Paper
24 July 2001 Analyzing the relevant time scales in a network of queues
Antonio Manuel Duarte Nogueira, Rui Jorge Morais Tomaz Valadas
Author Affiliations +
Proceedings Volume 4523, Internet Performance and Control of Network Systems II; (2001) https://doi.org/10.1117/12.434319
Event: ITCom 2001: International Symposium on the Convergence of IT and Communications, 2001, Denver, CO, United States
Abstract
Network traffic processes can exhibit properties of self-similarity and long-range dependence, i.e., correlations over a wide range of time scales. However, as already shown by several authors for the case of a single queue, the second-order behavior at time scales beyond the so-called correlation horizon or critical time scale does not significantly affect network performance. In this work, we extend previous studies to the case of a network with two queuing stages, using discrete event simulation. Results show that the second stage provokes a decrease in the correlation horizon, meaning that the range of time scales that need to be considered for accurate network performance evaluation is lower than predicted by a single stage model. We also used simulation to evaluate the single queue model. In this case, the estimated correlation horizon values are compared with those predicted by a formula derived by Grossglauser and Bolot, which presumes the approximation of the input data by a traffic model that enables to control the autocorrelation function independently of first-order statistics. Results indicate that although the correlation horizon increases linearly with the buffer size in both methods, the simulation ones predict a lower increase rate.
© (2001) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Antonio Manuel Duarte Nogueira and Rui Jorge Morais Tomaz Valadas "Analyzing the relevant time scales in a network of queues", Proc. SPIE 4523, Internet Performance and Control of Network Systems II, (24 July 2001); https://doi.org/10.1117/12.434319
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Cited by 6 scholarly publications.
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KEYWORDS
Networks

Data modeling

Statistical analysis

Wavelets

Performance modeling

Control systems

Statistical modeling

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