Toward an Improved Understanding of Network Traffic Dynamics
Riedi, Rudolf H.
Self-similar; multifractal; network traffic
Since the discovery of long range dependence in Ethernet LAN traces there has been significant progress in developing appropriate mathematical and statistical techniques that provide a physical-based, networking-related understanding of the observed fractal-like or self-similar scaling behavior of measured data traffic over time scales ranging from hundreds of milliseconds to seconds and beyond. These developments have helped immensely in demystifying fractal-based traffic modeling and have given rise to new insights and physical understanding of the effects of large-time scaling properties in measured network traffic on the design, management and performance of high-speed networks. However, to provide a complete description of data network traffic, the same kind of understanding is necessary with respect to the dynamic nature of traffic over small time scales, from a few hundreds of milliseconds downwards. Because of the predominant protocols and end-to-end congestion control mechanisms that determine the flow of packets, studying the fine-time scale behavior or local characteristics of data traffic is intimately related to understanding the complex interactions that exist in data networks. In this chapter, we first summarize the results that provide a unifying and consistent picture of the large-time scaling behavior of data traffic. We then report on recent progress in studying the small-time scaling behavior in data network traffic and outline a number of challenging open problems that stand in the way of providing an understanding of the local traffic characteristics that is as plausible, intuitive, appealing and relevant as the one that has been found for the global or large-time scaling properties of data traffic.