Estimating linking preferences and behaviors of autonomous systems in the Internet using a discrete choice model

Abstract

The Internet is a system-of-system composed of devices and networking technologies operated by autonomous systems (AS). The linking decisions made by ASes affect the topology, and thereby the performance of the Internet. The objective in this paper is to estimate AS linking preferences and behaviors in the evolution of the AS-level Internet using discrete choice models (DCM). The proposed approach utilizes the observable Internet topology as the input to estimate preferences and behaviors of ASes by considering not only the network metrics but also the geographic and economic aspects, such as AS geographic location, inter-domain traffic, business role and provider-customer relationships. The approach provides a way to quantify the impact of different variables on the linking probabilities. The results show that a) the geographic distance and the number of customers play a significant role in the AS linking behaviors, and b) heterogeneity in the linking preferences of ASes is important for accurate characterization of AS linking behaviors. The uniqueness of the proposed approach is that it provides an interpretation of AS decisions through the lens of utility-maximization principles. The approach can help in developing improved decision-making models for AS-level Internet topology generators.