Eyeball ASes: from geography to connectivity

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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 locatio...
Amir H. Rasti, Nazanin Magharei, Reza Rejaie, Walt
Added 13 Feb 2011
Updated 13 Feb 2011
Type Journal
Year 2010
Where IMC
Authors Amir H. Rasti, Nazanin Magharei, Reza Rejaie, Walter Willinger
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