Scalable cooperative latency estimation

This paper discusses SCoLE, a scalable system to estimate Internet latencies. SCoLE is based on GNP, which models Internet latencies in an n-dimensional Euclidean space. In contrast to GNP and other GNP-based systems, however, SCoLE does not employ any global space whose parameters must typically be negotiated by the participating hosts. Instead, it allows each host to construct its "\´private" space and model interhost latencies in that space. The private space parameters as well as the modeling algorithm can be adjusted on a per-host basis, which improves system flexibility. More importantly, the mutual independence of private spaces results in higher SCoLE scalability, which is bound neither by the global negotiation of space parameters nor by global knowledge of any kind. We show that latency estimates performed in different private spaces are highly correlated. This allows SCoLE to be used in large-scale applications where consistent latency estimates need to be performed simultaneously by many independent hosts.