Traffic models in broadband networks

Traffic models are at the heart of any performance evaluation of telecommunications networks. An accurate estimation of network performance is critical for the success of broadband networks. Such networks need to guarantee an acceptable quality of service (QoS) level to the users. Therefore, traffic models need to be accurate and able to capture the statistical characteristics of the actual traffic. We survey and examine traffic models that are currently used in the literature. Traditional short-range and non-traditional long-range dependent traffic models are presented. The number of parameters needed, parameter estimation, analytical tractability, and ability of traffic models to capture marginal distribution and auto-correlation structure of the actual traffic are discussed.

[1]  Predrag R. Jelenkovic,et al.  Automated TES modeling of compressed video , 1995, Proceedings of INFOCOM'95.

[2]  T. V. Lakshman,et al.  Statistical Analysis and Simulation Study of Video Teleconference Traffic in , 1992 .

[3]  Sang Bae Lee,et al.  Modeling and call admission control algorithm of variable bit rate video in ATM networks , 1994, IEEE J. Sel. Areas Commun..

[4]  B. Melamed,et al.  Traffic modeling for telecommunications networks , 1994, IEEE Communications Magazine.

[5]  John Cosmas,et al.  Characterization of Video Codecs as Autoregressive Moving Average Processes and Related Queueing System Performance , 1991, IEEE J. Sel. Areas Commun..

[6]  Walter Willinger,et al.  Self-similarity through high-variability: statistical analysis of Ethernet LAN traffic at the source level , 1997, TNET.

[7]  Nicolas D. Georganas,et al.  Analysis of an ATM buffer with self-similar ("fractal") input traffic , 1995, Proceedings of INFOCOM'95.

[8]  Gunnar Karlsson,et al.  Performance models of statistical multiplexing in packet video communications , 1988, IEEE Trans. Commun..

[9]  B. Melamed,et al.  The transition and autocorrelation structure of tes processes: Part II: Special Cases , 1992 .

[10]  David M. Lucantoni,et al.  A Markov Modulated Characterization of Packetized Voice and Data Traffic and Related Statistical Multiplexer Performance , 1986, IEEE J. Sel. Areas Commun..

[11]  Samuel Karlin,et al.  A First Course on Stochastic Processes , 1968 .

[12]  J. Hosking Modeling persistence in hydrological time series using fractional differencing , 1984 .

[13]  David M. Cohen,et al.  Performance modeling of video teleconferencing in ATM networks , 1993, IEEE Trans. Circuits Syst. Video Technol..

[14]  Walter Willinger,et al.  Long-range dependence in variable-bit-rate video traffic , 1995, IEEE Trans. Commun..

[15]  B. Melamed,et al.  The transition and autocorrelation structure of tes processes , 1992 .

[16]  B. Mandelbrot Long-Run Linearity, Locally Gaussian Process, H-Spectra and Infinite Variances , 1969 .

[17]  John G. Proakis,et al.  Probability, random variables and stochastic processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[18]  Walter Willinger,et al.  On the self-similar nature of Ethernet traffic , 1993, SIGCOMM '93.

[19]  Marcel F. Neuts,et al.  Methods for performance evaluation of VBR video traffic models , 1994, TNET.

[20]  Amarnath Mukherjee,et al.  On Long-Range Dependence in NSFNET Traffic , 1994 .

[21]  S. Wittevrongel,et al.  Queueing Systems , 2019, Introduction to Stochastic Processes and Simulation.

[22]  Benjamin Melamed,et al.  TES: A Class of Methods for Generating Autocorrelated Uniform Variates , 1991, INFORMS J. Comput..

[23]  Michael Devetsikiotis,et al.  Modeling and simulation of self-similar variable bit rate compressed video: a unified approach , 1995, SIGCOMM '95.

[24]  J. R. Wallis,et al.  Computer Experiments with Fractional Gaussian Noises: Part 2, Rescaled Ranges and Spectra , 1969 .

[25]  T. V. Lakshman,et al.  Statistical analysis and simulation study of video teleconference traffic in ATM networks , 1992, IEEE Trans. Circuits Syst. Video Technol..

[26]  Ilkka Norros,et al.  On the Use of Fractional Brownian Motion in the Theory of Connectionless Networks , 1995, IEEE J. Sel. Areas Commun..

[27]  J. R. Wallis,et al.  Computer Experiments With Fractional Gaussian Noises: Part 1, Averages and Variances , 1969 .

[28]  M. Taqqu,et al.  Using Renewal Processes to Generate Long-Range Dependence and High Variability , 1986 .

[29]  Joseph Yu Hui,et al.  Switching and Traffic Theory for Integrated Broadband Networks , 1990 .

[30]  Walter Willinger,et al.  Self-similarity through high-variability: statistical analysis of Ethernet LAN traffic at the source level , 1997, TNET.

[31]  Monson H. Hayes,et al.  Statistical Digital Signal Processing and Modeling , 1996 .

[32]  S.M. Kogon,et al.  Efficient generation of long-memory signals using lattice structures , 1995, Conference Record of The Twenty-Ninth Asilomar Conference on Signals, Systems and Computers.

[33]  Jonathan D. Cryer,et al.  Time Series Analysis , 1986 .

[34]  Jan Beran,et al.  Statistics for long-memory processes , 1994 .

[35]  J. R. Wallis,et al.  Some long‐run properties of geophysical records , 1969 .

[36]  T. V. Lakshman,et al.  Modeling teleconference traffic from VBR video coders , 1994, Proceedings of ICC/SUPERCOMM'94 - 1994 International Conference on Communications.

[37]  J. Kingman A FIRST COURSE IN STOCHASTIC PROCESSES , 1967 .

[38]  Basil S. Maglaris,et al.  Models for packet switching of variable-bit-rate video sources , 1989, IEEE J. Sel. Areas Commun..

[39]  D. Applebaum Stable non-Gaussian random processes , 1995, The Mathematical Gazette.

[40]  K. Knight Stable Non-Gaussian Random Processes Gennady Samorodnitsky and Murad S. Taqqu Chapman and Hall, 1994 , 1997, Econometric Theory.

[41]  Amarnath Mukherjee,et al.  On resource management and QoS guarantees for long range dependent traffic , 1995, Proceedings of INFOCOM'95.