Stochastic networks

Communication networks underpin our modern world, and provide fascinating and challenging examples of large-scale stochastic systems. Randomness arises in communication systems at many levels: for example, the initiation and termination times of calls in a telephone network, or the statistical struc...

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Bibliographic Details
Main Authors: Kelly, Frank, Yudovina, Elena (Author)
Format: eBook
Language:English
Published: Cambridge Cambridge University Press 2014
Series:Institute of Mathematical Statistics textbooks
Subjects:
Online Access:
Collection: Cambridge Books Online - Collection details see MPG.ReNa
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100 1 |a Kelly, Frank 
245 0 0 |a Stochastic networks  |c Frank Kelly, University of Cambridge, Elena Yudovina, University of Michigan, Ann Arbor 
260 |a Cambridge  |b Cambridge University Press  |c 2014 
300 |a x, 222 pages  |b digital 
505 0 |a Machine generated contents note: Queueing and loss networks -- Decentralized optimization -- Random access networks -- Broadband networks -- Internet modelling -- Part I -- 1.Markov chains -- 1.1.Definitions and notation -- 1.2.Time reversal -- 1.3.Erlang's formula -- 1.4.Further reading -- 2.Queueing networks -- 2.1.An M/M/1 queue -- 2.2.A series of M/M//1 queues -- 2.3.Closed migration processes -- 2.4.Open migration processes -- 2.5.Little's law -- 2.6.Linear migration processes -- 2.7.Generalizations -- 2.8.Further reading -- 3.Loss networks -- 3.1.Network model -- 3.2.Approximation procedure -- 3.3.Truncating reversible processes -- 3.4.Maximum probability -- 3.5.A central limit theorem -- 3.6.Erlang fixed point -- 3.7.Diverse routing -- 3.8.Further reading -- Part II -- 4.Decentralized optimization -- 4.1.An electrical network -- 4.2.Road traffic models -- 4.3.Optimization of queueing and loss networks -- 4.4.Further reading -- 5.Random access networks -- 
505 0 |a Contents note continued: 5.1.The ALOHA protocol -- 5.2.Estimating backlog -- 5.3.Acknowledgement-based schemes -- 5.4.Distributed random access -- 5.5.Further reading -- 6.Effective bandwidth -- 6.1.Chernoff bound and Cramer's theorem -- 6.2.Effective bandwidth -- 6.3.Large deviations for a queue with many sources -- 6.4.Further reading -- Part III -- 7.Internet congestion control -- 7.1.Control of elastic network flows -- 7.2.Notions of fairness -- 7.3.A primal algorithm -- 7.4.Modelling TCP -- 7.5.What is being optimized? -- 7.6.A dual algorithm -- 7.7.Time delays -- 7.8.Modelling a switch -- 7.9.Further reading -- 8.Flow level Internet models -- 8.1.Evolution of flows -- 8.2.[alpha]-fair rate allocations -- 8.3.Stability of [alpha]-fair rate allocations -- 8.4.What can go wrong? -- 8.5.Linear network with proportional fairness -- 8.6.Further reading 
653 |a Telecommunication systems / Statistical methods 
653 |a Stochastic models 
700 1 |a Yudovina, Elena  |e [author] 
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989 |b CBO  |a Cambridge Books Online 
490 0 |a Institute of Mathematical Statistics textbooks 
856 4 0 |u https://doi.org/10.1017/CBO9781139565363  |x Verlag  |3 Volltext 
082 0 |a 384.0151922 
520 |a Communication networks underpin our modern world, and provide fascinating and challenging examples of large-scale stochastic systems. Randomness arises in communication systems at many levels: for example, the initiation and termination times of calls in a telephone network, or the statistical structure of the arrival streams of packets at routers in the Internet. How can routing, flow control and connection acceptance algorithms be designed to work well in uncertain and random environments? This compact introduction illustrates how stochastic models can be used to shed light on important issues in the design and control of communication networks. It will appeal to readers with a mathematical background wishing to understand this important area of application, and to those with an engineering background who want to grasp the underlying mathematical theory. Each chapter ends with exercises and suggestions for further reading