A lightweight network proximity service based on neighborhood models

This paper proposes a network proximity service based on the neighborhood models used in recommender systems. Unlike previous approaches, our service infers network proximity without trying to recover the latency between network nodes. By asking each node to probe a number of landmark nodes which can be servers at Google, Yahoo and Facebook, etc., a simple proximity measure is computed and allows the direct ranking and rating of network nodes by their proximity to a target node. The service is thus lightweight and can be easily deployed in e.g. P2P and CDN applications. Simulations on existing datasets and experiments with a deployment over PlanetLab showed that our service achieves an accurate proximity inference that is comparable to state-of-the-art latency prediction approaches, while being much simpler.