Processing networks: fluid models and stability
This state-of-the-art account unifies material developed in journal articles over the last 35 years, with two central thrusts: It describes a broad class of system models that the authors call 'stochastic processing networks' (SPNs), which include queueing networks and bandwidth sharing ne...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge, United Kingdom ; New York, NY
Cambridge University Press
2020
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| Schlagwörter: | |
| Links: | https://doi.org/10.1017/9781108772662 https://doi.org/10.1017/9781108772662 https://doi.org/10.1017/9781108772662 https://doi.org/10.1017/9781108772662 |
| Zusammenfassung: | This state-of-the-art account unifies material developed in journal articles over the last 35 years, with two central thrusts: It describes a broad class of system models that the authors call 'stochastic processing networks' (SPNs), which include queueing networks and bandwidth sharing networks as prominent special cases; and in that context it explains and illustrates a method for stability analysis based on fluid models. The central mathematical result is a theorem that can be paraphrased as follows: If the fluid model derived from an SPN is stable, then the SPN itself is stable. Two topics discussed in detail are (a) the derivation of fluid models by means of fluid limit analysis, and (b) stability analysis for fluid models using Lyapunov functions. With regard to applications, there are chapters devoted to max-weight and back-pressure control, proportionally fair resource allocation, data center operations, and flow management in packet networks. Geared toward researchers and graduate students in engineering and applied mathematics, especially in electrical engineering and computer science, this compact text gives readers full command of the methods |
| Umfang: | 1 Online-Ressource (xx, 384 Seiten) |
| ISBN: | 9781108772662 |
| DOI: | 10.1017/9781108772662 |
Internformat
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| 100 | 1 | |a Dai, J. G. |d 1962- |e Verfasser |0 (DE-588)1055085084 |4 aut | |
| 245 | 1 | 0 | |a Processing networks |b fluid models and stability |c J. G. Dai, J. Michael Harrison |
| 264 | 1 | |a Cambridge, United Kingdom ; New York, NY |b Cambridge University Press |c 2020 | |
| 300 | |a 1 Online-Ressource (xx, 384 Seiten) | ||
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| 505 | 8 | |a Stochastic processing networks -- Markov representations -- Extensions and complements -- Is stability achievable? -- Fluid limits, fluid equations and positive recurrence -- Fluid equations that characterize specific policies -- Proving fluid model stability using Lyapunov functions -- Max-weight and back-pressure control -- Proportionally fair resource allocation -- Task allocation in server farms -- Multi-hop packet networks | |
| 520 | |a This state-of-the-art account unifies material developed in journal articles over the last 35 years, with two central thrusts: It describes a broad class of system models that the authors call 'stochastic processing networks' (SPNs), which include queueing networks and bandwidth sharing networks as prominent special cases; and in that context it explains and illustrates a method for stability analysis based on fluid models. The central mathematical result is a theorem that can be paraphrased as follows: If the fluid model derived from an SPN is stable, then the SPN itself is stable. Two topics discussed in detail are (a) the derivation of fluid models by means of fluid limit analysis, and (b) stability analysis for fluid models using Lyapunov functions. With regard to applications, there are chapters devoted to max-weight and back-pressure control, proportionally fair resource allocation, data center operations, and flow management in packet networks. Geared toward researchers and graduate students in engineering and applied mathematics, especially in electrical engineering and computer science, this compact text gives readers full command of the methods | ||
| 650 | 4 | |a Stochastic processes | |
| 650 | 4 | |a Linear programming | |
| 650 | 4 | |a Queuing networks (Data transmission) | |
| 650 | 4 | |a Fluid dynamics | |
| 650 | 4 | |a Stability | |
| 653 | 0 | |a Stochastic processes | |
| 653 | 0 | |a Linear programming | |
| 653 | 0 | |a Queuing networks (Data transmission) | |
| 653 | 0 | |a Fluid dynamics | |
| 653 | 0 | |a Stability | |
| 700 | 1 | |a Harrison, J. Michael |d 1944- |e Sonstige |0 (DE-588)171368746 |4 oth | |
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Datensatz im Suchindex
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| author | Dai, J. G. 1962- |
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| author_facet | Dai, J. G. 1962- |
| author_role | aut |
| author_sort | Dai, J. G. 1962- |
| author_variant | j g d jg jgd |
| building | Verbundindex |
