Multilayer Perceptron and Stacked Autoencoder for Internet Traffic Prediction

Abstract : Internet traffic prediction is an important task for many applications, such as adaptive applications, congestion control, admission control, anomaly detection and bandwidth allocation. In addition, efficient methods of resource management can be used to gain performance and reduce costs. The popularity of the newest deep learning methods has been increasing in several areas, but there is a lack of studies concerning time series prediction. This paper compares two different artificial neural network approaches for the Internet traffic forecast. One is a Multilayer Perceptron (MLP) and the other is a deep learning Stacked Autoencoder (SAE). It is shown herein how a simpler neural network model, such as the MLP, can work even better than a more complex model, such as the SAE, for Internet traffic prediction.
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Communication dans un congrès
Ching-Hsien Hsu; Xuanhua Shi; Valentina Salapura. 11th IFIP International Conference on Network and Parallel Computing (NPC), Sep 2014, Ilan, Taiwan. Springer, Lecture Notes in Computer Science, LNCS-8707, pp.61-71, 2014, Network and Parallel Computing. 〈10.1007/978-3-662-44917-2_6〉
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Tiago Oliveira, Jamil Barbar, Alexsandro Soares. Multilayer Perceptron and Stacked Autoencoder for Internet Traffic Prediction. Ching-Hsien Hsu; Xuanhua Shi; Valentina Salapura. 11th IFIP International Conference on Network and Parallel Computing (NPC), Sep 2014, Ilan, Taiwan. Springer, Lecture Notes in Computer Science, LNCS-8707, pp.61-71, 2014, Network and Parallel Computing. 〈10.1007/978-3-662-44917-2_6〉. 〈hal-01403065〉

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