Open AccessTowards traffic matrix prediction with LSTM recurrent neural networks
Author(s) -
Zhao Jianlong,
Qu Hua,
Zhao Jihong,
Jiang Dingchao
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2018.0336
Subject(s) - recurrent neural network , computer science , artificial intelligence , artificial neural network , deep learning , machine learning , data mining , deep neural networks , network architecture , computer network
This Letter investigates traffic matrix (TM) prediction that is widely used in various network management tasks. To fastly and accurately attain timely TM estimation in large‐scale networks, the authors propose a deep architecture based on LSTM recurrent neural networks (RNNs) to model the spatio‐temporal features of network traffic and then propose a novel TM prediction approach based on deep LSTM RNNs and a linear regression model. By training and validating it on real‐world data from Abilene network, the authors show that the proposed TM prediction approach can achieve state‐of‐the‐art TM prediction performance.