Statistical Self-Similarity in Broadband Traffic: Results and Performance Implications

Abstract

In this paper, we utilize data traffic measurements from the Ethernet LAN at IIT, Delhi to demonstrate the self-similar nature of Broadband network traffic. This ‘scale-invariant’ or self-similar behavior, detected visually as through the use of statistical techniques, is drastically different form both conventional telephone traffic and from stochastic models for packet traffic traditionally considered in the literature. We discuss the far-reaching implications of fractal nature on the design, management and performance of high-bandwidth, cell-based networks. The failure of classical traffic models to capture self-similarity necessitates the development of new mathematical approaches that are simple, accurate and realistic for aggregate data traffic. Issues such as the above, which arise in the light of this new understanding of broadband traffic, form the crux of this paper.

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Notes on contributors

Manoneet Singh

Manoneet Singh received the B Tech Degree in Electrical Engineering from the Indian Institute of Technology, New Delhi, India. Currently he is a graduate student at the Department of Electrical Engineering. Stanford University. Stanford. CA, where he is a Fellow of the School of Engineering. His major research interests are in wireless networks and in communication over unreliable and time varying channels.

Subrat Kar

Subrat Kar graduated with Honours in Electrical & Electronics Engineering from the Birla Institute of Technology & Science, Pilani in 1987. He holds a Doctoral Degree in Electrical Communication Engineering from the Indian Institute of Science. Bangalore (1991). He has been with the International Center for Theoretical Physics. Trieste as a Post-Doctoral Fellow (1991–1994). Presently he is an Associate Professor at the Department of Electrical Engineering. Indian Institute of Technology. Delhi. His research areas are in optical communication, switching, access technologies and high speed networks.