Markov-modulated on/off processes for long-range dependent internet traffic

The aim of this paper is to use a very simple queuing model to compare a number of models from the literature which have been used to replicate the statistical nature of internet traffic and, in particular, the long-range dependence of this traffic. The four models all have the form of discrete time Markov-modulated processes (two other models are introduced for comparison purposes). While it is often stated that long-range dependence has a critical effect on queuing performance, it appears that the models used here do not well replicated the queuing performance of real internet traffic. In particular, they fail to replicate the mean queue length (and hence the mean delay) and the probability of the queue length exceeding a given level.

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

[2]  Will E. Leland,et al.  High time-resolution measurement and analysis of LAN traffic: Implications for LAN interconnection , 1991, IEEE INFCOM '91. The conference on Computer Communications. Tenth Annual Joint Comference of the IEEE Computer and Communications Societies Proceedings.

[3]  Richard G. Baraniuk,et al.  A Multifractal Wavelet Model with Application to Network Traffic , 1999, IEEE Trans. Inf. Theory.

[4]  Ramin Sadre,et al.  The pseudo-self-similar traffic model: application and validation , 2004, Perform. Evaluation.

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

[6]  Wang Statistical physics of temporal intermittency. , 1989, Physical review. A, General physics.

[7]  San-qi Li,et al.  Queue response to input correlation functions: discrete spectral analysis , 1992, [Proceedings] IEEE INFOCOM '92: The Conference on Computer Communications.

[8]  Walter Willinger,et al.  On the Self-Similar Nature of Ethernet Traffic ( extended version ) , 1995 .

[9]  Parag Pruthi,et al.  Chaotic Maps As Models of Packet Traffic , 1994 .

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

[11]  Y. Pomeau,et al.  Intermittent transition to turbulence in dissipative dynamical systems , 1980 .

[12]  Zafer Sahinoglu,et al.  On multimedia networks: self-similar traffic and network performance , 1999, IEEE Commun. Mag..

[13]  R. Hilgers,et al.  Parameter , 2019, Springer Reference Medizin.

[14]  San-qi Li,et al.  Queue response to input correlation functions: discrete spectral analysis , 1993, TNET.

[15]  Richard G. Clegg,et al.  The Statistics of Dynamic Networks , 2004 .

[16]  Jean-Yves Le Boudec,et al.  New Models for Pseudo Self-Similar Traffic , 1997, Perform. Evaluation.

[17]  Gennady Samorodnitsky,et al.  Heavy Tails and Long Range Dependence in On/Off Processes and Associated Fluid Models , 1998, Math. Oper. Res..

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

[19]  Wilfredo Palma,et al.  Long‐Memory Processes , 2006 .

[20]  Walter Willinger,et al.  Long-Range Dependence and Data Network Traffic , 2001 .

[21]  Arnold L. Neidhardt,et al.  The concept of relevant time scales and its application to queuing analysis of self-similar traffic (or is Hurst naughty or nice?) , 1998, SIGMETRICS '98/PERFORMANCE '98.

[22]  David K. Arrowsmith,et al.  The autocorrelation of double intermittency maps and the simulation of computer packet traffic , 2004 .

[23]  Martino Barenco Packet traffic in computer networks , 2002 .