Toward an Improved Understanding of Network Traffic Dynamics
Rudolf H. Riedi, Walter Willinger · 2000
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 techniques explain, describe, and validate the reported large-time scaling phenomenon in aggregate network traffic at the packet level in terms of more elementary properties of the traffic patterns generated by the individual users and/or applications. They have impacted our understanding of actual network traffic, to the point where we now know why aggregate data traffic exhibits fractal scaling behavior over time scales from a few hundreds of milliseconds onward. In fact, a measure of the success of this new understanding is that the corresponding mathematical arguments are at the same time rigorous and simple, are in full agreement with the networking researchers' intuition and with measured data, and can be explained readily to a non-networking expert. 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 downward. Because of the predominant protocols and end-to-end congestion control mechanisms that play a central role in modern-day data networks and determine the flow of packets over those fine time scales and at the different layers in the TCP/IP protocol hierarchy, 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 such as the Internet between the different connections, across the different layers in the protocol hierarchy, over time as well as in space. In this chapter, we first summarize the results that provide a unifying and consistent picture of the large-time scaling behavior of data traffic and discuss the appropriateness of self-similar processes such as fractional Gaussian noise for modeling the fluctuations of the traffic rate process around its mean and for providing a complete description of the traffic on individual links within the network. Then we 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.