Measurement Manipulation and Space Selection in Network Coordinates

Internet coordinate systems have emerged as an efficient method to estimate the latency between pairs of nodes without any communication between them. However, most coordinate systems have been evaluated solely on data sets built by their authors from measurements gathered over large periods of time. Although they show good prediction results, it is unclear whether the accuracy is the result of the system design properties or is more connected to the characteristics of the data sets. In this paper, we revisit a simple question: how do the features of the embedding space and the inherent attributes of the data sets interact in producing good embeddings? We adapt the Vivaldi algorithm to use Hyperbolic space for embedding and evaluate both Euclidean and Hyperbolic Vivaldi on seven sets of real-world latencies. Our results show that node filtering and latency distributions can significantly influence the accuracy of the predictions. For example, although Euclidean Vivaldi performs well on data sets that were chosen, constructed and filtered by the designers of the algorithm, its performance and robustness decrease considerably when run on third party data sets that were not filtered a priori. Our results offer important insight into designing and building coordinate systems that are both robust and accurate in Internet-like environments.