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Supervising internet traffic is essential for any Internet Service Provider(ISP) to dynamically allocate bandwidth in an optimized manner. BitTorrent is awell-known peer-to-peer file-sharing protocol for bulky file transfer. Itsextensive bandwidth consumption affects the Quality of Service (QoS) and causeslatency to other applications. There is a strong requirement to predict theBitTorrent traffic to improve the QoS. In this paper, we propose a Kalmanfilter (KF) based method to predict the network traffic for various trafficdata sets. The observed performance of KF is superior in terms of both MeanSquared Error (MSE) and total computation time, when compared to AutoRegressive Moving Average (ARMA) model.

Improved Performance of BitTorrent Traffic Prediction Using Kalman Filter