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GENESIS: An agent-based model of interdomain network formation, traffic flow and economics

Understand dynamics of interdomain networks, pricing schemes, traffic flow, and economic fitness using GENESIS - an agent-based model. Explore peering strategies and network equilibrium. Validate with simulations.

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GENESIS: An agent-based model of interdomain network formation, traffic flow and economics

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  1. GENESIS: An agent-based model of interdomain network formation, traffic flow and economics AemenLodhi(Georgia Tech) AmoghDhamdhere(CAIDA) Constantine Dovrolis(Georgia Tech) 31st Annual IEEE International Conference on Computer Communications (IEEE INFOCOM 2012)

  2. Outline • GENESIS: Introduction & Motivation • The model: Key features • Results • Validation • Analysis of results • Case study • How to use GENESIS in your research

  3. Introduction

  4. 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?

  5. What is GENESIS • Agent based interdomain network formation model • Autonomous Systems (AS) as independent agentsacting in a distributed asynchronous manner Internet Transit Provider Transit Provider Enterprise customer Content Provider Content Provider Enterprise customer

  6. 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

  7. 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

  8. The Model

  9. 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

  10. Model features Fitness = Transit Revenue – Transit Cost – Peering cost • Objective: Maximize economic fitness • Optimize connectivity through peer and transit provider selection

  11. Geographic presence & constraints Geographic overlap Regions corresponding to unique IXPs

  12. Traffic Matrix Intra-domain traffic not captured in the model Traffic sent by AS 0 to other ASes in the network Traffic for ‘N’’ size network represented through an N * N matrix Illustration of traffic matrix for a 4 AS network Traffic received by AS 0 from other ASes in the network

  13. Traffic components Inbound traffic Transit traffic = Inbound traffic – Consumed traffic same as Transit traffic = Outbound traffic – Generated traffic Traffic generated within the AS Traffic transiting through the AS Traffic consumed in the AS Autonomous system Outbound traffic

  14. Peering strategies • Restrictive: Peer only to avoid network partitioning • Selective: Peer with ASes of similar size • Open: Every co-located AS except customers

  15. Peering strategy selection • Default model • Tier 1 Transit providers: Restrictive • All other transit providers: Selective • Stubs: Open

  16. Execution of a sample path • No exogenous changes • Finite states Depeering Peering Transit provider selection Peering strategy update Depeering Peering Transit provider selection Peering strategy update Depeering Peering Transit provider selection Peering strategy update Iteration Iteration 1 2 N 1 2 N Time

  17. results

  18. Stability of the model • Equilibrium: No topology, peering strategy changes in two consecutive iterations • 90% simulations reach equilibrium • Short time scales • Average time to equilibrium: 6 iterations Iteration Iteration 1 2 N 1 2 N Time

  19. Oscillations: An artifact? • 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 Iteration Iteration 1 2 N 1 2 N Time

  20. 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

  21. 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

  22. 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

  23. Variability across equilibria • Most predictable ASes • Stubs: Enterprise customers, Small ISPs • Very large transit providers • Most unpredictable ASes • Midsize (regional) transit providers

  24. 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)

  25. Case study: Peering Openness • Widespread peering: Saving on costs not the only outcome • Results in loss of transit revenue

  26. 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

  27. How can I use GENESIS in my research? • Flexible & Modular Presence at IXPs Presence at IXPs Resulting network Pricing schemes Traffic matrix Peering strategies Peering strategies

  28. 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

  29. Thank YOu

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