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...
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2014
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| Schriftenreihe: | Institute of Mathematical Statistics textbooks
2 |
| Schlagworte: | |
| Online-Zugang: | BSB01 FHN01 Volltext |
| Zusammenfassung: | 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 |
| Beschreibung: | Title from publisher's bibliographic system (viewed on 05 Oct 2015) |
| Beschreibung: | 1 online resource (x, 222 pages) |
| ISBN: | 9781139565363 |
| DOI: | 10.1017/CBO9781139565363 |
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| 245 | 1 | 0 | |a Stochastic networks |c Frank Kelly, University of Cambridge, Elena Yudovina, University of Michigan, Ann Arbor |
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| 505 | 8 | |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 | 8 | |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 | |
| 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 | ||
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| 650 | 4 | |a Stochastic models | |
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Datensatz im Suchindex
| _version_ | 1804176881650499584 |
|---|---|
| any_adam_object | |
| author | Kelly, Frank 1950- |
| author_facet | Kelly, Frank 1950- |
| author_role | aut |
| author_sort | Kelly, Frank 1950- |
| author_variant | f k fk |
| building | Verbundindex |
| bvnumber | BV043940746 |
| classification_rvk | QH 237 SK 820 |
| collection | ZDB-20-CBO |
| contents | 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 -- 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 |
| ctrlnum | (ZDB-20-CBO)CR9781139565363 (OCoLC)992827298 (DE-599)BVBBV043940746 |
| dewey-full | 384.0151922 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 384 - Communications |
| dewey-raw | 384.0151922 |
| dewey-search | 384.0151922 |
| dewey-sort | 3384.0151922 |
| dewey-tens | 380 - Commerce, communications, transportation |
| discipline | Mathematik Wirtschaftswissenschaften |
| doi_str_mv | 10.1017/CBO9781139565363 |
| format | Electronic eBook |
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| id | DE-604.BV043940746 |
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| indexdate | 2024-07-10T07:39:14Z |
| institution | BVB |
| isbn | 9781139565363 |
| language | English |
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| publisher | Cambridge University Press |
| record_format | marc |
| series2 | Institute of Mathematical Statistics textbooks |
| spelling | Kelly, Frank 1950- Verfasser aut Stochastic networks Frank Kelly, University of Cambridge, Elena Yudovina, University of Michigan, Ann Arbor Cambridge Cambridge University Press 2014 1 online resource (x, 222 pages) txt rdacontent c rdamedia cr rdacarrier Institute of Mathematical Statistics textbooks 2 Title from publisher's bibliographic system (viewed on 05 Oct 2015) 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 -- 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 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 Telecommunication systems / Statistical methods Stochastic models Stochastisches Modell (DE-588)4057633-4 gnd rswk-swf Rechnernetz (DE-588)4070085-9 gnd rswk-swf Warteschlangennetz (DE-588)4225823-6 gnd rswk-swf Stochastischer Prozess (DE-588)4057630-9 gnd rswk-swf 1\p (DE-588)1071861417 Konferenzschrift 1993 Minneapolis Minn. gnd-content 2\p (DE-588)4123623-3 Lehrbuch gnd-content Warteschlangennetz (DE-588)4225823-6 s Stochastischer Prozess (DE-588)4057630-9 s 3\p DE-604 Rechnernetz (DE-588)4070085-9 s Stochastisches Modell (DE-588)4057633-4 s 4\p DE-604 Yudovina, Elena Sonstige oth Erscheint auch als Druckausgabe 978-1-107-03577-5 Erscheint auch als Druckausgabe 978-1-107-69170-4 https://doi.org/10.1017/CBO9781139565363 Verlag URL des Erstveröffentlichers Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 2\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 3\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk 4\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Kelly, Frank 1950- Stochastic networks 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 -- 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 Telecommunication systems / Statistical methods Stochastic models Stochastisches Modell (DE-588)4057633-4 gnd Rechnernetz (DE-588)4070085-9 gnd Warteschlangennetz (DE-588)4225823-6 gnd Stochastischer Prozess (DE-588)4057630-9 gnd |
| subject_GND | (DE-588)4057633-4 (DE-588)4070085-9 (DE-588)4225823-6 (DE-588)4057630-9 (DE-588)1071861417 (DE-588)4123623-3 |
| title | Stochastic networks |
| title_auth | Stochastic networks |
| title_exact_search | Stochastic networks |
| title_full | Stochastic networks Frank Kelly, University of Cambridge, Elena Yudovina, University of Michigan, Ann Arbor |
| title_fullStr | Stochastic networks Frank Kelly, University of Cambridge, Elena Yudovina, University of Michigan, Ann Arbor |
| title_full_unstemmed | Stochastic networks Frank Kelly, University of Cambridge, Elena Yudovina, University of Michigan, Ann Arbor |
| title_short | Stochastic networks |
| title_sort | stochastic networks |
| topic | Telecommunication systems / Statistical methods Stochastic models Stochastisches Modell (DE-588)4057633-4 gnd Rechnernetz (DE-588)4070085-9 gnd Warteschlangennetz (DE-588)4225823-6 gnd Stochastischer Prozess (DE-588)4057630-9 gnd |
| topic_facet | Telecommunication systems / Statistical methods Stochastic models Stochastisches Modell Rechnernetz Warteschlangennetz Stochastischer Prozess Konferenzschrift 1993 Minneapolis Minn. Lehrbuch |
| url | https://doi.org/10.1017/CBO9781139565363 |
| work_keys_str_mv | AT kellyfrank stochasticnetworks AT yudovinaelena stochasticnetworks |