Peering in the Internet interdomain network has long been considered a “black art”, understood in-depth only by a select few peering experts while the majority of the network operator community only scratches the surface employing conventional rules-of-thumb to form peering links through ad hoc personal interactions. Why is peering considered a black art? What are the main sources of complexity in identifying potential peers, negotiating a stable peering relationship, and utility optimization through peering? How do contemporary operational practices approach these problems? In this work we address these questions for Tier-2 Network Service Providers. We identify and explore three major sources of complexity in peering: (a) inability to predict traffic flows prior to link formation (b) inability to predict economic utility owing to a complex transit and peering pricing structure (c) computational infeasibility of identifying the optimal set of peers because of the network structure. We show that framing optimal peer selection as a formal optimization problem and solving it is rendered infeasible by the nature of these problems. Our results for traffic complexity show that 15% NSPs lose some fraction of customer traffic after peering. Additionally, our results for economic complexity show that 15% NSPs lose utility after peering, approximately, 50% NSPs end up with higher cumulative costs with peering than transit only, and only 10% NSPs get paid-peering customers.

Peering agreements between Autonomous Systems affect not only the flow of interdomain traffic but also the economics of the entire Internet ecosystem. The conventional wisdom is that transit providers are selective in choosing their settlement-free peers because they prefer to offer revenue-generating transit service to others. Surprisingly, however, a large percentage of transit providers use an Open peering strategy. What causes this large-scale adoption of Open peering, especially among transit providers? More importantly, what is the impact of this peering trend on the economic performance of the population of transit providers? We approach these questions through game-theoretic modeling and agent-based simulations, capturing the dynamics of peering strategy adoption, inter-network formation and interdomain traffic flow. We explain why transit providers gravitate towards Open peering even though that move may be detrimental to their economic fitness. Finally, we examine the impact of an Open peering variant that requires some coordination among providers.

We propose an agent-based network formation model for the Internet at the Autonomous System (AS) level. The proposed model, called GENESIS, is based on realistic provider and peering strategies, with ASes acting in a myopic and decentralized manner to optimize a cost-related fitness function. GENESIS captures key factors that affect the network formation dynamics: highly skewed traffic matrix, policy-based routing, geographic co-location constraints, and the costs of transit/peering agreements. As opposed to analytical game-theoretic models, which focus on proving the existence of equilibria, GENESIS is a computational model that simulates the network formation process and allows us to actually compute distinct equilibria (i.e., networks) and to also examine the behavior of sample paths that do not converge. We find that such oscillatory sample paths occur in about 10% of the runs, and they always involve tier- 1 ASes, resembling the tier-1 peering disputes often seen in practice. GENESIS results in many distinct equilibria that are highly sensitive to initial conditions and the order in which ASes (agents) act. This implies that we cannot predict the properties of an individual AS in the Internet. However, certain properties of the global network or of certain classes of ASes are predictable. We also examine whether the underlying game is zero-sum, and identify three sufficient conditions for that property. Finally, we apply GENESIS in a specific “what-if” question, asking how the openness towards peering affects the resulting network in terms of topology, traffic flow and economics. Interestingly, we find that the peering openness that maximizes the fitness of different network classes (tier-1, tier-2 and tier-3 providers) closely matches that seen in real-world peering policies.