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Center for Applied Internet Data Analysis > publications : papers : 2015 : complexities_internet_peering
Complexities in Internet Peering: Understanding the "Black" in the "Black Art"
A. Lodhi, A. Dhamdhere, N. Laoutaris, and C. Dovrolis, "Complexities in Internet Peering: Understanding the "Black" in the "Black Art"", in IEEE Conference on Computer Communications (INFOCOM), Apr 2015, pp. 1778--1786.
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Complexities in Internet Peering: Understanding the "Black" in the "Black Art"

Aemen Lodhi2
Amogh Dhamdhere1
Nikolaos Laoutaris3
Constantine Dovrolis2

CAIDA, San Diego Supercomputer Center, University of California San Diego


Georgia Institute of Technology (Georgia Tech)


Telefonica Research

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.

Keywords: economics, measurement methodology, policy, topology
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