Network traffic modeling using connection-level information

Aggregate network traffic exhibits strong burstiness and non-Gaussian distributions, which popular models such as fractional Gaussian noise (fGn) fail to capture. To better understand the cause of traffic burstiness, we investigate the connection-level information of traffic traces. A careful study reveals that traffic burstiness is directly related to the heterogeneity in connection bandwidths and round-trip times and that a small number of high-bandwidth connections are solely responsible for bursts. This separation of connections has far-reaching implications on network control and leads to a new model for network traffic which we call the alpha/beta model. In this model, the network traffic is composed of two components: a bursty, non-Gaussian alpha component (stable Lévy noise) and a Gaussian, long range dependent beta component (fGn). We present a fast scheme to separate the alpha and beta components of traffic using wavelet denoising.