Many peer-to-peer distributed applications can benefit from accurate predictions of Internet path performance. Existing approaches either 1) achieve high accuracy for sophisticated path properties, but adopt an unscalable centralized approach, or 2) are lightweight and decentralized, but work only for latency prediction. In this paper, we present the design and implementation of iPlane Nano, a library for delivering Internet path information to peer-to-peer applications. iPlane Nano is itself a peer-to-peer application, and scales to a large number of end hosts with little centralized infrastructure and with a low cost of participation. The key enabling idea underlying iPlane Nano is a compact model of Internet routing. Our model can accurately predict end-to-end PoP-level paths, latencies, and loss rates between arbitrary hosts on the Internet, with 70 % of AS paths predicted exactly in our evaluation set. Yet our model can be stored in less than 7MB and updated with approximately 1MB/day. Our evaluation of iPlane Nano shows that it can provide significant performance improvements for large-scale applications. For example, iPlane Nano yields near-optimal download performance for both small and large files in a P2P content delivery system. 1

University of Washington 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.