Overview
- Examines ways to improve the design of online algorithms with predictions in dynamic provisioning
- Describes best practices for implementing online dynamic provisioning algorithms for data centers
- Explores the effective use of future workload prediction data in online dynamic provisioning algorithms
Part of the book series: Synthesis Lectures on Learning, Networks, and Algorithms (SLLNA)
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Table of contents (7 chapters)
Keywords
About this book
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.
Authors and Affiliations
About the authors
Sid Chi-Kin Chau received his Ph.D. from the University of Cambridge with a scholarship by the Croucher Foundation Hong Kong and a B.Eng. (First-class Honours) degree from The Chinese University of Hong Kong. He is a Senior Lecturer with the School of Computing at the Australian National University. His research interests are related to computing algorithms and systems for smart sustainable cities, including smart grid, smart buildings, intelligent vehicles, and transportation. He also researches in broad areas of algorithms, optimization, Internet of Things, and blockchain. He was Associate Professor with the Department of Computer Science at Masdar Institute, which was created in collaboration with MIT, and is a part of Khalifa University. Previously, he was Visiting Professor at MIT, Senior Research Fellow at A*STAR in Singapore, Croucher Foundation Research Fellow at University College London, and Visiting Researcher at IBM Watson Research Center and BBN Technologies. He is Area Editor of ACM SIG Energy Informatics Review and Associate Editor of IEEE Systems Journal. He is on the program committees of several ACM conferences in smart energy systems and smart cities, such as ACM e-Energy, ACM BuildSys, and ACM MobiHoc. He was TPC Chair of ACM e-Energy 2018 and Guest Editor for IEEE Journal on Selected Areas in Communications, IEEE Journal of Internet of Things, and IEEE Transactions on Sustainable Computing. He received a Best Paper Award at ACM e-Energy 2021, a Best Paper Runner-up Award at ACM BuildSys 2018, and numerous times has been selected as a Best Paper finalists.
Bibliographic Information
Book Title: Online Capacity Provisioning for Energy-Efficient Datacenters
Authors: Minghua Chen, Sid Chi-Kin Chau
Series Title: Synthesis Lectures on Learning, Networks, and Algorithms
DOI: https://doi.org/10.1007/978-3-031-11549-3
Publisher: Springer Cham
eBook Packages: Synthesis Collection of Technology (R0), eBColl Synthesis Collection 11
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-11548-6Published: 20 October 2022
Softcover ISBN: 978-3-031-11551-6Published: 21 October 2023
eBook ISBN: 978-3-031-11549-3Published: 19 October 2022
Series ISSN: 2690-4306
Series E-ISSN: 2690-4314
Edition Number: 1
Number of Pages: XII, 79
Number of Illustrations: 1 b/w illustrations, 11 illustrations in colour
Topics: Information Systems and Communication Service, Theory of Computation, Algorithm Analysis and Problem Complexity, Dynamical Systems and Ergodic Theory, Vibration, Dynamical Systems, Control, Power Electronics, Electrical Machines and Networks