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008			220601s2010 sz | s |||| 0|eng d
020			_a9783031799921
024	7		_a10.1007/978-3-031-79992-1 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aTK5105 _b2010 EB
100	1		_aJiang, Libin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9688420
245	1	0	_aScheduling and Congestion Control for Wireless and Processing Networks _cby Libin Jiang, Jean Walrand
250			_a1st edition 2010
264		1	_aCham _bSpringer International Publishing _c2010
300			_a1 recurso en línea (XI, 144 páginas)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Learning Networks and Algorithms _x2690-4314
505	0		_aIntroduction -- Overview -- Scheduling in Wireless Networks -- Utility Maximization in Wireless Networks -- Distributed CSMA Scheduling with Collisions -- Stochastic Processing networks.
520			_aIn this book, we consider the problem of achieving the maximum throughput and utility in a class of networks with resource-sharing constraints. This is a classical problem of great importance. In the context of wireless networks, we first propose a fully distributed scheduling algorithm that achieves the maximum throughput. Inspired by CSMA (Carrier Sense Multiple Access), which is widely deployed in today's wireless networks, our algorithm is simple, asynchronous, and easy to implement. Second, using a novel maximal-entropy technique, we combine the CSMA scheduling algorithm with congestion control to approach the maximum utility. Also, we further show that CSMA scheduling is a modular MAC-layer algorithm that can work with other protocols in the transport layer and network layer. Third, for wireless networks where packet collisions are unavoidable, we establish a general analytical model and extend the above algorithms to that case. Stochastic Processing Networks (SPNs) model manufacturing, communication, and service systems. In manufacturing networks, for example, tasks require parts and resources to produce other parts. SPNs are more general than queueing networks and pose novel challenges to throughput-optimum scheduling. We proposes a "deficit maximum weight" (DMW) algorithm to achieve throughput optimality and maximize the net utility of the production in SPNs. Table of Contents: Introduction / Overview / Scheduling in Wireless Networks / Utility Maximization in Wireless Networks / Distributed CSMA Scheduling with Collisions / Stochastic Processing networks.
988			_aSynthesis Collection of Technology_2010
650		7	_2embne _9141354 _aRedes informáticas
650		7	_2embne _9158044 _aSistemas de comunicación inalámbricos
650		7	_2embne _aInteligencia artificial _9413115
700	1		_aWalrand, Jean _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686506
776	0	8	_iPrinted edition: _z9783031799914
776	0	8	_iPrinted edition: _z9783031799938
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-79992-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b05/2023 _dz _eb _zSI