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Index Terms
- On the self-similar nature of Ethernet traffic (extended version)
Recommendations
On the self-similar nature of Ethernet traffic
SIGCOMM '93: Conference proceedings on Communications architectures, protocols and applicationsWe demonstrate that Ethernet local area network (LAN) traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal behavior, and that such behavior has serious implications for the design, control, ...
On the self-similar nature of Ethernet traffic
We demonstrate that Ethernet local area network (LAN) traffic is statistically self-similar, that none of the commonly used traffic models is able to capture this fractal behavior, and that such behavior has serious implications for the design, control, ...
Reviews
Valentin Cristea
Starting from the analysis of traffic data, accurately collected on an Ethernet network, the authors discovered a characteristic nobody expected: they demonstrate that Ethernet LAN traffic is statistically self-similar. None of the commonly used traffic models is able to capture this behavior, which has great implications for the design, control, and analysis of high-speed, cell-based networks. The paper contains a description of the available traffic measurements; a theoretical presentation of self-similarity; the statistical analysis of the collected data for self-similarity; and a discussion on the significance of self-similarity for traffic engineering of B-ISDN (broadband ISDN) environments. All the sections of the paper contribute to the demonstration of the traffic self-similarity characteristic. Details on the network environment at Bellcore are given. References to papers describing self-similarity are included. The statistical analysis is presented in detail. The impact of the self-similar nature of traffic on the engineering of high-speed networks is also highlighted. Network engineers will find this paper interesting and useful.
Wai Sum Lai
High-speed integrated-services networks require a high level of performance to support the fluctuating and heterogeneous demands from different users. The ability to predict performance accurately is crucial for the design and development of such networks. To achieve this goal, one major need is to model various traffic sources accurately so that their salient features can be reflected in the performance analysis and evaluation. Focusing primarily on one type of traffic—packet data from Ethernet LANs—the authors propose the use of a self-similar stochastic model for the modeling of aggregate data traffic sources. Based on the statistical analysis of a series of extensive measurements of Ethernet traffic spanning a four-year period (1989–92), they show that such a model can capture the bursty characteristics of this type of traffic more accurately than the existing Poisson-related models. This work is an important step toward a better understanding of the bursty nature of different traffic sources. Hopefully, it will be the starting point in the development of a new set of queueing models and tools for the performance analysis of broadband integrated networks.
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