With an increasing popularity of real-time applications, such as live chat and gaming, latency prediction between personal devices including mobile devices becomes an important problem. Traditional approaches recover all-pair latencies in a network from sampled measurements using either Euclidean embedding or matrix factorization. However, these approaches targeting static or mean network latency prediction are insufficient to predict personal device latencies, due to unstable and time-varying network conditions, triangle inequality violation and unknown rank of latency matrices. In this paper, by analyzing latency measurements from the Seattle platform, we propose new methods for both static latency estimation as well as the dynamic estimation problem given 3D latency matrices sampled over time. We propose a distance-feature decomposition algorithm that can decompose latency matrices into a distance component and a network feature component, and further leverage the structured pattern inherent in the 3D sampled data to increase estimation accuracy. Extensive evaluations driven by real-world traces show that our proposed approaches significantly outperform various state-of-the-art latency prediction techniques.
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