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Lookup NU author(s): Dr Fawzy Mohammad H Habeeb, Dr Dev JhaORCiD, Duaa Alqattan, Yinhao Li, Tomasz Szydlo, Professor Raj Ranjan
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2022.
For re-use rights please refer to the publisher's terms and conditions.
© 2005-2012 IEEE.Edge computing has gained momentum in recent years, as complementary to cloud computing, for supporting applications (e.g., industrial control systems) that require time-critical communication guarantees. While edge computing can provide immediate analysis of streaming data from Internet of Things devices, those devices lack computing capabilities to guarantee reasonable performance for time-critical applications. To alleviate this critical problem, the prevalent trend is to offload these data analytic tasks from the edge devices to the cloud. However, existing offloading approaches are static in nature as they are unable to adapt varying workload and network conditions. To handle these issues, we present a novel distributed and quality of services based multilevel queue traffic scheduling system that can undertake semiautomatic bandwidth slicing to process time-critical incoming traffic in the edge-cloud environments. Our developed system shows a great enhancement in latency and throughput as well as reduction in energy consumption for edge-cloud environments.
Author(s): Habeeb F, Alwasel K, Noor A, Jha DN, AlQattan D, Li Y, Aujla GS, Szydlo T, Ranjan R
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Industrial Informatics
Year: 2022
Volume: 18
Issue: 11
Pages: 8017-8026
Print publication date: 01/11/2022
Online publication date: 25/04/2022
Acceptance date: 12/04/2022
Date deposited: 11/11/2024
ISSN (print): 1551-3203
ISSN (electronic): 1941-0050
Publisher: IEEE
URL: https://doi.org/10.1109/TII.2022.3169971
DOI: 10.1109/TII.2022.3169971
ePrints DOI: 10.57711/m7v4-1j73
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