Eyeball ASes: from geography to connectivity

This paper presents a new approach to determine the geographical footprint of individual Autonomous Systems that directly provide service to end-users, i.e., eyeball ASes. The key idea is to leverage the geo-location of end-users associated with an eyeball AS to identify its geographical footprint. We leverage the kernel density estimation method to estimate the density of users across individual eyeball ASes. This method enables us to cope with the potential error associated with the location of individual end-users while controlling the level of aggregation among data points to capture a geo-footprint at the desired resolution. We use the resulting geo-footprint of individual eyeball ASes to identify their likely Point-of-Presence (PoP) locations. To demonstrate our proposed technique, we use the inferred geo-locations of 48 million users from three popular P2P applications and assess the geo- and PoP-level footprints of 1233 eyeball ASes. The validation of the identified PoP locations by our technique against online information and prior results by a commonly-used technique based on traceroute shows a very high accuracy. Leveraging the acquired PoP locations, we examine the implications of geo-footprint of eyeball ASes on their connectivity to the rest of the Internet. In particular, we present a case study that reveals a much more complex picture of AS-level connectivity as compared to what the more traditional but geography-agnostic BGP- or traceroute-based approaches depict.