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Retrial Queueing Systems: A Computational Approach

Retrial Queueing Systems: A Computational Approach

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Authors: Jesus R. Artalejo, Antonio Gomez-corral
Publisher: Springer
Category: Book

List Price: $129.00
Buy New: $106.81
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New (18) Used (4) from $106.81

Sales Rank: 649815

Media: Hardcover
Edition: 1
Pages: 318
Number Of Items: 1
Shipping Weight (lbs): 1.5
Dimensions (in): 9.5 x 6.4 x 0.9

ISBN: 3540787240
Dewey Decimal Number: 519.82
EAN: 9783540787242
ASIN: 3540787240

Publication Date: May 23, 2008
Shipping: Eligible for Super Saver Shipping
Availability: Usually ships in 24 hours

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Product Description

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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