New PDF release: Analytical and Stochastic Modeling Techniques and

By Dieter Fiems, Veronique Inghelbrecht, Bart Steyaert, Herwig Bruneel (auth.), Khalid Al-Begain, Armin Heindl, Miklós Telek (eds.)

ISBN-10: 354068980X

ISBN-13: 9783540689805

ISBN-10: 3540689826

ISBN-13: 9783540689829

This publication constitutes the refereed court cases of the fifteenth overseas convention on Analytical and Stochastic Modeling ideas and functions, ASMTA 2008, held in Nicosia, Cyprus, in June 2008 together with ECMS 2008, the twenty second eu convention on Modeling and Simulation.

The 22 revised complete papers awarded have been rigorously reviewed and chosen from fifty five submissions. The papers are equipped in topical sections on site visitors modeling, queueing platforms, analytical equipment and purposes, distributions in stochastic modeling, queueing networks, simulation and version checking, in addition to instant networks.

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Extra resources for Analytical and Stochastic Modeling Techniques and Applications: 15th International Conference, ASMTA 2008 Nicosia, Cyprus, June 4-6, 2008 Proceedings

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1 The pdf of the Erlang distribution is given by f (x) = 2 The pdf of the log-normal distribution is given by f (x) = 1 √ e xσ 2π − [ln(x)−μ]2 2σ2 . 40 S. E. 5 0 1000 2000 3000 4000 5000 0 0 1000 Stand. Deviation 2000 3000 4000 5000 Stand. Deviation Fig. 2. Deviation from the uniform distribution on [0, M ] of the fragment distribution for Erlang distributed packet lengths (left) resp. log-normal distributed packet lengths (right). scenarios with Forward Error Correction and can be used for load modeling and parameter optimization.

5 0 1000 2000 3000 4000 5000 0 0 1000 Stand. Deviation 2000 3000 4000 5000 Stand. Deviation Fig. 2. Deviation from the uniform distribution on [0, M ] of the fragment distribution for Erlang distributed packet lengths (left) resp. log-normal distributed packet lengths (right). scenarios with Forward Error Correction and can be used for load modeling and parameter optimization. Proposition 2. Let the instantaneous loss rate at time instant t be driven by the Markov process X = (X(t) : t ≥ 0) with generator matrix QL .

IEEE Journal on Selected Areas in Communications 16(5), 600–611 (1998) 28. : A storage model with self-similar input. Queueing Systems 16, 387–396 (1994) 29. : Buffer engineering for M/G/∞ input processes. D. Thesis, University of Maryland, College Park, MD, EEUU (2001) 30. : Wide-area traffic: The failure of Poisson modeling. IEEE/ACM Transactions on Networking 3(3), 226–244 (1995) 31. : A refined version of M/G/∞ processes for modeling VBR video traffic. Computer Communications 24(11), 1105–1114 (2001) 32.

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Analytical and Stochastic Modeling Techniques and Applications: 15th International Conference, ASMTA 2008 Nicosia, Cyprus, June 4-6, 2008 Proceedings by Dieter Fiems, Veronique Inghelbrecht, Bart Steyaert, Herwig Bruneel (auth.), Khalid Al-Begain, Armin Heindl, Miklós Telek (eds.)


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