Modeling and Prediction of the Internet End-to-end Delay Using Recurrent Neural Networks

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Executive Summary

This paper focuses on modeling and predicting the Internet end-to-end (e2e) delay multi-step ahead using Recurrent Neural Networks (RNNs). In this work, Round-Trip Time (RTT) is used as the basic metric to forecast the Internet e2e delay. A method for delay prediction model is developed using RNNs, able to model nonlinear systems. By observing the delay between two Internet nodes, RTT data has been collected as a time series during several days. Then this discrete-time series data has been organized into two parts, the first one is used as a training/learning set of the RNN, whereas the rest of data is used for the testing/evaluation of the RNN performance.

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