Retrial Queueing Systems [electronic resource]: A Computational Approach/ by Jesús R. Artalejo, Antonio Gómez-Corral.
Publication details: Berlin, Heidelberg: Springer Berlin Heidelberg, 2008.Description: digitalISBN:- 9783540787259
- 519.6
Part I: An Introduction to Retrial Queueing Systems -- Introduction and Motivating Examples -- A General Overview -- Part II: Computational Analysis of Performance Descriptors -- Limiting Distribution of the System State -- Busy Period -- Waiting Time -- Other Descriptors -- Part III: Retrial Queueing Systems Analyzed Through the Matrix-Analytic Formalism -- The Matrix-Analytic Formalism -- Selected Retrial Queues with QBD Structure -- Selected Retrial Queues with GI/M/1 and M/G/1 Structures -- References -- Index .
The application of auto-repeat facilities in telephone systems, as well as the use of random access protocols in computer networks, have led to growing interest in retrial queueing models. Since much of the theory of retrial queues is complex from an analytical viewpoint, with this book the authors give a comprehensive and updated text focusing on approximate techniques and algorithmic methods for solving the analytically intractable models. Retrial Queueing Systems: A Computational Approach also Presents motivating examples in telephone and computer networks. Establishes a comparative analysis of the retrial queues versus standard queues with waiting lines and queues with losses. Integrates a wide range of techniques applied to the main M/G/1 and M/M/c retrial queues, and variants with general retrial times, finite population and the discrete-time case. Surveys basic results of the matrix-analytic formalism and emphasizes the related tools employed in retrial queues. Discusses a few selected retrial queues with QBD, GI/M/1 and M/G/1 structures. Features an abundance of numerical examples, and updates the existing literature. The book is intended for an audience ranging from advanced undergraduates to researchers interested not only in queueing theory, but also in applied probability, stochastic models of the operations research, and engineering. The prerequisite is a graduate course in stochastic processes, and a positive attitude to the algorithmic probability .
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