Optimal Transit Price Negotiation: The Distributed Learning Perspective

We present a distributed learning algorithm for optimizing transit prices in the inter-domain routing framework. We present a combined game theoretical and distributed algorithmic analysis, where the notion of Nash equilibrium with the first approach meets the notion of stability in the second. We show that providers can learn how to strategically set their prices according to a Nash equilibrium; even when assuming incomplete information. We validate our theoretical model by simulations confirming the expected outcome. Moreover, we observe that some unilateral deviations from the proposed rule do not seem to affect the dynamic of the system.