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Online Capacity Provisioning for Energy-Efficient Datacenters

Name: Online Capacity Provisioning for Energy-Efficient Datacenters
Brand: Springer, Berlin
SKU: 9783031115486
Price: 19486 HUF
Availability: InStock

Szerző: Minghua Chen, Sid Chi-Kin Chau

Nyelv:

Kötés: Kemény kötésű

Kiadó: Springer, Berlin

Elérhetőség: Beszállítói készleten

Küldés 10-13 napon belül

19 486 Ft

This book addresses the urgent issue of massive and inefficient energy consumption by data centers,...

Információk a könyvről

Szerző

Minghua Chen, Sid Chi-Kin Chau

Nyelv

Angol

Kötés

Könyv - Kemény kötésű

Kiadva

2022

oldal

EAN

9783031115486

Enbook ID

39336042

Kiadó

Springer, Berlin

Súly

346

Méretek

168 x 240 x 11

Teljes leírás

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.