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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.
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Proceedings ArticleDOI
Mobile computing: opportunities for parallel algorithms research
TL;DR: The last thirty years have seen trmendous growth in research in mobile telemcommunications as discussed by the authors, however, most of the research on mobile computing addresses the "engineering" issues and the electronic componentary required for building mobile systems.
Journal ArticleDOI
Detection of SLA Violation for Big Data Analytics Applications in Cloud
Xuezhi Zeng,Saurabh Garg,Mutaz Barika,Sanat Bista,Deepak Puthal,Albert Y. Zomaya,Rajiv Ranjan +6 more
TL;DR: This work proposes four machine learning techniques and integrates 12 resampling methods to detect SLA violations for batch-based BDAAs in the cloud and evaluates the efficiency of the proposed techniques in comparison with ideal and baseline classifiers based on a real-world trace dataset (Alibaba).
Proceedings ArticleDOI
Throughput Enhancement through Selective Time Sharing and Dynamic Grouping
TL;DR: This work designs a selective time sharing technique that allows waiting jobs to be co-scheduled with existing running jobs only if the overall throughput can be improved, and presents a dynamic grouping resource allocation mechanism that relaxes the contiguous allocation requirement imposed on gang scheduling.
Journal ArticleDOI
Time-Optimal Proximity Graph Computations on Enhanced Meshes
TL;DR: This work proposes time-optimal algorithms for constructing the Euclidian minimum spanning tree, the all-nearest neighbor graph, the relative neighborhood graph, and the symmetric farthest neighbor graph of ann-vertex unimodal polygon and shows that these algorithms run on meshes enhanced with row and column buses.
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An error-learning neural network for the tuning of robot dynamic models
TL;DR: This work presents an attempt adaptively to tune robot dynamic models in real-time situations with a neural network used as a dynamic model compensator (DMC) to learn the error or the deviation between the model and the system.