Analysis of prediction performance of training-based models using real network traffic
by Mohamed Faten Zhani, Halima Elbiaze, Farouk Kamoun
International Journal of Computer Applications in Technology (IJCAT), Vol. 37, No. 1, 2010

Abstract: Traffic prediction constitutes a hot research topic of network metrology. This work focuses on the design and the empirical evaluation of the behaviour of training-based models for predicting the input rate of a single link. Via experimentation on real network traffic, we study the effect of some parameters on the prediction performance such as the amount of data needed to identify the model, the inputs of the model, the data granularity, and packet size distribution. Experiments show that the models provide accurate prediction using one lag. We also show that count of large packets is sufficient to predict the throughput.

Online publication date: Thu, 17-Dec-2009

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