We present Topology-based Geolocation (TBG), a novel approach to estimating the geographic location of arbitrary Internet hosts. We motivate our work by showing that 1) existing approaches, based on end-to-end delay measurements from a set of landmarks, fail to outperform much simpler techniques, and 2) the error of these approaches is strongly determined by the distance to the nearest landmark, even when triangulation is used to combine estimates from different landmarks. Our approach improves on these earlier techniques by leveraging network topology, along with measurements of network delay, to constrain host position. We convert topology and delay data into a set of constraints, then solve for router and host locations simultaneously. This approach improves the consistency of location estimates, reducing the error substantially for structured networks in our experiments on Abilene and Sprint. For networks with insufficient structural constraints, our techniques integrate external hints that are validated using measurements before being trusted. Together, these techniques lower the median estimation error for our university-based dataset to 67 km vs. 228 km for the best previous approach.

Modern content-distribution networks both provide bulk content and act as "serving infrastructure" for web services in order to reduce user-perceived latency. Serving infrastructures such as Google's are now critical to the online economy, making it imperative to understand their size, geographic distribution, and growth strategies. To this end, we develop techniques that enumerate IP addresses of servers in these infrastructures, find their geographic location, and identify the association between clients and clusters of servers. While general techniques for server enumeration and geolocation can exhibit large error, our techniques exploit the design and mechanisms of serving infrastructure to improve accuracy. We use the EDNS-client-subnet DNS extension to measure which clients a service maps to which of its serving sites. We devise a novel technique that uses this mapping to geolocate servers by combining noisy information about client locations with speed-of-light constraints. We demonstrate that this technique substantially improves geolocation accuracy relative to existing approaches. We also cluster server IP addresses into physical sites by measuring RTTs and adapting the cluster thresholds dynamically. Google's serving infrastructure has grown dramatically in the ten months, and we use our methods to chart its growth and understand its content serving strategy. We find that the number of Google serving sites has increased more than sevenfold, and most of the growth has occurred by placing servers in large and small ISPs across the world, not by expanding Google's backbone.

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Abstract. In the cellular environment, operators, researchers and end users have poor visibility into network performance for devices. Improving visibility is challenging because this performance depends factors that include carrier, access technology, signal strength, geographic location and time. Addressing this requires longitudinal, continuous and large-scale measurements from a diverse set of mobile devices and networks.This paper takes a first look at cellular network performance from this perspective, using 17 months of data collected from devices located throughout the world. We show that (i) there is significant variance in key performance metrics both within and across carriers; (ii) this variance is at best only partially explained by regional and time-of-day patterns; (iii) the stability of network performance varies substantially among carriers. Further, we use the dataset to diagnose the causes behind observed performance problems and identify additional measurements that will improve our ability to reason about mobile network behavior.

The Internet suffers from well-known performance, reliability, and security problems. However, proposed improvements have seen little adoption due to the difficulties of Internet-wide deployment. We observe that, instead of trying to solve these problems in the general case, it may be possible to make substantial progress by focusing on solutions tailored to the paths between popular content providers and their clients, which carry a large share of Internet traffic. In this paper, we identify one property of these paths that may provide a foothold for deployable solutions: they are often very short. Our measurements show that Google connects directly to networks hosting more than 60% of end-user prefixes, and that other large content providers have similar connectivity. These direct paths open the possibility of solutions that sidestep the headache of Internetwide deployability, and we sketch approaches one might take to improve performance and security in this setting.

Operators and researchers want accurate router-level views of the Internet for purposes including troubleshooting and modeling. However, tools such as traceroute return IP addresses. Because routers may have dozens of IP addresses, or aliases, multiple measurements may return different addresses, obscuring whether they represent the same machine. While many techniques exist to address this issue by identifying some IP aliases, these techniques, even in combination, find only a subset of alias pairs.To improve this state, we design and evaluate a new alias resolution technique using the IP prespecified timestamp option. This option allows a sender to request timestamp values from multiple IP addresses in the same probe. By careful arrangement of these IP addresses, we show that we can infer aliases in many cases.In this paper, we conduct a measurement study of how many routers support IP timestamps, demonstrating that enough honor the option to base our technique on it. Using our technique, and compared to the most accurate alias information available, we find that 94.7% of the aliases identified by our technique are true positives. Further, we show that our IP timestamp-based technique complements existing alias resolution techniques, providing significant gains by discovering previously unidentifiable aliases.

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