes, small transit providers, large transit

providers, access providers, content providers

Classify inter-AS relations as “transit” and “peering” Transit link – Customer pays provider Peering link – No money exchanged

04/19/23 8

Visibility Issue

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-200104/19/23 9

Path lengths stay constant

Number of ASes has grown from 5000 in 1998 to 27000 in 2007

Average path length remains almost constant at 4 hops

04/19/23 10

Rewiring more important than growth

Most new links due to internal rewiring and not birth (75%)

Most dead links are due to internal rewiring and not death (almost 90%)

04/19/23 11

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) Based on customer and

peer degrees Classification based on

decision-trees 80-85% accurate04/19/23 12

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 1.504/19/23 13

Multihoming by AS types

CAHPs have increased their multihoming degree significantly On the average, 8 providers for CAHPs today

Multihoming degree of ECs almost constant (average < 2) Densification of the Internet occurs at the core

04/19/23 14

Conjectures on the Evolution of peering

Peering by CAHPs has increased significantly CAHPs try to get close to sources/destinations of content

04/19/23 15

Conclusions Where is the Internet heading?

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 the core

CAHPs are most active in terms of rewiring, while ECs are least active

1604/19/23

Outline

Measuring the Evolution of the Internet Ecosystem

The Core of the Internet: Provider and Peer Selection for Transit Providers

The Edge of the Internet: ISP and Egress Path Selection for Stub Networks

ISP Profitability and Network Neutrality

04/19/23 17

Modeling the Internet Ecosystem

From measurements: Significant rewiring activity Especially by transit providers

Networks rewire connectivity to optimize a certain objective function Distributed Localized spatially and temporally Rewiring by changing the set of providers and peers

What are the global, long-term effects of these distributed optimizations? Topology and traffic flow Economics Performance (path lengths)

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The Feedback Loop

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When does it converge?When no network has the incentive to change its connectivity – “steady-state”

InterdomainTM

Traffic flow

Interdomain topology

Per-ASprofit

Cost/priceparameters

Routing

Providerselectio

n

Peer selectio

n

Impact of provider/peer selection strategies

04/19/23 20

C CS

S S

C

C

P1

P2 P3

S

No peeringP2 and P3 peer

P1 open to peering with CPs

Our Approach

What is the outcome when networks use certain provider and peer selection strategies?

Model the feedback loop in the Internet ecosystem Real-world economics of transit, peering, operational costs Realistic routing policies Geographical constraints Provider and peer selection strategies

Computationally find a “steady-state” No network has further incentive to change connectivity

Measure properties of the steady-state Topology, traffic flow, economics

04/19/23 21

Network Types

Enterprise Customers (EC) Stub networks at the edge (e.g. Georgia Tech) Either sources or sinks

Small Transit Providers (STP) Provide Internet transit Mostly regional in presence (e.g. France Telecom)

Large Transit Providers (LTP) Transit providers with global presence (e.g. AT&T)

Content Providers (CP) Major sources of content (e.g. Google)

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Provider and peer selection for STPs and LTPs

What would happen if..? The traffic matrix consists of mostly P2P traffic? P2P traffic benefits STPs, can make LTPs

unprofitable

LTPs peer with content providers? LTPs could harm STP profitability, at the expense of

longer end-to-end paths

Edge networks choose providers using path lengths?

LTPs would be profitable and end-to-end paths shorter

04/19/23 23

Provider and Peer Selection

Provider selection strategies Minimize monetary cost (PR) Minimize AS path lengths weighted by traffic (PF) Avoid selecting competitors as providers (SEL)

Peer selection strategies Peer only if necessary to maintain reachability (NC) Peer if traffic ratios are balanced (TR) Peer by cost-benefit analysis (CB)

Peer and provider selection are related

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Provider and Peer Selection are Related

04/19/23 25

A A

C

C

B B

U

U

B B

U

U

A A

C

C

Peering by necessity

Level3-Cogent peering dispute

Restrictive peering

A A

CC

B B

?X

Economics, Routing and Traffic Matrix

Realistic transit, peering and operational costs Transit prices based on data from Norton Economies of scale

BGP-like routing policies No-valley, prefer customer, prefer peer routing policy

Traffic matrix Heavy-tailed content popularity and consumption by sinks Predominantly client-server: Traffic from CPs to ECs Predominantly peer-to-peer: Traffic between ECs

04/19/23 26

Algorithm for network actions

Networks perform their actions sequentially Can observe the actions of previous networks

