Paulo Cortez***, Miguel Rio**, Pedro Sousa*, Miguel Rocha*
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Universidade do Minho Departamento de Informática* Departamento de Sistemas de Informação*** P-4710-057 Braga, Portugal
Tel.: +351 253 604430 |
UCL (University College London)** Department of Computer Science London WC1E 6BT United Kingdom
Tel: +44 20 7679 7214 |
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).