978 research outputs found

    Rare event analysis of Markov-modulated infinite-server queues: a Poisson limit

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    This article studies an infinite-server queue in a Markov environment, that is, an infinite-server queue with arrival rates and service times depending on the state of a Markovian background process. Scaling the arrival rates (i) by a factor N and the rates (ij) of the background process by N1+E (for some E>0), the focus is on the tail probabilities of the number of customers in the system, in the asymptotic regime that N tends to . In particular, it is shown that the logarithmic asymptotics correspond to those of a Poisson distribution with an appropriate mean

    Asymptotic analysis by the saddle point method of the Anick-Mitra-Sondhi model

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    We consider a fluid queue where the input process consists of N identical sources that turn on and off at exponential waiting times. The server works at the constant rate c and an on source generates fluid at unit rate. This model was first formulated and analyzed by Anick, Mitra and Sondhi. We obtain an alternate representation of the joint steady state distribution of the buffer content and the number of on sources. This is given as a contour integral that we then analyze for large N. We give detailed asymptotic results for the joint distribution, as well as the associated marginal and conditional distributions. In particular, simple conditional limits laws are obtained. These shows how the buffer content behaves conditioned on the number of active sources and vice versa. Numerical comparisons show that our asymptotic results are very accurate even for N=20

    Rare event analysis of Markov-modulated infinite-service queues: A Poisson limit

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    This paper studies an infinite-server queue in a Markov environment, that is, an infinite-server queue with arrival rates and service times depending on the state of a Markovian background process. Scaling the arrival rates λi\lambda_i by a factor NN and the rates νij\nu_{ij} of the background process by N^{1+\vareps} (for some \vareps > 0), the focus is on the tail probabilities of the number of customers in the system, in the asymptotic regime that NN tends to \infty. In particular, it is shown that the logarithmic asymptotics correspond to those of a Poisson distribution with an appropriate mean

    Sample path large deviations for queues with many inputs

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    This paper presents a large deviations principle for the average of real-valued processes indexed by the positive integers, one which is particularly suited to queueing systems with many traffic flows. Examples are given of how it may be applied to standard queues with finite and infinite buffers, to priority queues and to finding most likely paths to overflow

    A discrete-time Markov modulated queuing system with batched arrivals

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    This paper examines a discrete-time queuing system with applications to telecommunications traffic. The arrival process is a particular Markov modulated process which belongs to the class of discrete batched Markovian arrival processes. The server process is a single server deterministic queue. A closed form exact solution is given for the expected queue length and delay. A simple system of equations is given for the probability of the queue exceeding a given length.Comment: to appear Performance Evaluatio
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