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. Leveraging the acquired PoP locations, we examine the implications of geo-footprint of eyeball ASes on their connectivity to the rest of the Internet. 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. 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. We use the resulting geo-footprint of individual eyeball ASes to identify their likely Point-of-Presence (PoP) locations. 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 leverage the kernel density estimation method to estimate the density of users across individual eyeball ASes. The key idea is to leverage the geo-location of end-users associated with an eyeball AS to identify its geographical footprint. 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.
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