1. Employing Agent-based Models to study Interdomain Network Formation, Dynamics & Economics Aemen Lodhi (Georgia Tech) 1 Workshop on Internet Topology & Economics (WITE’12)

2. Outline Agent-based modeling for AS-level Internet Our model: GENESIS Application of GENESIS – Large-scale adoption of Open peering strategy Conclusion 2

3. What is the environment that we are we trying to model? Autonomous System level Internet Economic network 3 Enterprise customer Transit Provider Internet Enterprise customer Content Provider

4. 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 4

5. 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? 5

6. Different approaches Analytical / Game-theoretic approach Empirical studies Generative models e.g., Preferential attachment Distributed optimization Agent-based modeling 6

7. 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” 7

8. 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 8

9. GENESIS 9

10. The model: GENESIS*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 10 *Aemen Lodhi, Amogh Dhamdhere, Constantine Dovrolis, “GENESIS: An agent-based model of interdomain network formation, traffic flow and economics,” InfoCom 2012

11. Geographic presence & constraints 11 Link formation across geography not possible Regions corresponding to unique IXPs Peering link at top tier possible across regions Geographic overlap

12. The model: GENESIS*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

13. Peering strategies 13

14. Peering strategy adoption No coordination, limited foresight Eventual fitness can be different Stubs always use Open peering strategy 14 Time 123 Depeering Peering Transit Provider selection OpenSelective Open

15. Application of GENESIS: Analysis of peering strategy adoption by transit providers in the Internet* 15 *Aemen Lodhi, Amogh Dhamdhere, Constantine Dovrolis, “Analysis of peering strategy adoption by transit providers in the Internet,” NetEcon 2012

16. Motivation: Existing peering environment Increasing fraction of interdomain traffic flows over peering links* How are transit providers responding? 16 Transit Provider Content Provider/CDN Access ISP/Eyeballs *C. Labovitz, S. Iekel Johnson, D. McPherson, J. Oberheide and F. Jahanian, “Internet Interdomain Traffic,” in ACM SIGCOMM, 2010

17. Motivation: Existing peering environment Peering strategies of ASes in the Internet (source: PeeringDB www.peeringdb.com)www.peeringdb.com Transit Providers peering openly ? 17

18. Approach Agent based computational modeling Corroboration by PeeringDB data Scenarios *Stubs always use Open Without-open Selective Restrictive With-open Selective Restrictive Open vs.

19. Strategy adoption by transit providers 19

20. Collective impact of Open peering on fitness of transit providers Cumulative fitness reduced in all simulations 20

21. Impact on fitness of individual transit providers switching from Selective to Open 70% providers have their fitness reduced 21

22. Why do transit providers adopt Open peering? xy zw v Save transit costs But your customers are doing the same! Affects: Transit Cost Transit Revenue Peering Cost

23. Why gravitate towards Open peering? xy zw z w, z y, traffic bypasses x x lost transit revenue Options for x? x regains lost transit revenue partially Y peering openly x adopts Open peering Not isolated decisions Network effects !! z w, traffic passes through x again!

24. Employ agent-based models for large- scale study of interdomain network formation Parameterization and validation are difficult Agent-based models can reveal surprising behavior 24 Conclusion

25. 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 25 Conclusion

26. Thank you 26