| bvnumber | BV046925101 |
| collection | ZDB-20-CBO |
| contents | Stochastic processing networks -- Markov representations -- Extensions and complements -- Is stability achievable? -- Fluid limits, fluid equations and positive recurrence -- Fluid equations that characterize specific policies -- Proving fluid model stability using Lyapunov functions -- Max-weight and back-pressure control -- Proportionally fair resource allocation -- Task allocation in server farms -- Multi-hop packet networks |
| ctrlnum | (ZDB-20-CBO)CR9781108772662 (OCoLC)1199061048 (DE-599)BVBBV046925101 |
| dewey-full | 519.2/3 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.2/3 |
| dewey-search | 519.2/3 |
| dewey-sort | 3519.2 13 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1017/9781108772662 |
| format | Electronic eBook |
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| id | DE-604.BV046925101 |
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| institution | BVB |
| isbn | 9781108772662 |
| language | English |
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| spelling | Dai, J. G. 1962- Verfasser (DE-588)1055085084 aut Processing networks fluid models and stability J. G. Dai, J. Michael Harrison Cambridge, United Kingdom ; New York, NY Cambridge University Press 2020 1 Online-Ressource (xx, 384 Seiten) txt rdacontent c rdamedia cr rdacarrier Stochastic processing networks -- Markov representations -- Extensions and complements -- Is stability achievable? -- Fluid limits, fluid equations and positive recurrence -- Fluid equations that characterize specific policies -- Proving fluid model stability using Lyapunov functions -- Max-weight and back-pressure control -- Proportionally fair resource allocation -- Task allocation in server farms -- Multi-hop packet networks This state-of-the-art account unifies material developed in journal articles over the last 35 years, with two central thrusts: It describes a broad class of system models that the authors call 'stochastic processing networks' (SPNs), which include queueing networks and bandwidth sharing networks as prominent special cases; and in that context it explains and illustrates a method for stability analysis based on fluid models. The central mathematical result is a theorem that can be paraphrased as follows: If the fluid model derived from an SPN is stable, then the SPN itself is stable. Two topics discussed in detail are (a) the derivation of fluid models by means of fluid limit analysis, and (b) stability analysis for fluid models using Lyapunov functions. With regard to applications, there are chapters devoted to max-weight and back-pressure control, proportionally fair resource allocation, data center operations, and flow management in packet networks. Geared toward researchers and graduate students in engineering and applied mathematics, especially in electrical engineering and computer science, this compact text gives readers full command of the methods Stochastic processes Linear programming Queuing networks (Data transmission) Fluid dynamics Stability Harrison, J. Michael 1944- Sonstige (DE-588)171368746 oth Erscheint auch als Druck-Ausgabe 978-1-108-48889-1 https://doi.org/10.1017/9781108772662 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Dai, J. G. 1962- Processing networks fluid models and stability Stochastic processing networks -- Markov representations -- Extensions and complements -- Is stability achievable? -- Fluid limits, fluid equations and positive recurrence -- Fluid equations that characterize specific policies -- Proving fluid model stability using Lyapunov functions -- Max-weight and back-pressure control -- Proportionally fair resource allocation -- Task allocation in server farms -- Multi-hop packet networks Stochastic processes Linear programming Queuing networks (Data transmission) Fluid dynamics Stability |
| title | Processing networks fluid models and stability |
| title_auth | Processing networks fluid models and stability |
| title_exact_search | Processing networks fluid models and stability |
| title_full | Processing networks fluid models and stability J. G. Dai, J. Michael Harrison |
| title_fullStr | Processing networks fluid models and stability J. G. Dai, J. Michael Harrison |
| title_full_unstemmed | Processing networks fluid models and stability J. G. Dai, J. Michael Harrison |
| title_short | Processing networks |
| title_sort | processing networks fluid models and stability |
| title_sub | fluid models and stability |
| topic | Stochastic processes Linear programming Queuing networks (Data transmission) Fluid dynamics Stability |
| topic_facet | Stochastic processes Linear programming Queuing networks (Data transmission) Fluid dynamics Stability |
| url | https://doi.org/10.1017/9781108772662 |
| work_keys_str_mv | AT daijg processingnetworksfluidmodelsandstability AT harrisonjmichael processingnetworksfluidmodelsandstability |