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

2017/10/17 by Abdelhadi Azzouni, Guy Pujolle, Azzouni, Abdelhadi +1 · 1 citation
Computer Science · #FOS: Computer and information sciences #Internet Traffic Analysis and Secure E-voting #Network Traffic and Congestion Control #Networking and Internet Architecture (cs.NI) #Software-Defined Networks and 5G

paper · pdf · doi:10.48550/arxiv.1710.06799

openalex publication_date 2017/10/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper presents NeuTM, a framework for network Traffic Matrix (TM)\nprediction based on Long Short-Term Memory Recurrent Neural Networks (LSTM\nRNNs). TM prediction is defined as the problem of estimating future network\ntraffic matrix from the previous and achieved network traffic data. It is\nwidely used in network planning, resource management and network security. Long\nShort-Term Memory (LSTM) is a specific recurrent neural network (RNN)\narchitecture that is well-suited to learn from data and classify or predict\ntime series with time lags of unknown size. LSTMs have been shown to model\nlong-range dependencies more accurately than conventional RNNs. NeuTM is a LSTM\nRNN-based framework for predicting TM in large networks. By validating our\nframework on real-world data from GEEANT network, we show that our model\nconverges quickly and gives state of the art TM prediction performance.\n

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• Improving Internet Traffic Matrix Prediction via Time Series Clustering

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