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

Abdelhadi Azzouni 1 Guy Pujolle 1
1 Phare
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Network Traffic Matrix (TM) prediction is defined as the problem of estimating future network traffic from the previous and achieved network traffic data. It is widely used in network planning, resource management and network security. Long Short-Term Memory (LSTM) is a specific recurrent neural network (RNN) architecture that is well-suited to learn from experience to classify, process and predict time series with time lags of unknown size. LSTMs have been shown to model temporal sequences and their long-range dependencies more accurately than conventional RNNs. In this paper, we propose a LSTM RNN framework for predicting Traffic Matrix (TM) in large networks. By validating our framework on real-world data from G ´ EANT network, we show that our LSTM models converge quickly and give state of the art TM prediction performance for relatively small sized models.
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https://hal.sorbonne-universite.fr/hal-01538471
Contributor : Abdelhadi Azzouni <>
Submitted on : Tuesday, June 13, 2017 - 4:09:43 PM
Last modification on : Friday, January 8, 2021 - 5:42:03 PM
Long-term archiving on: : Tuesday, December 12, 2017 - 5:25:34 PM

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  • HAL Id : hal-01538471, version 1
  • ARXIV : 1705.05690

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Abdelhadi Azzouni, Guy Pujolle. A Long Short-Term Memory Recurrent Neural Network Framework for Network Traffic Matrix Prediction. 2017. ⟨hal-01538471⟩

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