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Network Traffic Matrix (TM) prediction is defined as the problem ofestimating future network traffic from the previous and achieved networktraffic data. It is widely used in network planning, resource management andnetwork security. Long Short-Term Memory (LSTM) is a specific recurrent neuralnetwork (RNN) architecture that is well-suited to learn from experience toclassify, process and predict time series with time lags of unknown size. LSTMshave been shown to model temporal sequences and their long-range dependenciesmore accurately than conventional RNNs. In this paper, we propose a LSTM RNNframework for predicting short and long term Traffic Matrix (TM) in largenetworks. By validating our framework on real-world data from GEANT network, weshow that our LSTM models converge quickly and give state of the art TMprediction performance for relatively small sized models.

A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction