5th International ICST Conference on Performance Evaluation Methodologies and Tools

Research Article

Explicit solutions for queues with Hypo-exponential service time and applications to product-form analysis

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  • @INPROCEEDINGS{10.4108/icst.valuetools.2011.245722,
        author={Andrea Marin and Samuel Rota Bul\'{o}},
        title={Explicit solutions for queues with Hypo-exponential service time and applications to product-form analysis},
        proceedings={5th International ICST Conference on Performance Evaluation Methodologies and Tools},
        publisher={ICST},
        proceedings_a={VALUETOOLS},
        year={2012},
        month={6},
        keywords={queueing theory product-form solutions},
        doi={10.4108/icst.valuetools.2011.245722}
    }
    
  • Andrea Marin
    Samuel Rota Bulò
    Year: 2012
    Explicit solutions for queues with Hypo-exponential service time and applications to product-form analysis
    VALUETOOLS
    ICST
    DOI: 10.4108/icst.valuetools.2011.245722
Andrea Marin1,*, Samuel Rota Bulò1
  • 1: Università Ca' Foscari
*Contact email: marin@dsi.unive.it

Abstract

Queueing systems with Poisson arrival processes and Hypo-exponential service time distribution have been widely studied in literature. Their steady-state analysis relies on the observation that the infinitesimal generator matrix has a block-regular structure and, hence, matrix-analytic method may be applied. Let $\pi{nk}$ be the steady-state probability of observing the $k$-th stage of service busy and $n$ customers in the queue, with $1\leq k \leq K$, and $K$ the number of stages, and let $\pibn=(\pi{n1}, \ldots, \pi{nK})$. Then, it is well-known that there exists a rate matrix {\R} such that $\pib{n+1}=\pibn {\R}$. In this paper we give a symbolic expression for such a matrix {\R}. Then, we exploit this result in order to address the problem of approximating a {\MH} queue by a model with initial perturbations which yields a product-form stationary distribution. We show that the result on the rate matrix allows us to specify the approximations for more general models than those that have been previously considered in literature and with higher accuracy.