# A tutorial on queuing and trunking with applications to communications [electronic resource] / William H. Tranter and Allen B. MacKenzie.

Material type: TextSeries: Synthesis digital library of engineering and computer science | Synthesis lectures on communications ; # 8.Publication details: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) :: Morgan & Claypool,, c2012Description: 1 electronic text (xii, 92 p.) : ill., digital fileISBN: 9781598292695 (electronic bk.)Subject(s): MATLAB | Queuing networks (Data transmission) -- Mathematical models | Queuing theory | queuing | trunking | Poisson process | Erlang distribution | Erlang-A and Erlang-B characteristics | blocking probability | delay probability | Little's theorem | networks of queues | Jackson's theorem | BCMP theorem | Kleinrock's formulaAdditional physical formats: Print version:: No titleDDC classification: 519.82 LOC classification: T57.9 | .T727 2012Online resources: Abstract with links to resource Also available in print.Item type | Current library | Call number | Status | Date due | Barcode |
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Ebooks | Indian Institute of Technology Delhi - Central Library | Available |

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series from website.

Includes bibliographical references (p. 89-90).

1. Introduction -- 1.1 The Poisson process - strengths and weaknesses -- 1.2 Outline -- 1.3 MATLAB --

2. Poisson, Erlang, and Pareto distributions -- 2.1 The Poisson distribution -- 2.1.1 Development of the Poisson distribution -- 2.1.2 Interevent times -- 2.2 The Erlang distribution -- 2.2.1 Derivation of the Erlang distribution -- 2.2.2 Mean and variance of the Erlang-m random variable -- 2.2.3 Plots of the Erlang distribution -- 2.2.4 Erlang and gamma random variables -- 2.3 The Pareto distribution -- 2.4 Problems -- 2.5 Appendix A. Generating samples with an exponential distribution -- 2.6 Appendix B. The gamma function --

3. A brief introduction to queueing theory -- 3.1 Birth-death processes -- 3.2 Examples of simple queues -- 3.2.1 The single-server queue -- 3.2.2 Multiple-server queues -- 3.3 Three example simulations -- 3.3.1 The simulation of a pure birth process -- 3.3.2 Simulation of a birth-death process -- 3.4 Problems -- 3.5 Appendix A. The moment-generating function -- 3.6 Appendix B. MATLAB code for examples 3.2 and 3.3 -- 3.6.1 MATLAB code for example 3.2 -- 3.6.2 MATLAB code for example 3.3 --

4. Blocking and delay -- 4.1 Erlang-B results (M/M/C/C) -- 4.2 Erlang-C results (M/M/C/[infinity]) -- 4.3 Delay time, Little's theorem -- 4.3.1 Little's theorem -- 4.3.2 Average queue length for M/M/C/[infinity] system -- 4.3.3 Result for delay -- 4.4 Problems -- 4.5 Appendix A. MATLAB code for the Erlang-B chart -- 4.6 Appendix B. MATLAB code for the Erlang-C chart --

5. Networks of queues -- 5.1 Burke's theorem -- 5.2 Basic model -- 5.3 Jackson's theorem -- 5.3.1 Statement of Jackson's theorem -- 5.3.2 Proof of Jackson's theorem -- 5.4 Extensions to Jackson's theorem -- 5.4.1 Dependent service rate networks -- 5.4.2 Jackson's theorem for dependent service rate network -- 5.4.3 Closed networks -- 5.4.4 Jackson's theorem for closed networks -- 5.5 BCMP theorem -- 5.5.1 Statement of the BCMP theorem -- 5.6 Kleinrock's formula -- 5.7 Problems -- 5.8 Appendix A. MATLAB code for example 5.3 -- 5.9 Appendix B. MATLAB code for example 5.3 --

Bibliography -- Authors' biographies.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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The motivation for developing this synthesis lecture was to provide a tutorial on queuing and trunking, with extensions to networks of queues, suitable for supplementing courses in communications, stochastic processes, and networking. An essential component of this lecture are the MATLAB-based demonstrations and exercises, which can be easily modified to enable the student to observe and evaluate the impact of changing parameters, arrival and departure statistics, queuing disciplines, the number of servers, and other important aspects of the underlying system model. Much of the work in this lecture is based on Poisson statistics, since Poisson models are useful due to the fact that Poisson models are analytically tractable and provide a useful approximation for many applications. We recognize that the validity of Poisson statistics is questionable for a number of networking applications and therefore we briefly discuss self-similar models and the Hurst parameter, long-term dependent models, the Pareto distribution, and other related topics. Appropriate references are given for continued study on these topics.

Also available in print.

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