Internet Traffic Prediction Using Artificial Neural Network

Abstract

In this paper we employed neural network on real network traffic measurements to predict Internet traffic. The traffic data consists of hourly traces from freestats.com collected over periods of 7 and 82 days respectively. First, self-similarity tests were carried out on the data series to ascertain its self-similar nature. The data were aggregated at various frequencies to smooth out burstiness. A feed-forward multi-layer perceptions (MLP) neural network was constructed and trained to predict the traffic. The results show that, using neural network, a reliable prediction can be made on packet data despite its self-similar profile. The results also showed that a neural network trained on a self-similar series can reliably predict the aggregated forms of the series. It was concluded that the results obtained can be used to moderate congestion using limited network resources and sharing the bandwidth allocation. Keywords: Self-similarity, neural networks, Hurst parameter, Internet traffic, aggregation, long range dependence (LRD).