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Titlebook: Computer Performance Engineering and Stochastic Modelling; 19th European Worksh Mauro Iacono,Marco Scarpa,Francesco Longo Conference procee

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Sojourn Time in a Markov Driven Fluid Queue with Finite Buffer entering fluid might be lost. We are interested in computing the sojourn time distribution.The lost of fluid at full capacity needs to be taken into account in order to extend the two step approach used in Deiana et al. in [.].
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Computer Performance Engineering and Stochastic Modelling978-3-031-43185-2Series ISSN 0302-9743 Series E-ISSN 1611-3349
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Sources of the Agulhas Current, entering fluid might be lost. We are interested in computing the sojourn time distribution.The lost of fluid at full capacity needs to be taken into account in order to extend the two step approach used in Deiana et al. in [.].
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https://doi.org/10.1007/978-0-387-30253-9ady state. Performance measures, including the rate of aborts, are evaluated. Numerical results are presented, illustrating the behaviour of the system when various parameters are varied. The accuracy of certain approximations is assessed by means of simulations.
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The Inexorable Expansion of Agriculture,ll as an additional fee if customers opt to receive premium service. We first study the parameter allocation of the DPS discipline which optimises the server’s revenue, assuming that all customers rationally select between premium and non-premium service. We then extend revenue management to DPS que
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The Inexorable Expansion of Agriculture,he fact that nodes in a distributed system are not necessarily synchronized, and may fail at any time. This has led to the emergence of randomized rumor spreading protocols, such as push and pull protocols, which have been studied extensively. The .-pull operation, which allows an uninformed node to
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https://doi.org/10.1007/3-540-37212-1we consider a traffic light with variable lengths for the red and green periods. More specifically we apply a method that allows the red and green periods to be according to a stochastic distribution. We obtain the probability generating functions of the system content at various observation epochs
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