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001 978-3-031-11549-3
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007 cr nn 008mamaa
008 221019s2022 sz | s |||| 0|eng d
020 _a9783031115493
_9978-3-031-11549-3
024 7 _a10.1007/978-3-031-11549-3
_2doi
050 4 _aQA76.9.D3
072 7 _aUN
_2bicssc
072 7 _aCOM021000
_2bisacsh
072 7 _aUN
_2thema
082 0 4 _a005.7
_223
100 1 _aChen, Minghua.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979175
245 1 0 _aOnline Capacity Provisioning for Energy-Efficient Datacenters
_h[electronic resource] /
_cby Minghua Chen, Sid Chi-Kin Chau.
250 _a1st ed. 2022.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2022.
300 _aXII, 79 p. 12 illus., 11 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _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.
650 0 _aDatabase management.
_93157
650 0 _aComputer science.
_99832
650 0 _aAlgorithms.
_93390
650 0 _aDynamical systems.
_979176
650 0 _aPower electronics.
_93614
650 1 4 _aDatabase Management System.
_932540
650 2 4 _aTheory and Algorithms for Application Domains.
_979177
650 2 4 _aDesign and Analysis of Algorithms.
_931835
650 2 4 _aDynamical Systems.
_979178
650 2 4 _aPower Electronics.
_93614
700 1 _aChau, Sid Chi-Kin.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_979179
710 2 _aSpringerLink (Online service)
_979180
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031115486
776 0 8 _iPrinted edition:
_z9783031115509
776 0 8 _iPrinted edition:
_z9783031115516
830 0 _aSynthesis Lectures on Learning, Networks, and Algorithms,
_x2690-4314
_979181
856 4 0 _uhttps://doi.org/10.1007/978-3-031-11549-3
912 _aZDB-2-SXSC
942 _cEBK
999 _c84732
_d84732