And the effects of those actions Network actions in each move

Pick set of preferred providers Attempt to convert provider links to peering links “due to

necessity” Evaluate each existing peering link Evaluate new peering links

Networks make at most one change to their set of peers in a single move

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Solving the Model

Determine the outcome as each network selects providers and peers according to its strategy

Too complex to solve analytically: Solve computationally

Typical computation Proceeds iteratively, networks act in a predefined

sequence Pick next node n to “play” its possible moves Compute routing, traffic flow, AS fitness Repeat until no player has incentive to move “steady-state” or equilibrium

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Properties of the steady-state

Is steady-state always reached? Yes, in most cases

Is steady-state unique? No, can depend on playing sequence Different steady-states have qualitatively similar

properties

Multiple runs with different playing sequence Average over different runs Confidence intervals are narrow

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Canonical Model

Parameterization of the model that resembles real world

Traffic matrix is predominantly client-server (80%) Impact of streaming video, centralized file sharing

services 20% of ECs are content sources, 80% sinks Heavy tailed popularity of traffic sources Edge networks choose providers based on price 5 geographical regions STPs cheaper than LTPs

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Model Validation

Reproduces almost constant average path length Activity frequency: How often do networks change

their connectivity? ECs less active than providers – Qualitatively similar to

measurement results

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Results – Canonical Model

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EC

LTP

S1

S2 CP

CP

CP

EC

Hierarchy of STPs

Traffic can bypass LTPs – LTPs unprofitable

STPs should not peer with CPsResist the temptation!

Results – Canonical Model

04/19/23 33

LTP

S1

S2

CP

CP

CP

EC EC

What-if: LTPs peer with CPs

Generate revenue from downstream traffic

Can harm STP fitness

Long paths

Deviation 1: P2P Traffic matrix

04/19/23 34

LTP

S1

S2

CP

CP

CP

EC EC

P2P traffic helps STPsSmaller traffic volume from CPs to Ecs

More EC-EC traffic => balanced traffic ratiosMore opportunities for STPs to peerPeering by “traffic ratios” makes sense

LTP

S1

S2

EC EC

S3

EC EC

Conclusions

A model that captures the feedback loop between topology, traffic and fitness in the Internet

Considers effects of Economics Geography Heterogeneity in network types

Predict the effects of provider and peer selection strategies Topology, traffic flow, economics, and performance

04/19/23 35

Outline

Measuring the Evolution of the Internet Ecosystem

The Core of the Internet: Peer and Provider Selection for Transit Providers

The Edge of the Internet: ISP and Egress Path Selection for Stub Networks

ISP Profitability and Network Neutrality

04/19/23 36

The Edge of the Internet

Sources and sinks of content Content Providers (CP): sources Enterprise Customers (EC): sinks

From measurements: ECs connect increasingly to STPs

Cost conscious ? CPs connect increasingly to LTPs

Performance ?

Increasing multihoming (about 60% of stubs) Redundancy, load balancing, cost effectiveness

How should stub networks choose their providers?

04/19/23 37

Major Questions How to select the set of

upstream ISPs ? Low monetary cost Short AS paths to major

destinations Path diversity to major traffic

destinations – robustness to network failures

How to allocate egress traffic to the set of selected ISPs ? Objective: Avoid congestion on

the upstream paths Also maintain low cost

04/19/23 38

ISP Selection Select k ISPs out of N

Let C be a subset of k ISPs out of N

Total cost of a selection of ISPs C: Weighted sum of monetary, path length and path diversity costs

Select combination C with minimum total cost Feasible to enumerate all combinations

04/19/23 39

Monetary and Path Length Cost

For set of ISPs C, what is the monetary and path length cost of routing egress flows?

Find the minimum cost mapping G* of flows to ISPs (Bin Packing) Flows = items ISPs = bins NP hard !

Use First Fit Decreasing (FFD) heuristic Mapping G* very close to optimal

Monetary and path length costs of C are calculated using the mapping G*

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Path Diversity selection C gives K paths to

each destination d

K-shared link to d: link shared by all K paths to d If a K-shared link fails, destination

d is unreachable Minimize the number of K-

shared links Path diversity metric: The number

of k-shared links to destination d averaged over all destinations

Gives the best resiliency to single-link failures

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Summary Algorithms for ISP selection

Choosing best set of upstream ISPs Objectives are minimum monetary cost, short AS paths

and high path diversity

ISP selection for monetary and performance constraints Formulated as a bin-packing problem Heuristic gives solution very close to optimal

