Topology aware internet traffic forecasting using neural networks

Authors:
Paulo Cortez;Miguel Rio;Pedro Sousa;Miguel Rocha
Affiliations:
Department of Information Systems/R&D Algoritmi Centre, University of Minho, Guimarãães, Portugal;Department of Electronic and Electrical Engineering, University College London, London, UK;Department of Informatics, University of Minho, Braga, Portugal;Department of Informatics, University of Minho, Braga, Portugal
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

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Abstract

Forecasting Internet traffic is receiving an increasing attention from the computer networks domain. Indeed, by improving this task efficient traffic engineering and anomaly detection tools can be developed, leading to economic gains due to better resource management. This paper presents a Neural Network (NN) approach to predict TCP/IP traffic for all links of a backbone network, using both univariate and multivariate strategies. The former uses only past values of the forecasted link, while the latter is based on the neighbor links of the backbone topology. Several experiments were held by considering real-world data from the UK education and research network. Also, different time scales (e.g. every ten minutes and hourly) were analyzed. Overall, the proposed NN approach outperformed other forecasting methods (e.g. Holt-Winters).