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| 003 | ES-MaUEC | ||
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| 008 | 211103s2021 sz | s |||| 0|eng d | ||
| 020 | _a9783030871369 | ||
| 024 | 7 |
_a10.1007/978-3-030-87136-9 _2doi |
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| 040 |
_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC |
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| 050 | 4 |
_aTK5103.2 _b2021 EB |
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| 100 | 1 |
_aZhuang, Wei _9681747 |
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| 245 | 1 | 0 |
_aDynamic Resource Management in Service-Oriented Core Networks _cby Weihua Zhuang, Kaige Qu. |
| 250 | _aFirst edition 2021 | ||
| 264 | 1 |
_aCham _bSpringer International Publising _c2021 |
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| 300 |
_a1 recurso en línea (XII, 173 páginas) _b189 ilustraciones, 59 ilustraciones a color |
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| 336 |
_2rdacontent _aTexto _btxt |
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| 337 |
_2rdamedia _aelectrónico _bc |
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| 338 |
_2rdacarrier _arecurso electrónico _bcr |
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| 347 |
_aarchivo de texto _bPDF |
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| 490 | 0 |
_aWireless Networks _x2366-1445 |
|
| 490 | 0 | _aComputer Science (SpringerNature-11645) | |
| 490 | 0 | _aComputer Science (R0) (SpringerNature-43710) | |
| 520 | 3 | _aThis book provides a timely and comprehensive study of dynamic resource management for network slicing in service-oriented fifth-generation (5G) and beyond core networks. This includes the perspective of developing efficient computation resource provisioning and scheduling solutions to guarantee consistent service performance in terms of end-to-end (E2E) data delivery delay. Based on a simplified M/M/1 queueing model with Poisson traffic arrivals, an optimization model for flow migration is presented to accommodate the large-timescale changes in the average traffic rates with average E2E delay guarantee, while addressing a trade-off between load balancing and flow migration overhead. To overcome the limitations of Poisson traffic model, the authors present a machine learning approach for dynamic VNF resource scaling and migration. The new solution captures the inherent traffic patterns in a real-world traffic trace with non-stationary traffic statistics in large timescale, predicts resource demands for VNF resource scaling, and triggers adaptive VNF migration decision making, to achieve load balancing, migration cost reduction, and resource overloading penalty suppression in the long run. Both supervised and unsupervised machine learning tools are investigated for dynamic resource management. To accommodate the traffic dynamics in small time granularities, the authors present a dynamic VNF scheduling scheme to coordinate the scheduling among VNFs of multiple services, which achieves network utility maximization with delay guarantee for each service. Researchers and graduate students working in the areas of electrical engineering, computing engineering and computer science will find this book useful as a reference or secondary text. Professionals in industry seeking solutions to dynamic resource management for 5G and beyond networks will also want to purchase this book. | |
| 988 | _aSpringer_Computer_2021 | ||
| 650 | 7 |
_2embne _9158044 _aSistemas de comunicación inalámbricos |
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| 700 | 1 |
_aQu, Kaige _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 776 | 0 | 8 |
_iPrinted edition: _z9783030871352 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030871376 |
| 776 | 0 | 8 |
_iPrinted edition: _z9783030871383 |
| 856 | 4 | 0 |
_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-87136-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid) |
| 942 |
_2lcc _cLE |
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| 998 |
_b02/2022 _dz _eh _zSI |
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