Estimation of Flow Length Distributions Using Bayesian Nonnegative Tensor Factorization (Journal Publication)

By | December 1, 2019

We published our work for recovering true network flow distributions from sampled observations using Bayesian nonnegative tensor factorization on Wireless Communications and Mobile Computing. You can reach the article here.


In this paper, we develop a framework to estimate network flow length distributions in terms of the number of packets. We model the network flow length data as a three-way array with day-of-week, hour-of-day, and flow length as entities where we observe a count. In a high-speed network, only a sampled version of such an array can be observed and reconstructing the true flow statistics from fewer observations becomes a computational problem. We formulate the sampling process as matrix multiplication so that any sampling method can be used in our framework as long as its sampling probabilities are written in matrix form. We demonstrate our framework on a high-volume real-world data set collected from a mobile network provider with a random packet sampling and a flow-based packet sampling methods. We show that modeling the network data as a tensor improves estimations of the true flow length histogram in both sampling methods.

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