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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 Lodhi 2
Amogh Dhamdhere 1
Nikolaos Laoutaris 3
Constantine Dovrolis 2

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


Georgia Institute of Technology


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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