In this paper, we propose a model for representing and predicting distances in large-scale networks by matrix factorization. The model is useful for network distance sensitive applications, such as content distribution networks, topologyaware overlays, and server selections. Our approach overcomes several limitations of previous coordinates-based mechanisms, which cannot model sub-optimal routing or asymmetric routing policies. We describe two algorithmssingular value decomposition (SVD) and nonnegative matrix factorization (NMF)-for representing a matrix of network distances as the product of two smaller matrices. With such a representation, we build a scalable system-Internet Distance Estimation Service (IDES)-that predicts large numbers of network distances from limited numbers of measurements. Extensive simulations on real-world data sets show that IDES leads to more accurate, efficient and robust predictions of latencies in large-scale networks than previous approaches.
IDES REF assigns an incoming and an outgoing vector to each node by matrix factorization techniques, such as Singular Value Decomposition (SVD) or Non-negative Matrix Factorization(NMF).
Modeling distances in large-scale networks by matrix factorization
IMC '04
Computer Science