TSMF: Network Latency Estimation using Matrix Factorization and Time Series Forecasting

The ability to accurately estimate end-to-end network latencies is extremely important for many services, from overlay network formation to Edge computing and 5G. Research in Network Coordinate Systems (NCS) has over the years focused on providing such estimates while conserving network resources by avoiding excessive probing. However, Internet latencies are inherently unstable and estimates produced by existing NCS's are shown to quickly become obsolete. In this paper, we devise TSMF, a novel NCS method based on an ensemble of Time-Series Forecasting and Matrix Factorization (MF). Fusing the two approaches results in a model that takes advantage of the low-rank structure of end-to-end latencies and temporal correlations with past measurements. In addition, TSMF can forecast future end-to-end latencies which has been impossible using existing NCS approaches. Our results demonstrate that TSMF outperforms Euclidean and MF-based NCS's with up to 6× less relative error in predicting end-to-end latencies. We also demonstrate the accuracy of TSMF in forecasting future end-to-end latencies, and its consequent suitability for services such as web-service recommendation.