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001			394128
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008			221005s2022 si | s |||| 0|eng d
020			_a9789811948749
024	7		_a10.1007/978-981-19-4874-9 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC
100	1		_aGuo, Zehua _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aBringing Machine Learning to Software-Defined Networks _cby Zehua Guo
250			_a1st edition 2022
264		1	_aSingapore _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XIII, 68 páginas) _b1 illus
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSpringerBriefs in Computer Science _x2191-5776
505	0		_aChapter 1 Machine Learning for Software-Defined Networking -- Chapter 2 Deep Reinforcement Learning-based Traffic Engineering in SD-WANs -- Chapter 3 Multi-Agent Reinforcement Learning-based Controller Load Balancing in SD-WANs -- Chapter 4 Deep Reinforcement Learning-based Flow Scheduling for Power Efficiency in Data Center Networks -- Chapter 5 Graph Neural Network-based Coflow Scheduling in Data Center Networks -- Chapter 6 Graph Neural Network-based Flow Migration for Network Function Virtualization -- Chapter 7 Conclusion and Future work.
520			_aEmerging machine learning techniques bring new opportunities to flexible network control and management. This book focuses on using state-of-the-art machine learning-based approaches to improve the performance of Software-Defined Networking (SDN). It will apply several innovative machine learning methods (e.g., Deep Reinforcement Learning, Multi-Agent Reinforcement Learning, and Graph Neural Network) to traffic engineering and controller load balancing in software-defined wide area networks, as well as flow scheduling, coflow scheduling, and flow migration for network function virtualization in software-defined data center networks. It helps readers reflect on several practical problems of deploying SDN and learn how to solve the problems by taking advantage of existing machine learning techniques. The book elaborates on the formulation of each problem, explains design details for each scheme, and provides solutions by running mathematical optimization processes, conducting simulated experiments, and analyzing the experimental results.
776	0	8	_iPrinted edition: _z9789811948732
776	0	8	_iPrinted edition: _z9789811948756
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-19-4874-9 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aSpringer_Computer_2022
999			_c394128 _d394128