Online Capacity Provisioning for Energy-Efficient Datacenters (Synthesis Lectures on Learning, Networks, and Algorithms) - Softcover

Minghua Chen received his B.Eng. and M.S. degrees from the Department of Electronic Engineering at Tsinghua University. He received his Ph.D. degree from the Department of Electrical Engineering and Computer Sciences at University of California, Berkeley. He was with Microsoft Research Redmond and The Chinese University of Hong Kong before joining the School of Data Science at City University of Hong Kong, where he is a Professor. Minghua received the 2007 Eli Jury award from UC Berkeley (presented to a graduate student or recent alumnus for outstanding achievement in the area of systems, communications, control, or signal processing) and the 2013 Young Researcher Award from The Chinese University of Hong Kong. He has also received several best paper awards, including IEEE ICME Best Paper Award in 2009, IEEE Transactions on Multimedia Prize Paper Award in 2009, and ACM Multimedia Best Paper Award in 2012. He served as Associate Editor of IEEE/ACM Transactions on Networkingfrom 2014–2018. He received the ACM Recognition of Service Award in 2017 for service contributions to the research community. He is currently Senior Editor for IEEE Systems Journal (2021―present) and Executive Committee Member of ACM SIG Energy (2018–present). Minghua’s recent research interests include online optimization and algorithms, machine learning in power system operations, intelligent transportation systems, distributed optimization, delay-constrained network coding, and capitalizing the benefit of data-driven prediction in algorithm/system design. He is Distinguished Member of the ACM. 

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.

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.