Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud

These days, fog-cloud based healthcare application partitioning techniques have been growing progressively. However, existing static fog-cloud based application partitioning methods are static and cannot adopt dynamic changes in the dynamic environment (e.g., where network and computing nodes have r...

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Bibliographic Details
Published inEnterprise information systems Vol. 16; no. 7
Main Authors Lakhan, Abdullah, Mastoi, Qurat-Ul-Ain, Elhoseny, Mohamed, Memon, Muhammad Suleman, Mohammed, Mazin Abed
Format Journal Article
LanguageEnglish
Published Taylor & Francis 03.07.2022
Subjects
cloud
deep neural networks
Enterprise
fog
iot
mobile
partitioning
resource management
scheduling
system
workflow
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Summary:These days, fog-cloud based healthcare application partitioning techniques have been growing progressively. However, existing static fog-cloud based application partitioning methods are static and cannot adopt dynamic changes in the dynamic environment (e.g., where network and computing nodes have resource value variation) during the execution process. This study devises a Deep Neural Networks Energy Cost-Efficient Partitioning and Task Scheduling (DNNECTS) algorithm framework which consists of the following components: application partitioning, task sequencing, and scheduling. Experimental results show the suggested methods in terms of energy consumption and the applications' cost in the dynamic environment.
ISSN:1751-7575
1751-7583
DOI:10.1080/17517575.2021.1883122