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http://dx.doi.org/10.3745/KIPSTC.2005.12C.2.281

Algorithmic Generation of Self-Similar Network Traffic Based on SRA  

Jeong HaeDuck J. (한국성서대학교 정보과학부)
Lee JongSuk R. (한국과학기술정보연구원 슈퍼컴퓨팅센터그리드연구실)
Publication Information
The KIPS Transactions:PartC / v.12C, no.2, 2005 , pp. 281-288 More about this Journal
Abstract
It is generally accepted that self-similar (or fractal) Processes may provide better models for teletraffic in modem computer networks than Poisson processes. f this is not taken into account, it can lead to inaccurate conclusions about performance of computer networks. Thus, an important requirement for conducting simulation studies of telecommunication networks is the ability to generate long synthetic stochastic self-similar sequences. A generator of pseudo-random self similar sequences, based on the SRA (successive random addition) method, is implemented and analysed in this paper. Properties of this generator were experimentally studied in the sense of its statistical accuracy and the time required to produce sequences of a given (long) length. This generator shows acceptable level of accuracy of the output data (in the sense of relative accuracy of the Hurst parameter) and is fast. The theoretical algorithmic complexity is O(n).
Keywords
Self-similar 프로세스;Self-similar sequence 생성기;Hurst 변수;통신 네트워크;
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1 M. Krunz and A. Makowski, 'A Source Model for VBR Video Traffic Based on M/G/${\infty}$ Input Processes,' Proceedings of IEEE INFOCOM'98, San Francisco, CA, USA, pp.1441-1448, 1998
2 M. W. Garrett and W. Willinger, 'Analysis, Modeling and Generation of Self-Similar VBR Video Traffic,' Computer Communication Review, Proceedings of ACM SIGCOMM'94, London, UK, Vol.24(4), pp.269-280, 1994   DOI
3 J. D. Gibbons and S. Chakraborti, 'Nonparametric Statistical Inference,' Marcel Dekker, Inc., New York, 1992
4 D. R. Cox, 'Long-Range Dependence: a Review,' Statistics: An Appraisa, Iowa State Statistirol Library, The Iowa State University Press, H.A. David and H.T. David (eds.), pp.55-74, 1984
5 A. J. Crilly, R.A. Earnshaw and H. Jones, 'Fractals and Chaos,' Springer-Verlag, New York, 1991
6 I. Norros, 'A Storage Model with Self-Similar Input,' Queueing Systems, Vol.16, pp.387-396, 1994   DOI
7 J. Beran, 'Statistical Methods for Data with Long Range Dependence,' Statistical Science, VoI.7(4), pp.404-427, 1992   DOI   ScienceOn
8 J. Beran, 'Statistics for Long-Memory Processes,' Chapman and Hall, New York, 1994
9 M. C. Cario and B. L. Nelson, 'Numerical Methods for Fitting and Simulating Autoregressive-to-Anything Precesses,' INFORMS Journal on Computing, Vol.10(1), pp.72-81, 1998   DOI   ScienceOn
10 T. Taralp, M. Devetsikiotis, I. Lambadaris and A. Bose, 'Efficient Fractional Gaussian Noise Generation Using the Spatial Renewal Process,' Proceedings cf IEEE International Conference on Communications (ICC'98), Atlanta, GA, USA, pp.7-11, 1998   DOI
11 O. Rose, 'Traffic Modeling of Variable Bit Rate MPEG Video and Its Impacts on ATM Networks,' PhD thesis, Bayerische Julius-Maximilians-Universitat Wurzburg, 1997
12 V. Paxson, 'Fast Approximation of Self-Similar Network Traffic,' Lawrence Berkeley Laboratory and EECS Division, University of California, Berkeley (No.LBL-36750), 1995
13 V. Paxson and S. Floyd, 'Wide-Area Traffic: the Failure of Poisson Modeling,' Computer Communication Review, Proceedings of ACM SIGCOMM'94, London, UK, pp.257-268, 1994   DOI
14 H.-O. Peitgen and D. Saupe, 'The Science of Practal Images,' Springer-Verlag, New York, 1988
15 B. K. Ryu, 'Fractal Network Traffic: from Understanding to Implications,' PhD thesis, Graduate School of Arts and Sciences, Columbia University, 1996
16 B .B. Mandelbrot and J. R. Wallis, 'Computer Experiments with Fractional Gaussian Noises,' Water Resources Research, Vol.5(1) , pp.228-267, 1969   DOI
17 N. Likhanov, B. Tsybakov and N.D. Georganas, 'Analysis of an ATM Buffer with Self-Similar ('Fractal') Input Traffic,' Proceedings of IEEE INFOCOM'95, Boston, Massachusetts, pp.985-992, 1995   DOI
18 A. L. Neidhardt and J. L. Wang, 'The Concept of Relevant Time Scales and its Application to Queueing Analysis of Self-Similar Traffic (or Is Hurst Naughty or Nice?),' Perforrmnce Evaluation Review, Proceedings of ACM SIGMETRICS'98, Madison, Wisconsin, USA, pp.222-232, 1998   DOI
19 B. B. Mandelbrot, 'A Fast Fractional Gaussian Noise Generator,' Water Resources Research, Vol.7, pp.543-553, 1971   DOI
20 W. E. Leland, M. S. Taqqu, W. WIllinger and D. V. Wilson, 'On the Self-Similar Nature of Ethernet Traffic (Extended Version),' IEEE ACM Transactions en Networking, Vol.2(1), pp.1-15, 1994   DOI   ScienceOn
21 J. R. M. Hosking, 'Modeling Persistence in Hydrological Time Series Using Fractional Differencing,' Water Resources Research, Vol.20(12), pp.1898-1908, 1984   DOI
22 W-C. Lau, A. Erramilli, J. L. Wang and W. Willinger, 'Self-Similar Traffic Generation: the Random Midpoint Displacement Algorithm and its Properties,' Proceedings of IEEE International Conference on Cmmunications (ICC'95), Seattle, WA, pp.466-472, 1995   DOI
23 C. W. J. Granger, 'Long Memory Relationships and the Aggregation of Dynamic Models,' Journal of Econometrics, Vol.14, North-Holland Publishing Company, pp.227-238, 1980   DOI   ScienceOn