9783031115486 - online capacity provisioning for energy-efficient datacenters (synthesis lectures on learning, networks, and algorithms) de chen, minghua; chau, sid chi-kin (13 resultados)

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Hardcover. Condición: new. Hardcover. This 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 mo…st 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Librería: Revaluation Books, Exeter, Reino UnidoRevaluation Books
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Hardcover. Condición: Brand New. 91 pages. 9.45x6.61x0.43 inches. In Stock.

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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This 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 algor…ithms 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.

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Condición: Hervorragend. Zustand: Hervorragend | Sprache: Englisch | Produktart: Bücher | This 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 pro…visioning 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.

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Librería: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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Condición: new. Questo è un articolo print on demand.

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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This 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 pr…ovisioning 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. 92 pp. Englisch.

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Librería: Majestic Books, Hounslow, Reino UnidoMajestic Books
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Librería: Biblios, frankfurt am main, HESSE, AlemaniaBiblios
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Idioma: Inglés
Editorial: Springer, Berlin|Springer International Publishing|Springer 2022
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Librería: moluna, Greven, Alemaniamoluna
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This 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 algorit.

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Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemaniabuchversandmimpf2000
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Buch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This 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 provis…ioning 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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 92 pp. Englisch.