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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

Observations on using genetic-algorithms for channel allocation in mobile computing

TL;DR: The objective in this work is to gauge how well a GA-based channel borrower performs when compared to a greedy borrowing heuristic to establish how suited GA-like (stochastic search) algorithms are for the solution of optimization problems in mobile computing environments.
Posted Content

A Taxonomy and Survey of Energy-Efficient Data Centers and Cloud Computing Systems

TL;DR: In this paper, the authors present a taxonomy of energy-efficient design of computing systems covering the hardware, operating system, virtualization and data center levels, and discuss causes and problems of high power / energy consumption.
Book ChapterDOI

New research in nature inspired algorithms for mobility management in GSM networks

TL;DR: This study assesses the performance of two different nature inspired algorithms for mobile location management using a recent version of Particle Swarm Optimization based on geometric ideas and shows that the proposed techniques outperform existing methods in the literature.
Journal ArticleDOI

Collaboration- and Fairness-Aware Big Data Management in Distributed Clouds

TL;DR: A novel collaboration- and fairness-aware big data management problem in distributed cloud environments that aims to maximize the system throughout, while minimizing the operational cost of service providers to achieve the system throughput, subject to resource capacity and user fairness constraints is studied.
Journal ArticleDOI

Power efficient rate monotonic scheduling for multi-core systems

TL;DR: A technique to find the lowest core speed to schedule individual tasks and guarantees that all the tasks fulfill their deadlines and the overall system energy consumption is reduced.