Fractional Levy motion with dependent increments and its application to network traffic modeling

Since the beginning of the 1990s, accurate traffic measurements carried out in different network scenarios highlighted that Internet traffic exhibits strong irregularities (burstiness) both in terms of extreme variability and long-term correlations. These features, which cannot be captured in a parsimonious way by traditional Markovian models, have a deep impact on the network performance and lead to the introduction of $\alpha$-stable distribution and self-similar processes into the network traffic modeling. In this paper, a generalization of fractional Brownian motion (fBm), which is able to capture both above-mentioned features of the real traffic, is considered.