On the Self-Similar Nature of ATM Network Traffic

Abstract

Modeling multimedia traffic is an important issue in performance analysis and design of communication networks. With introduction of new applications, the characteristics of data traffic changes. In this paper, a measurement study of ATM Network traffic has been carried out and it is shown that the recorded data exhibit self-similar features. The conclusions are supported by a comprehensive analysis using one of the most popular statistical methods called Indices of Dispersion. Our results validate one of the most striking findings of the present teletraffic research: a broad range of packet network traffic has fractal-like behavior. We also investigate three popular synthetic self-similar traffic models and find out the most accurate one for the measured traffic. Keywords: ATM, Modeling, Self-similar traffic, Indices of Dispersion, Hurst parameter, Performance Testing. Introduction The characterization of real traffic is a critical issue to the success of efficient traffic engineering in ATM networks. Research in this field has resulted in numerous models and techniques over the last decade (Bjorkman, Latour-Henner, Hasson, pers, and Miah, 1995; Stamoulis, Anagostu, and Georgantas, 1994). However, the developed traffic characterization methods have been, in general, rather complicated and demand intensive computation of several statistical parameters. There is a lack of simple and accurate methods that can be of practical use to network operators. Extensive data studies indicate that traffic in high-speed communications networks has long-memory and heavy tailed (impulsive) characteristics. With the rising popularity of multimedia applications over networks, these properties of the traffic are only likely to become more dominant, posing unique new challenges to designers of network systems and protocols. Traditional teletraffic theory cannot capture these traffic characteristics. During the last few years, significant research results have been proposed on models that capture self-similarity of traffic. These models, however, are inadequate for predicting queuing performance, delays, and buffer dimensions since the implications of the combination of self-similarity and impulsiveness queuing performance can be dramatically different from that of self-similarity alone. To the best of my knowledge there are no models that have been derived based on real traffic dynamics that also capture the data impulsiveness? The modeling of self-similar traffic appeared as an emerging and challenging field of the present teletraffic research. It seems that there are different promising approaches to capture this complex fractal-like behavior. Norros (1993, 1995) used a Gaussian self-similar process known as the Fractional Brownian Motion. Willinger, Taqqu, Sherman and Wilson (2004) applied the superposition of on/off sources with heavy-tailed on and off period. Erramilli (1994) and Singh and Erramilli (1999) studied different chaotic maps. The rest of this paper is organized as follows. In section 2 we give information about the self-similar traffic characteristics and the used techniques for measuring the self-similarity level (Hurst-parameter H). In section 3 we present the analysis based on the real measurements taken from the Eastern Mediterranean University (EMU) ATM network and find out the self-similarity level. In section 4 we investigate the three promising self-similar modeling approaches to capture the observed properties, and we find out the most appropriate model for EMU ATM network traffic. Finally, section 5 summarizes the results of the paper and identifies areas for future research. The Self-Similarity Phenomena and its Testing A self-similar phenomenon represents a process displaying structural similarities across a wide range of scales of a specific dimension. In other words, the reference structure is repeating itself over a wide range of scales of diverse dimensions (geometrical, or statistical, or temporal). …

Document type: Article