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This paper presents NeuTM, a framework for network Traffic Matrix (TM)prediction based on Long Short-Term Memory Recurrent Neural Networks (LSTMRNNs). TM prediction is defined as the problem of estimating future networktraffic matrix from the previous and achieved network traffic data. It iswidely used in network planning, resource management and network security. LongShort-Term Memory (LSTM) is a specific recurrent neural network (RNN)architecture that is well-suited to learn from data and classify or predicttime series with time lags of unknown size. LSTMs have been shown to modellong-range dependencies more accurately than conventional RNNs. NeuTM is a LSTMRNN-based framework for predicting TM in large networks. By validating ourframework on real-world data from GEEANT network, we show that our modelconverges quickly and gives state of the art TM prediction performance.

NeuTM: A Neural Network-based Framework for Traffic Matrix Prediction in SDN