A novel network traffic predictor based on multifractal traffic characteristic

In this work, we propose a network traffic predictor based on a novel multifractal network traffic model. This multifractal traffic model extends the notion of the classical fractional Brownian traffic model proposed by Norros [5], replacing the fractional Brownian motion (fBm) process by the multifractional Brownian motion (mBm) process. The network traffic is assumed to be modeled by the extended fractional Brownian traffic model, which is characterized by its Milder exponents. The value of the Holder exponent at a given time indicates the degree of the traffic burstiness at that time. Based on the mBm covariance structure, a mean-square error discrete-time k-step predictor is implemented. Tests carried out using real wide area network traffic traces proved the proposed traffic model and predictor effectiveness.