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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
Performance Analysis of EDF Scheduling in a Multi-Priority Preemptive M/G/1 Queue
TL;DR: Comparisons with other algorithms reveal that EDF achieves a better balance among priority classes where high priority requests are favored while preventing lower priority requests from overstarvation.
BookDOI
Grid computing for bioinformatics and computational biology
TL;DR: This book presents a meta-modelling architecture for Grid computing for high performance bioinformatics applications that combines Globus and BOINC based systems, and discusses recent advances in solving the protein threading problem.
Proceedings ArticleDOI
The use of a Hopfield neural network in solving the mobility management problem
Javid Taheri,Albert Y. Zomaya +1 more
TL;DR: This work presents a new approach to solve the location management problem by using the location areas approach, and Hopfield neural network is used in this work to find the optimal configuration of location areas in a mobile network.
Journal ArticleDOI
SiteSeek: Post-translational modification analysis using adaptive locality-effective kernel methods and new profiles
TL;DR: The newly proposed methods used in SiteSeek were shown to be useful for the identification of protein phosphorylation sites as it performed much better than widely known predictors on the newly built PS-Benchmark_1 dataset.
Journal ArticleDOI
A least flow-time first load sharing approach for distributed server farm
TL;DR: A size- based approach, called the least flow-time first (LFF-SIZE), which reduces the delay caused by size variation while maintaining a balanced load in the system and shows a substantial improvement over existing load sharing and static size-based approaches under realistic heavy-tailed workloads.