ISP selection for path diversity Returns set of ISPs with best path diversity to the set of

major destinations04/19/23 42

Outline

Measuring the Evolution of the Internet Ecosystem

The Edge of the Internet: ISP and Egress Path Selection for Stub Networks

The Core of the Internet: Peer and Provider Selection for Transit Providers

ISP Profitability and Network Neutrality

04/19/23 43

The debate

Recent evolution trend: Large amounts of video and peer-to-peer traffic

Content providers (CP) generate the content Provide content and services “over the top” of the basic

connectivity provided by ISPs Profitable (think Google)

Access Providers (AP) deliver content to users Recent trend: Not profitable Commoditization of basic Internet access Want a share of the pie

Tension between AP and CPs: “Network neutrality”

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A Technical View

Previous work Mostly non-technical Highly emotional debates in the press Legislation/policy aspects: Do we need network

neutrality legislation? But what about the underlying problem: Non-

profitability of Access Providers? Our approach: A quantitative look at AP

profitability Investigate reasons for non-profitability Evaluate strategies for remaining profitable

04/19/23 45

Modeling AP Profitability

Three AS types: AP, CP and transit provider (TP)

Focus on the AP AS links

customer-provider (customer pays provider)

peering (no payments) AP and CPs can transfer

traffic either through customer-provider or peering links

04/19/23 46

AP Profitability

Reasons why APs can be unprofitable AP users The impact of video traffic

AP strategies: Pricing Heavy hitter charging Heavy hitter blocking Non-neutral charging

AP strategies: Connection Caching CP content Peering selectively with CPs

04/19/23 47

Major Findings

Variability in AP users can cause large variability in costs

Video traffic: Increases costs for AP

AP strategies based on differential/non-neutral pricing may not succeed Have to account for user departure due to competition

AP strategies based on connection are promising Caching content from CPs Peering selectively with large CPs

04/19/23 48

Contributions of this Thesis

A measurement study of the evolution of the Internet ecosystem

Modeling the evolution of the Internet ecosystem “what-if” questions about possible evolution paths

Optimizations at the edge of the Internet Algorithms for provider selection and egress routing

A technical view of the network neutrality debate Strategies for ISP profitability

04/19/23 49

Future Directions

Measurements: Investigate the evolution of the connectivity for monitor ASes We can observe all links for such ASes Focus on transitions between peering and customer-

provider links

Measurements: What does the interdomain traffic matrix really look like? Can we use measurements from a large Tier-1 provider? Can we augment that data with information about the

interdomain topology?

04/19/23 50

Future Directions

What is the best strategy for different types of providers? Strategies for classes of providers Strategies for individual providers

Do the distributed optimizations by networks solve a centralized problem? E.g., minimizing path lengths

04/19/23 51

Other things I’ve been up to Router buffer sizing

“Buffer Sizing For Congested Internet Links” “Open Issues in Router Buffer Sizing”

Network troubleshooting “NetDiagnoser: Troubleshooting network disruptions

using end-to-end probes and routing data” Network monitoring

“Route monitoring from passive data plane measurements”

Measurement “Poisson vs. Periodic Path Probing” “Bootstrapping in Gnutella”

04/19/23 52

[Infocom ‘05]

[CCR ‘06]

[CoNext ‘07]

[In progress]

[IMC ‘05]

[PAM ‘04]

Thank You !

04/19/23 53

04/19/23

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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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

04/19/23 55

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

04/19/23 56

Global Internet trends

04/19/23 57

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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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

04/19/23 59

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Regional distribution of AS types

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 200204/19/23 61

Evolution of Internet transit: the customer’s perspective

04/19/23 62

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?

04/19/23 63

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

04/19/23 64

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

04/19/23 65

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?

04/19/23 66

Evolution of Internet transit: the provider’s perspective

04/19/23 67

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

04/19/23 68

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 however04/19/23 69

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 months04/19/23 70

Evolution of Internet peering (conjectures)

04/19/23 71

Evolution of Internet Peering

ECs and STPs have low peering frequency Aggressive peering by CAHPs after 2003

Open peering policies to reduce transit costs

04/19/23 72

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

04/19/23 73

04/19/23

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 active04/19/23 74

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

04/19/23 75

Multihoming Multihoming: Connection of

a stub network to multiple ISPs x% of stub networks are

multihomed Redundancy

primary/backup relationships Load Balancing

Distribute outgoing traffic among ISPs

Cost Effectiveness Lower cost ISP for bulk traffic,

higher cost ISP for performance-sensitive traffic

Performance Intelligent Route Control

04/19/23 76

ISP selection

Should consider both monetary cost and performance

Minimum monetary cost Estimate the cost that “would be” incurred if a set of

ISPs was selected Minimum AS path lengths

Longer paths: delays, interdomain routing failures Measure AS path length offline using Looking Glass

