iPlane Nano: Path Prediction for Peer-to-Peer Applications

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.

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