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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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Enhanced Simulated Annealing Technique for the Single-Row Routing Problem

TL;DR: ESSR (Enhanced Simulated annealing for Single-row Routing) model for solving the single-row routing problem is presented and results compare well against results obtained from the earlier model (SRR-7) and two other methods reported in the literature.
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Robust task scheduling for volunteer computing systems

TL;DR: Two novel robust task scheduling heuristics, which identify best task-resource matches in terms of makespan and robustness are proposed, based on a proactive reallocation scheme enabling output schedules to tolerate a certain degree of performance degradation.
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NHAD: Neuro-Fuzzy Based Horizontal Anomaly Detection in Online Social Networks

TL;DR: In this article, a self-healing neuro-fuzzy approach (NHAD) is used for the detection, recovery, and removal of horizontal anomalies efficiently and accurately, which operates over the five paradigms, namely, missing links, reputation gain, significant difference, trust properties, and trust score.
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Edge-Oriented Computing: A Survey on Research and Use Cases

TL;DR: The paper summarizes the concept of edge computing and compares it with cloud computing and discusses the challenges of optimal server placement, data security in edge networks, hybrid edge-cloud computing, simulation platforms for edge computing, and state-of-the-art improved edge networks.
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Zeus: A resource allocation algorithm for the cloud of sensors

TL;DR: This work forms the problem of resource allocation in CoS and proposes Zeus, a hybrid (partly-decentralized) algorithm for solving it, which is able to identify requests that are common for multiple applications and perform only once their required tasks, sharing the results among the applications for saving resources.