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.

This paper presents Respondent-Driven Sampling (RDS) as a promising technique to derive unbiased estimates of node properties in unstructured overlay networks such as Gnutella. Using RDS and a previously proposed technique, namely Metropolized Random Walk (MRW) sampling, we examine the efficiency of estimating node properties in unstructured overlays and identify some of the key factors that determine the accuracy of sampling techniques. We evaluate the RDS and MRW techniques using simulation over a wide range of static and dynamic graphs as well as experiments over a widely deployed Gnutella network. Our study sheds light on how the connectivity structure among nodes and its dynamics affect the accuracy and efficiency of the two sampling techniques. Both techniques exhibit a rather similar performance over a wide range of scenarios. However, RDS significantly outperforms MRW when the overlay structure exhibits a combination of highly skewed node degrees and highly skewed (local) clustering coefficients.

During the past few years, Peer-to-Peer (P2P) applications have become increasingly popular over the Internet. In these applications, a large number (e.g., millions) of geographically distributed end-systems (or peers) form an overlay to exchange content and share their resources. Several recent studies have observed the network traffic at one or few vantage points and shown that P2P applications significantly contribute to the overall Internet traffic(e.g., [2]). Given the distributed nature of P2P applications, the observed traffic associated with a P2P application across different regions (i.e., ASes) in the Internet depends on several factors including (i) the location of participating peers, (ii) the connectivity among the peers, and (iii) the BGP routing of traffic among ASes in the underlay. Therefore, the observed traffic for a P2P application at a single (or few) vantage point(s) clearly does not represent the extent of its impact across the network. Capturing an accurate view of the global impact of a P2P application requires access to a large number of vantage points that is prohibitively expensive. To our knowledge, none of the previous empirical studies on P2P systems have characterized the global impact of a P2P application on the AS-level underlay.

The observed performance by individual peers in BitTorrent can be simply measured by their average download rate. While it is often stated that the observed peer-level performance by BitTorrent clients is high, it is difficult to accurately verify this claim due to the large scale, distributed and dynamic nature of this P2P system. To provide a "representative" characterization of peer-level performance in BitTorrent, the following two important questions should be addressed: (i) What is the distribution of observed performance among participating peers in a torrent? (ii) What are the primary peer-or group-level properties that determine observed performance by individual peers? In this paper, we conduct a measurement study to tackle these two questions. Toward this end, we derive observed performance for nearly all participating peers along with their main peer-and (peer-view of) group-level properties in three different torrents. Our results show that the probability of experiencing certain level of performance has a roughly uniform distribution across the entire range of observed values. Furthermore, while the performance of each peer has the highest correlation with its outgoing bandwidth, there is no dominant peer-and group-level property that primarily determines the observed performance by the majority of peers.

Peer-to-Peer (P2P) file sharing applications have witnessed a dramatic increase in popularity during the past few years. To accommodate the rapid growth in user population, developers introduced new features in their client software, in particular a two-tier overlay topology. The effect of the two- tier overlay topology in a widely-deployed P2P system primarily depends on the availability and coherency of its implementations among participating clients throughout the system. This paper sheds some light on the long-term evolution of such a two-tier overlay topology in the Gnutella network during a 15-month period over which the system quadrupled in size, exceeding three million concurrent peers. Our results show two interesting phenomena including: (i) During this period, the two- tier overlay has repeatedly begun to lose its balance. However, proper modifications in major client software coupled with the rapid upgrade rate of users, has enabled the developers to maintain the overlay's desired properties, (ii) Despite its random connectivity, the Gnutella overlay exhibits a strong bias towards intra-continent connectivity, especially in continents with smaller user populations, that has not changed as the system scaled.