An Internet Protocol Address Clustering Algorithm

We pose partitioning a b-bit Internet Protocol (IP) address space as a supervised learning task. Given (IP, property) labeled training data, we develop an IP-specific clustering algorithm that provides accurate predictions for unknown addresses in O(b) run time. Our method offers a natural means to penalize model complexity, limit memory consumption, and is amenable to a non-stationary environment. Against a live Internet latency data set, the algorithm outperforms IP-na?ive learning methods and is fast in practice. Finally, we show the model's ability to detect structural and temporal changes, a crucial step in learning amid Internet dynamics.

• Type: Conference
• Year: 2008
• Where: OSDI
• Authors: Robert Beverly, Karen R. Sollins
• Keywords: Internet Latency Data, IP-specific Clustering Algorithm, Operating System, OSDI 2008, Supervised Learning Task