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Employing Agent-based Models to study Interdomain Network Formation,

Dynamics & Economics

Aemen Lodhi (Georgia Tech)

Workshop on Internet Topology & Economics (WITE’12)

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Outline• Agent-based modeling for AS-level

Internet• Our model: GENESIS• Application of GENESIS– Large-scale adoption of Open peering

strategy• Conclusion

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What is the environment that we are we trying to model?

• Autonomous System level Internet• Economic network

Enterprise customer

Transit Provider

Transit Provider

Internet

Enterprise customer

Content Provider

Content Provider

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What is the environment that we are we trying to model?

• Complex, dynamic environment– Mergers, acquisitions, new entrants, bankruptcies– Changing prices, traffic matrix, geographic

expansion• Co-evolutionary network• Self-organization• Information “fuzziness”• Social aspects: 99% of all peering relationships

are “handshake” agreements*

*”Survey of Characteristics of Internet Carrier Interconnection Agreements 2011” – Packet Clearing House

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What are we asking?• Aggregate behavior– Is the network stable?– Is their gravitation towards a particular

behavior e.g., Open peering– Is their competition in the market?

• Not so academic questions– Is this the right peering strategy for me?–What if I depeer AS X?– Should I establish presence at IXP Y?– CDN: Where should I place my caches?

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Different approaches• Analytical / Game-theoretic approach• Empirical studies• Generative models e.g., Preferential

attachment• Distributed optimization• Agent-based modeling

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Why to use agent-based modeling?

• Incorporation of real-world constraints– Non-uniform traffic matrix– Complex geographic co-location patterns–Multiple dynamic prices per AS– Different peering strategies at different

locations• Scale – hundreds of agents• What-if scenarios• Understanding the “process” and not just

the “end-state”

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Why not to use agent-based modeling?

• Large parameterization space– Systematic investigation of full

parameter space is difficult• Validation• Computational cost• Under some circumstances reasoning

may be difficult e.g. instability in a model with hundreds of agents

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GENESIS

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The model: GENESIS*• Agent based interdomain network formation

model• Fundamental unit: An agent (AS) with

economic interests• Incorporates– Co-location constraints in provider/peer

selection– Traffic matrix– Public & Private peering– Set of peering strategies– Peering costs, Transit costs, Transit revenue

*Aemen Lodhi, Amogh Dhamdhere, Constantine Dovrolis, “GENESIS: An agent-based model of interdomain network formation, traffic flow and economics,” InfoCom 2012

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Geographic presence & constraints

 

     

   

       

Link formation

across geography

not possibleRegions

corresponding to unique

IXPs

Peering link at top tier

possible across regions

Geographic overlap

The model: GENESIS*

Fitness = Transit Revenue – Transit Cost – Peering cost

• Objective: Maximize economic fitness• Optimize connectivity through peer

and transit provider selection• Choose the peering strategy that

maximizes fitness

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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

• Choose peering strategy that is predicted to give maximum fitness

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Peering strategy adoption

• No coordination, limited foresight• Eventual fitness can be different • Stubs always use Open peering strategy

Time1 2 3

Depeering Peering Transit Provider selection

Open Selective Open

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Application of GENESIS:Analysis of peering strategy

adoption by transit providers in the Internet*

*Aemen Lodhi, Amogh Dhamdhere, Constantine Dovrolis, “Analysis of peering strategy adoption by transit providers in the Internet,” NetEcon 2012

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Motivation: Existing peering environment

• Increasing fraction of interdomain traffic flows over peering links*

• How are transit providers responding?Transit

Provider

Content Provider/CD

NAccess

ISP/Eyeballs

*C. Labovitz, S. Iekel Johnson, D. McPherson, J. Oberheide and F. Jahanian, “Internet Interdomain Traffic,” in ACM SIGCOMM, 2010

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Motivation: Existing peering environment

• Peering strategies of ASes in the Internet (source: PeeringDB www.peeringdb.com)• Transit Providers peering openly ?

Approach• Agent based computational modeling• Corroboration by PeeringDB data• Scenarios

*Stubs always use Open

Without-open• Selective • Restrictive

With-open• Selective• Restrictive• Open

vs.

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Strategy adoption by transit providers

Conservative Non-conservative0

102030405060708090

100

RestrictiveSelectiveOpen

Scenarios

Perc

enta

ge o

f tra

nsit

prov

ider

s

Without-open With -open

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Collective impact of Open peering on fitness of transit providers

• Cumulative fitness reduced in all simulations

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Impact on fitness of individual transit providers switching from Selective to Open

• 70% providers have their fitness reduced

Why do transit providers adopt Open peering?

x y

z w

vSave transit

costs

But your customers are

doing the same!

Affects:• Transit Cost

• Transit Revenue• Peering Cost

Why gravitate towards Open peering?

x y

z wz w,z y,traffic bypasses x

x lost transit revenu

e

Options for x?

x regains lost transit

revenue partially

Y peering openly

x adopts Open peering

Not isolated decisionsNetwork effects !!

z w,traffic passes through x again!

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• Employ agent-based models for large-scale study of interdomain network formation

• Parameterization and validation are difficult

• Agent-based models can reveal surprising behavior

Conclusion

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• Gravitation towards Open peering is a network effect for transit providers (70% adopt Open peering)– Economically motivated strategy

selection–Myopic decisions– Lack of coordination

• Extensive Open peering by transit providers in the network results in collective loss

Conclusion

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Thank you