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Download e-book for kindle: Analytical and Stochastic Modeling Techniques and by Dieter Fiems, Veronique Inghelbrecht, Bart Steyaert, Herwig

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

This booklet constitutes the refereed court cases of the fifteenth foreign convention on Analytical and Stochastic Modeling suggestions 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 provided have been conscientiously reviewed and chosen from fifty five submissions. The papers are geared up in topical sections on site visitors modeling, queueing structures, analytical tools and functions, distributions in stochastic modeling, queueing networks, simulation and version checking, in addition to instant networks.

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

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In [HW07a] analytical transformations based on BM AP s were given for fragmentation and sliding window procedures. Additionally, we proposed analytical transformations based on BM AP s with which we are able to incorporate the influence of loss processes occuring at wireless links [HW07b]. Besides their direct use for load modeling these results can be used to compute an optimal maximum fragment length with respect to throughput maximization. Moreover, the concept of load transformation is closely related to that of departure processes, where the transformed load is described by means of process specifications.

Being (m × m) matrices defined by: (D0 )ii = −λi , 1≤i≤m (D0 )ij = λi pi (0, j), 1 ≤ i, j ≤ m ∧ i = j (Dk )ij = λi pi (k, j), 1 ≤ i, j ≤ m ∧ k > 0 m ∞ m pi (0, j) + j=1 j=i pi (k, j) = 1, 1≤i≤m k=1 j=1 ♦ Here pi (k, j) denotes the probability that – given the process is in state i – a transition to state j occurs with a batch arrival of size k. Thus, we are able to model arbitrary discrete distributions with possibly infinite support. The overall rate with which the process leaves state i is given by λi .

In: Proc. 2006 Winter Simulation Conference, Monterey, CA, EEUU, pp. 2146–2153 (2006) 35. : Application of the Whittle estimator to the modeling of traffic based on the M/G/∞ process. IEEE Communications Letters 11(10), 817–819 (2007) 36. : A new heavy-tailed discrete distribution for LRD M/G/∞ sample generation. Performance Evaluation 47(2/3), 197–219 (2002) 37. : On estimating the intensity of Long-Range Dependence in finite and infinite variance time series. In: A Practical Guide to Heavy Tails, pp.

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