Servers Maximum Path diversity

AS-level paths to destinations should be as “different” as possible

04/19/23 77

Problem Definition Two phases Phase I – ISP Selection:

Select K upstream ISPs K depends on monetary and performance constraints “Static” operation Change only when major changes in the traffic

destinations or ISP pricing Phase II – Egress Path Selection

Allocate egress traffic to selected ISPs Avoid long term congestion and minimize cost “Semi-static” operation, performed every few hours or

days

04/19/23 78

Evaluation – Path Diversity

AS-level paths and traffic rates are input to simulator 9 ISPs, 250 destinations

Given K, find the selection C* with the minimum path diversity cost

For each selection C, find u(C) = total traffic lost due to the failure of each link in topology

Calculate Δu(C) = u(C) – u(C*) for each selection C

04/19/23 79

Single link failures: C* is the optimal selection

Egress Path Selection After Phase-I, S has K upstream ISPs Problem: How to map outgoing traffic to the ISPs M flows: KM mappings of flows to ISPs

Some mappings may cause congestion to flows ! Flows can be congested at access links or further upstream

Objective: Find the loss-free mapping with the minimum cost

Challenges: Upstream topology and capacities are unknown Iterative routing approaches required

Propose an iterative routing based on simulated annealing

04/19/23 80

Evaluation – Path Diversity

AS-level paths and traffic rates are input to simulator 9 ISPs, 250 destinations

Given K, find the selection C* with the minimum path diversity cost

For each selection C, find u(C) = total traffic lost due to the failure of each link in topology

Calculate Δu(C) = u(C) – u(C*) for each selection C

04/19/23 81

Single link failures: C* is the optimal selection

2,3 link failures: C* is close to the optimal selection

Provider and Peer Selection

Detailed model for provider and peer selection Complex real-world decisions

Provider selection objectives Monetary cost AS path lengths

Peer selection Minimize transit costs Maintain reachability

Constraints Only local knowledge Geographical constraints

04/19/23 82

Peering Federation

Traditional peering links: Not transitive Peering federation of A, B, C: Allows mutual transit

Longer chain of “free” traffic Incentives to join peering federation? What happens to tier-1 providers if smaller providers form

federations?

04/19/23 83

A A B B C C

Why can the AP be unprofitable?

Variability of users => high variability in the costs incurred by AP

Variability increases with the access speed

Increase in video traffic: higher transit payment by AP

04/19/23 84

Baseline model AP and CP connect to the TP as customers N users of AP, charged a flat rate R ($/month) Transit pricing: 95th percentile of traffic volume,

concave transit pricing functions 95th / mean = 2:1 for normal traffic, 4:1 for video1

More video means higher transit payment by AP AP users: Heavy tailed distribution of content

downloaded per month High variability in AP costs

04/19/23 85

1Norton’06: Internet Video: The Next Wave of Massive Disruption to the U.S. Peering Ecosystem

AP Strategies Charging strategies

AP charges “heavy hitters” according to volume downloaded

AP caps heavy hitters AP charges CP (non-

network neutral) Charging strategies are

disruptive AP cannot control

customer departure probability

04/19/23 86

AP Strategies

Charging heavy hitters download amount D,

threshold T, flat rate R c(D) = D*R/T AP’s profit is sensitive to

customer departure prob

Capping heavy hitters and non-neutral charging would not work for the same reason

04/19/23 87

AP Strategies

Connection Strategies AP caches content from CPs AP peers with CPs

Non-disruptive Caching can reduce transit costs of AP

But depends on the amount of content cacheable Selective peering with CPs can improve

profitability Peering cost depends on CP Cost/benefit analysis for each CP

CP with large network: low cost of peering

04/19/23 88

AP Strategies Connection Strategies

AP caches content from CPs AP peers with CPs

Non-disruptive Cost-benefit analysis for

peering Peering cost depends on CP

(easy/medium/hard) r = saving/cost (both

estimated) Peer if r > R

AP controls the factor R

04/19/23 89

AP Strategies

Charging heavy hitters download amount D,

threshold T, flat rate R c(D) = D*R/T AP’s profit is sensitive to

customer departure prob Non-neutral charging

Customer departure prob “How discriminatory is

my AP?” AP’s profit is sensitive to

customer departure prob

04/19/23 90

Why Study Internet Evolution?

“Bottom-up” models (more later) Understand how local actions lead to emerging

properties

Performance of protocols over time “How would BGP perform 10 years from now?”

Clean slate vs. evolutionary design After initial design, both must evolve ! Understanding evolution of the current Internet can help

design

04/19/23 91