Network Latency Estimation for Personal Devices A Matrix Completion Approach
Abstract: Network latency prediction is important for server selection and quality-of-service quality estimation in real-time time applications on the Internet. Traditional network latency prediction schemes attempt to estimate the latencies between all pairs of nodes in a network rk based on sampled round round-trip trip times, through either Euclidean embedding or matrix factorization. However, these schemes become less effective in terms of estimating the latencies of personal devices, due to unstable and time-varying varying network conditions, tr triangle iangle inequality violation and the unknown ranks of latency matrices. In this paper, we propose a matrix completion approach to network latency estimation. Specifically, we propose a new class of low-rank rank matrix completion algorithms, which predicts the m missing issing entries in an extracted “network feature matrix” by iteratively minimizing a weighted SchattenSchatten p norm to approximate the rank. Simulations on true low low-rank rank matrices show that our new algorithm achieves better and more robust performance than multiple multipl state-of-the-art art matrix completion algorithms in the presence of noise. We further enhance latency estimation based on multiple “frames” of latency matrices measured in the past, and extend the proposed matrix completion scheme to the case of 3-D D tensor completion. Extensive performance evaluations driven by real-world world latency measurements collected from the Seattle platform show that our proposed approaches significantly outperform various state-of-thestate