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008			230211s2022 sz | s |||| 0|eng d
020			_a9783031115493
024	7		_a10.1007/978-3-031-11549-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_a TJ163.5.D38 _b2022 EB
100	1		_aChen, Minghua _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686745
245	1	0	_aOnline Capacity Provisioning for Energy-Efficient Datacenters _cby Minghua Chen, Sid Chi-Kin Chau
250			_a1st edition 2022
264		1	_aCham _bSpringer International Publishing _c2022
300			_a1 recurso en línea (XII, 79 páginas) _b12 ilustraciones, 11 ilustraciones en blanco y negro
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 -- Preliminaries of Online Algorithms and Competitive Analysis -- Modeling and Problem Formulation -- The Case of A Single Server -- The General Case of Multiple Servers -- Experimental Studies -- Conclusion and Extensions.
520			_aThis book addresses the urgent issue of massive and inefficient energy consumption by data centers, which have become the largest co-located computing systems in the world and process trillions of megabytes of data every second. Dynamic provisioning algorithms have the potential to be the most viable and convenient of approaches to reducing data center energy consumption by turning off unnecessary servers, but they incur additional costs from being unable to properly predict future workload demands that have only recently been mitigated by advances in machine-learned predictions. This book explores whether it is possible to design effective online dynamic provisioning algorithms that require zero future workload information while still achieving close-to-optimal performance. It also examines whether characterizing the benefits of utilizing the future workload information can then improve the design of online algorithms with predictions in dynamic provisioning. The book specifically develops online dynamic provisioning algorithms with and without the available future workload information. Readers will discover the elegant structure of the online dynamic provisioning problem in a way that reveals the optimal solution through divide-and-conquer tactics. The book teaches readers to exploit this insight by showing the design of two online competitive algorithms with competitive ratios characterized by the normalized size of a look-ahead window in which exact workload prediction is available.
988			_aSynthesis Collection of Technology_2022
650		7	_2embne _9676716 _aCentros de proceso de datos
650		7	_2embne _9148602 _aAhorro de energía
650		7	_2embne _9156136 _aConsumo de energía
700	1		_aChau, Sid Chi-Kin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9686746
776	0	8	_iPrinted edition: _z9783031115486
776	0	8	_iPrinted edition: _z9783031115509
776	0	8	_iPrinted edition: _z9783031115516
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-11549-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b02/2022 _dz _eIG _zSI