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GENESIS: An agent-based model of interdomain network formation,

traffic flow and economics

Aemen Lodhi (Georgia Tech)

Amogh Dhamdhere (CAIDA)

Constantine Dovrolis (Georgia Tech)

31st Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2012)

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Outline

• GENESIS: Introduction & Motivation• The model: Key features• Results– Validation– Analysis of results

• Case study• How to use GENESIS in your research

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INTRODUCTION

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Motivations for an interdomain network formation model

• Insight into dynamics of interdomain network

• Study pricing schemes• Study increasing asymmetry in

interdomain traffic matrix• Evaluate peering strategies• Impact of actions on economic

fitness• Internet “ecosystem” in the future?

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What is GENESIS• Agent based interdomain network

formation model• Autonomous Systems (AS) as

independent agents acting in a distributed asynchronous manner

Enterprise

customer

Transit Provide

r

Transit Provide

r

Internet

Enterprise

customer

Content Provider

Content Provider

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What is GENESIS

• Actions by ASes– Transit provider selection– Peering strategy selection– Peering and Depeering decisions

• Outcome of these actions– Formation of an interdomain network

starting from a random initial state–Mostly ending in equilibrium

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What GENESIS is not

• Not a topology generation model• Not a crystal ball to accurately

predict the economic fitness or hierarchical status of a single specific AS in future

• Use GENESIS for – computing statistical properties of

network topology + economic fitness of different categories of ASes

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THE MODEL

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Model features

• Geographic co-location constraints in provider/peer selection

• Traffic matrix• Public & Private peering• Set of peering strategies• Transit provider selection mechanism• Economic attributes: Peering costs,

Transit costs, Transit revenue

Model features

Fitness = Transit Revenue – Transit Cost – Peering cost

• Objective: Maximize economic fitness• Optimize connectivity through peer

and transit provider selection

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Geographic presence & constraints

 

     

   

       

Regions corresponding to unique

IXPs

Geographic overlap

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Traffic Matrix• Traffic for ‘N’’ size network represented

through an N * N matrix• Illustration of traffic matrix for a 4 AS

network

0

0

0

0

30

20

10

030201

t

t

t

ttt

Traffic sent by AS 0 to other ASes in

the network

Traffic received by AS 0 from

other ASes in the network

Intra-domain traffic not

captured in the model

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Traffic components

Autonomous system

Inbound traffic

Traffic consumed in

the ASTraffic

transiting through the AS

Traffic generated

within the AS

Outbound traffic

• Transit traffic = Inbound traffic – Consumed trafficsame as

• Transit traffic = Outbound traffic – Generated traffic

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Peering strategies

• Restrictive: Peer only to avoid network partitioning

• Selective: Peer with ASes of similar size

• Open: Every co-located AS except customers

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Peering strategy selection

• Default model– Tier 1 Transit providers: Restrictive– All other transit providers: Selective– Stubs: Open

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Execution of a sample path

1 2 N

Iteration

1. Depeering2. Peering3. Transit provider

selection4. Peering strategy

update

1. Depeering2. Peering3. Transit provider

selection4. Peering strategy

update

1. Depeering2. Peering3. Transit provider

selection4. Peering strategy

update

1 2 N

Iteration

Time

• No exogenous changes• Finite states

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RESULTS

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Stability of the model

1 2 N

Iteration

1 2 N

Iteration

Time

• Equilibrium: No topology, peering strategy changes in two consecutive iterations

• 90% simulations reach equilibrium• Short time scales• Average time to equilibrium: 6

iterations

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Oscillations: An artifact?

1 2 N

Iteration

1 2 N

Iteration

Time

• 10% simulations oscillate• Always involve Tier-1 ASes• Resemble real Tier-1 peering disputes• GENESIS captures that endogenous

dynamics cannot always produce stable network

• Exogenous intervention required

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Validation

• Comprehensive validation not possible• Should be viewed as proof of concept• 10% ASes end up being transit providers• Average path length 3.7 (500 nodes) vs.

Average Internet measured path length 4• Path length does not increase significantly

as GENESIS scales from 500 to 1000 nodes

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Validation• Highly skewed degree distribution• Not exactly a power law owing to limited

number of nodes• Few links carry several orders of magnitude

more traffic

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Variability across equilibria

• Sources of variation in a single population: Initial topology, Playing order

• Same population but different initial topology: 85% distinct equilibria

• Same population & initial topology but different playing order: 90% distinct equilibria

• Distinct equilibria quite similar in terms of topology

• Coefficient of variation of fitness close to zero for 90% ASes

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Variability across equilibria

• Most predictable ASes– Stubs: Enterprise customers, Small ISPs– Very large transit providers

• Most unpredictable ASes–Midsize (regional) transit providers

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Case study: Peering Openness• How does peering openness affect the

properties of the network?• Optimal fitness in range of peering ratios

observed in the real world (1.5 to 5)

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Case study: Peering Openness

• Widespread peering: Saving on costs not the only outcome

• Results in loss of transit revenue

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Summary of GENESIS findings

• Individual AS status hard to predict• Regional transit providers most

sensitive to network level changes• Overall network characteristics more

predictable• Internet a stable network (mostly) in

the absence of exogenous factors• Increased peering may result in loss of

transit revenue

How can I use GENESIS in my research?

• Flexible & Modular

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Peering strategi

es

Resulting network

Traffic matrix

Pricing schemes

Presence at IXPs

Presence at IXPs

Peering strategi

es

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How can I use GENESIS in my research?

• C++ single thread implementation• Fast: average simulation time for 500 nodes: 1.25

hours• Scales up to 1000 nodes• Used in “Analysis of peering strategy adoption by

transit providers in the Internet” NetEcon 2012• Available at:

www.cc.gatech.edu/~dovrolis/Papers/genesis.zip

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THANK YOU