W. E. Leland, W. Willinger, M. S. Taqqu, and D. V. Wilson. Statistical analysis and stochastic modeling of self-similar datatraffic. In Proc. 14th Int. Teletraffic Cong., 6-10, volume 1, pages 319--328, Antibes Juan Les Pins, France, June 1994. Home/Search Document Not in Database Summary Related Articles Check |
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....[2] we briefly review the characteristics of aggregated traffic patterns with self similar nature. In section [3] we give an overview of the neural network modeling of bursty traffic patterns. Section [4] describes the typical multiple source system used for the application task. In section [5], we discuss the packet loss reduction application and compare the performance of our Dynamic Neural Sharing (DNS) scheme with the other buffer management schemes. Finally, section [6] concludes the paper. 2 Aggregated Bursty Traffic The main objective of the current section is to establish a ....
....N 1, with Hurst parameter H typically about 0.73, observations Xn from a short range dependent models are known to satisfy E[R(N) S(N ) k 5 N 0:5 , as N 1. This is usually referred to as the Hurst effect. 2.2 An Evidence of Self Similarity: Ethernet and VBR Video Traffic Measurements. In [5], Leland et al. from Bellcore research center have used high quality, high time resolution LAN traffic data collected between August 1989 and February 1992 on several Ethernet LAN s and have observed the absence of natural length of a burst. This behavior is very different from conventional ....
W.E.Leland, W.Willinger, M.S.Taqqu, D.V.Willson, "Statistical Analysis and Stochastic Modeling of Self-Similar Datatraffic ", ITC Vol. 14, PP. 319-328, 1994.
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....distributions and moments of the GOP sizes to model the GOP size process, e.g. with a Markov chain, an autoregressive process, or simply drawing GOP size samples based on the GOP size histogram. Another way to detect long range dependences is to use variance time plots, R S plots, or periodograms [1, 8]. Here, we focus on the R S plots, because it is a robust method Sequence Hurst exponent H race 0.99 soccer 0.91 lambs 0.89 terminator 0.89 mtv 0.89 simpsons 0.89 talk1 0.89 dino 0.88 atp 0.88 mr.bean 0.85 asterix 0.81 news 0.79 starwars 0.74 talk2 0.73 settop 0.53 Table 3: Hurst exponents of ....
W. E. Leland, W. Willinger, M. S. Taqqu, and D. V. Wilson. Statistical analysis and stochastic modeling of self-similar datatraffic. In Proceedings of the ITC 14, pages 319--328, 1994.
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W. E. Leland, W. Willinger, M. S. Taqqu, and D. V. Wilson. Statistical analysis and stochastic modeling of self-similar datatraffic. In Proc. 14th Int. Teletraffic Cong., 6-10, volume 1, pages 319--328, Antibes Juan Les Pins, France, June 1994.
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W. E. Leland, W. Willinger, M. S. Taqqu, and D. V. Wilson. Statistical analysis and stochastic modeling of self-similar datatraffic. In Proc. 14th Int. Teletraffic Cong., 6-10, volume 1, pages 319--328, Antibes Juan Les Pins, France, June 1994.
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W. E. Leland, W. Willinger, M. S. Taqqu, D. V. Willson, "Statistical Analysis and Stochastic Modeling of Self-Similar Datatraffic", ITC Vol. 14, pp. 319-328, 1994.
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