A study on some infinite server queues in discrete-time

This work analysis some discrete-time queueing mechanisms with infinitely many servers.By using a shot noise process, general results on the system size in discrete-time are given both in transient state and in steady state. For this we use the classical differentiation formula of F´a di Bruno. First two moments of the system size and distribution of the busy period of the system are also computed.

The properties of the queuing model with the parallel structure

The present article is devoted to research the multi-channelk model with the parallel structure. It means that we consider the model which consists of two infinite-server queues. The service time in the each system has general function of distribution. In this case the stochastic dynamic of our model cannot be defined by Markov chain. As a result, analysis of such models is much more difficult than that of the corresponding Markovian queueing models. Besides we assume that customers arrive to our model according a bivariate Poisson input flow. This input process is characterized by the fact that customers arrive according to a bivariate Poisson flow simultaneously. We consider the number of customers in the systems at time t. This stochastic process describes the state of our model. In present paper we find the limit joint distribution of the number of customers in the systems. In a general way (by differentiating the corresponding generating function.) we obtain the main characteristics of this distribution, such as the expected number of customers in the nodes, its variance and correlation. In the case when parameters of our model dependent on the parameter n (number of series) the limit normal distribution was obtained for the service process in the stationary regime.

Large deviations of bivariate Gaussian extrema

We establish sharp tail asymptotics for componentwise extreme values of bivariate Gaussian random vectors with arbitrary correlation between the components. We consider two scaling regimes for the tail event in which we demonstrate the existence of a restricted large deviations principle and identify the unique rate function associated with these asymptotics. Our results identify when the maxima of both coordinates are typically attained by two different versus the same index, and how this depends on the correlation between the coordinates of the bivariate Gaussian random vectors. Our results complement a growing body of work on the extremes of Gaussian processes. The results are also relevant for steady-state performance and simulation analysis of networks of infinite server queues.