SELF-SIMILARITY IN HIGH-SPEED PACKET TRAFFIC - ANALYSIS AND MODELING OF ETHERNET TRAFFIC MEASUREMENTS

Traffic modeling of today's communication networks is a prime example of the role statistical inference methods for stochastic processes pla y in such classical areas of applied probability as queueing theory or performance analysis. In practice, however, statistics and applied pr obability have failed to interface. As a result, traffic modeling and performance analysis rely heavily on subjective arguments; hence, deba tes concerning the validity of a proposed model and its predicted perf ormance abound. In this paper, we show how a careful statistical analy sis of large sets of actual traffic measurements can reveal new featur es of network traffic that have gone unnoticed by the literature and, yet, seem to have serious implications for predicted network performan ce. We use hundreds of millions of high-quality traffic measurements f rom an Ethernet local area network to demonstrate that Ethernet traffi c is statistically self-similar and that this property clearly disting uishes between currently used models for packet traffic and our measur ed data. We also indicate how such a unique data set (in terms of size and quality) (i) can be used to illustrate a number of different stat istical inference methods for self-similar processes, (ii) gives rise to new and challenging problems in statistics, statistical computing a nd probabilistic modeling and (iii) opens up new areas of mathematical research in queueing theory and performance analysis of future high-s peed networks.