Self-Similar Network Traffic and Performance Evaluation by Kihong Park, Walter Willinger

By Kihong Park, Walter Willinger

A suite of labor from best researchers within the box, this publication covers all facets of self-similar community site visitors. Readers will achieve a greater realizing of those networks via a wide advent to the subject, in addition to feedback for destiny examine.

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6. Let SN …t† ˆ Niˆ1 Xi …t† denote the aggregate traf®c at time t. 4 TECHNICAL BACKGROUND Fig. 6 N ˆ 3 on=off sources, X1 …t†; X2 …t†, and X3 …t†, and their aggregation S3 …t† ˆ X1 …t† ‡ X2 …t† ‡ X3 …t†. where T > 0 is a scale factor that is explicitly incorporated. Thus YN …Tt† measures the total traf®c up to time Tt. What is the behavior of YN …Tt† for large T and N? We will simplify the discussion so as to concentrate on the single salient feature of how heavy-tailedness in¯uences long-range dependence.

In Proc. 1997 Winter Simulation Conference, 1997. 16. N. G. Duf®eld, J. T. Lewis, N. O'Connel, R. Russell, and F. Toomey. Statistical issues raised by the Bellcore data. In Proc. 11th IEE Teletraf®c Symposium, 1994. 17. N. G. Duf®eld and N. O'Connell. Large deviations and over¯ow probabilities for the general single server queue, with applications. Math. Proc. Cambridge Philos. Soc. 118: 363±374, 1995. 18. N. Duf®eld and W. Whitt. Control and recovery from rare congestion events in a large multi-server system.

69. T. Tuan and K. Park. Performance evaluation of multiple time scale TCP under self-similar traf®c conditions. Technical report, Dept. of Computer Sciences, Purdue University, 1999. CSD-TR-99-040. 70. A. Shwartz and A. Weiss. Large Deviations for Performance Analysis.

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