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Online Capacity Provisioning for Energy-Efficient Datacenters [electronic resource] / by Minghua Chen, Sid Chi-Kin Chau.

By: Chen, Minghua [author.].
Contributor(s): Chau, Sid Chi-Kin [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Learning, Networks, and Algorithms: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2022Edition: 1st ed. 2022.Description: XII, 79 p. 12 illus., 11 illus. in color. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031115493.Subject(s): Database management | Computer science | Algorithms | Dynamical systems | Power electronics | Database Management System | Theory and Algorithms for Application Domains | Design and Analysis of Algorithms | Dynamical Systems | Power ElectronicsAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 005.7 Online resources: Click here to access online
Contents:
Introduction -- 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.
In: Springer Nature eBookSummary: 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 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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Introduction -- 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.

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 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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