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Albert Y. Zomaya
Researcher at University of Sydney
Publications - 1020
Citations - 30827
Albert Y. Zomaya is an academic researcher from University of Sydney. The author has contributed to research in topics: Cloud computing & Scheduling (computing). The author has an hindex of 75, co-authored 946 publications receiving 24637 citations. Previous affiliations of Albert Y. Zomaya include University of Alabama & University of Sheffield.
Papers
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Journal ArticleDOI
A Survey of Clustering Algorithms for Big Data: Taxonomy and Empirical Analysis
Adil Fahad,Najlaa Alshatri,Zahir Tari,Abdullah Alamri,Ibrahim Khalil,Albert Y. Zomaya,Sebti Foufou,Abdelaziz Bouras +7 more
TL;DR: Concepts and algorithms related to clustering, a concise survey of existing (clustering) algorithms as well as a comparison, both from a theoretical and an empirical perspective are introduced.
Book ChapterDOI
A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems
Anton Beloglazov,Anton Beloglazov,Rajkumar Buyya,Rajkumar Buyya,Young Choon Lee,Young Choon Lee,Albert Y. Zomaya,Albert Y. Zomaya +7 more
TL;DR: This study discusses causes and problems of high power/energy consumption, and presents a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization, and data center levels.
Proceedings ArticleDOI
Federated Learning over Wireless Networks: Optimization Model Design and Analysis
TL;DR: This work formulates a Federated Learning over wireless network as an optimization problem FEDL that captures both trade-offs and obtains the globally optimal solution by charactering the closed-form solutions to all sub-problems, which give qualitative insights to problem design via the obtained optimal FEDl learning time, accuracy level, and UE energy cost.
Journal ArticleDOI
Energy efficient utilization of resources in cloud computing systems
Young Choon Lee,Albert Y. Zomaya +1 more
TL;DR: Two energy-conscious task consolidation heuristics are presented, which aim to maximize resource utilization and explicitly take into account both active and idle energy consumption and demonstrate their promising energy-saving capability.
Journal ArticleDOI
Remote sensing big data computing
TL;DR: A brief overview on the Big Data and data-intensive problems, including the analysis of RS Big Data, Big Data challenges, current techniques and works for processing RS Big data is given.