A unified framework for understanding network traffic using independent wavelet models

Properties of heterogeneous network traffic have been investigated from different aspects, resulting in different understanding. Specifically, one previous work discovers that the variance of network traffic exhibits a linear relationship with respect to the mean. Such a linear relation suggests that the traffic is "Poisson-like", and thus "smooth". On the other hand, prior work has shown that the heterogeneous traffic can be long-range dependent, and is thus bursty. The focus of this work is to investigate these seemingly contradictory issues, and to provide a unified understanding on the burstiness of heterogeneous traffic. In particular, we use a simple statistic, the variance of the traffic, for our investigation. We first study variance-mean relations at a single time scale. We then investigate the behavior of variances at multiple time scales, which determines the temporal correlation structure. Finally, we provide a unified view to include most important understanding of the network traffic.