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020			_a9783030150280
024	7		_a10.1007/978-3-030-15028-0 _2doi
040			_bspa _dES-MaUEC _cES-MaUEC
050		4	_aQ335 _b2019 EB
100	1		_aYao, Haipeng _9671072
245	1	0	_aDeveloping Networks using Artificial Intelligence _cby Haipeng Yao, Chunxiao Jiang, Yi Qian.
264		1	_aCham _bImprint: Springer _c2019
300			_a1 recurso en línea (XI, 248 páginas) _b116 ilustraciones
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_atext file _bPDF
490	0		_aEngineering (Springer-11647)
490	0		_aWireless Networks _x2366-1186
505	0		_aPreface vii -- Acknowledgements ix -- Table of Contents xi -- Chapter 1 Introduction 1 -- Chapter 2 Intelligence-Driven Networking Architecture 13 -- Chapter 3 Intelligent Network Awareness 31 -- Chapter 4 Intelligent Network Control 79 -- Chapter 5 Intelligent Network Resource Management 151 -- Chapter 6 Intention Based Networking Management 191 -- Chapter 7 Conclusions and Future Challenges 237 -- Index 241.
520	3		_aThis book mainly discusses the most important issues in artificial intelligence-aided future networks, such as applying different ML approaches to investigate solutions to intelligently monitor, control and optimize networking. The authors focus on four scenarios of successfully applying machine learning in network space. It also discusses the main challenge of network traffic intelligent awareness and introduces several machine learning-based traffic awareness algorithms, such as traffic classification, anomaly traffic identification and traffic prediction. The authors introduce some ML approaches like reinforcement learning to deal with network control problem in this book. Traditional works on the control plane largely rely on a manual process in configuring forwarding, which cannot be employed for today's network conditions. To address this issue, several artificial intelligence approaches for self-learning control strategies are introduced. In addition, resource management problems are ubiquitous in the networking field, such as job scheduling, bitrate adaptation in video streaming and virtual machine placement in cloud computing. Compared with the traditional with-box approach, the authors present some ML methods to solve the complexity network resource allocation problems. Finally, semantic comprehension function is introduced to the network to understand the high-level business intent in this book. With Software-Defined Networking (SDN), Network Function Virtualization (NFV), 5th Generation Wireless Systems (5G) development, the global network is undergoing profound restructuring and transformation. However, with the improvement of the flexibility and scalability of the networks, as well as the ever-increasing complexity of networks, makes effective monitoring, overall control, and optimization of the network extremely difficult. Recently, adding intelligence to the control plane through AI&ML become a trend and a direction of network development This book's expected audience includes professors, researchers, scientists, practitioners, engineers, industry managers, and government research workers, who work in the fields of intelligent network. Advanced-level students studying computer science and electrical engineering will also find this book useful as a secondary textbook. .
650		7	_2embne _aInteligencia artificial _9413115
650		7	_2embne _aSistemas de comunicación móviles _9147646
700	1		_aJiang, Chunxiao. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aQian, Yi. _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut
776	0	8	_iPrinted edition: _z9783030150273
776	0	8	_iPrinted edition: _z9783030150297
776	0	8	_iPrinted edition: _z9783030150303
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-15028-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
988			_aPrimersemestre_2019_Engineering
998			_aSI _cm _dz _feng _ggw _h0 _b10/2019 _ek _zSI