Accurate and fast replication on the generation of fractal network traffic using alternative probability models

Synthetic self-similar traffic in computer networks simulation is of imperative significance for the capturing and reproducing of actual Internet data traffic behavior. A universally used procedure for generating self-similar traffic is achieved by aggregating On/Off sources where the active (On) and idle (Off) periods exhibit heavy tailed distributions. This work analyzes the balance between accuracy and computational efficiency in generating self-similar traffic and presents important results that can be useful to parameterize existing heavy tailed distributions such as Pareto, Weibull and Lognormal in a simulation analysis. Our results were obtained through the simulation of various scenarios and were evaluated by estimating the Hurst (H) parameter, which measures the self-similarity level, using several methods.

[1]  Sándor Molnár,et al.  On the propagation of long-range dependence in the Internet , 2000, SIGCOMM.

[2]  Moshe Zukerman,et al.  Performance evaluation of a queue fed by a Poisson Pareto burst process , 2002, Comput. Networks.

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

[4]  Sheldon M. Ross,et al.  Introduction to probability models , 1975 .

[5]  Allen B. Downey,et al.  Evidence for long-tailed distributions in the internet , 2001, IMW '01.

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

[7]  Biplab Sikdar,et al.  On reducing the degree of second-order scaling in network traffic , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

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

[9]  Krzysztof Pawlikowski,et al.  Fast self-similar teletraffic generation based on FGN and wavelets , 1999, ICON.

[10]  Fei Xue,et al.  Traffic modeling based on FARIMA models , 1999, Engineering Solutions for the Next Millennium. 1999 IEEE Canadian Conference on Electrical and Computer Engineering (Cat. No.99TH8411).

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

[12]  Mark E. Crovella,et al.  Effect of traffic self-similarity on network performance , 1997, Other Conferences.

[13]  Sheldon M. Ross,et al.  Introduction to probability models , 1975 .

[14]  Matthias Grossglauser,et al.  On the relevance of long-range dependence in network traffic , 1996, SIGCOMM '96.

[15]  Azer Bestavros,et al.  Self-similarity in World Wide Web traffic: evidence and possible causes , 1996, SIGMETRICS '96.

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

[17]  Moshe Zukerman,et al.  Admission control schemes for bursty multimedia traffic , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).