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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
Federated Clouds for Efficient Multitasking in Distributed Artificial Intelligence Applications
TL;DR: In this article , a federated cloud/edge (FCE) framework for distributed medical image processing across multiple hospital sites is presented, which appeals to train many machine learning models efficiently with workload balancing and reduced communication overheads.
Proceedings ArticleDOI
Semi-Online Multi-Machine with Restart Scheduling for Integrated Edge and Cloud Computing Systems
TL;DR: In this paper , a multi-machine task scheduling problem in an integrated serverless edge and cloud computing system is studied, where tasks can be scheduled locally on edge processors or offloaded to cloud servers, with the objective of minimizing the makespan, i.e., the total time to finish all tasks.
Proceedings ArticleDOI
RBT-MF: A Distributed Rubber Band Technique for Maximum Flow Problem in Azure
TL;DR: The experimental results show that the proposed technique can effectively find an answer for the maximum flow problem in a graph, and as the size of the graph in terms of number of nodes, number of edges and flow value increase, the proposed scheme outperforms in comparison to the selected benchmarks.
Journal ArticleDOI
Cloud-Native Computing: A Survey from the Perspective of Services
Shuguang Deng,Hailiang Zhao,Binbin Huang,Cheng Zhang,Feiyi Chen,Jianwei Yin,Schahram Dustdar,Albert Y. Zomaya +7 more
TL;DR: In this article , the authors discuss the fundamental necessities and performance metrics that play critical roles during the development and management of cloud-native applications, highlight the key implications and limitations of existing works in each state, and discuss the challenges, future directions and research opportunities.
BookDOI
Network and Parallel Computing
TL;DR: Experimental results show that these application-level scheduling approaches, when equipped with task bundling, can deliver good performance for Many-Task Computing in terms of both Makespan and Flowtime.