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

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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Main Authors: Lakhan, Abdullah, Mastoi, Qurat-Ul-Ain, Elhoseny, Mohamed, Memon, Muhammad Suleman, Mohammed, Mazin Abed
Format: Article
Published: TAYLOR & FRANCIS LTD 2022
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.um.edu.my/41832/
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Institution: Universiti Malaya
id my.um.eprints.41832
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spelling my.um.eprints.418322023-10-20T04:37:13Z http://eprints.um.edu.my/41832/ Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud Lakhan, Abdullah Mastoi, Qurat-Ul-Ain Elhoseny, Mohamed Memon, Muhammad Suleman Mohammed, Mazin Abed QA75 Electronic computers. Computer science 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. TAYLOR & FRANCIS LTD 2022-07-03 Article PeerReviewed Lakhan, Abdullah and Mastoi, Qurat-Ul-Ain and Elhoseny, Mohamed and Memon, Muhammad Suleman and Mohammed, Mazin Abed (2022) Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud. Enterprise Information Systems, 16 (7). ISSN 1751-7575, DOI https://doi.org/10.1080/17517575.2021.1883122 <https://doi.org/10.1080/17517575.2021.1883122>. 10.1080/17517575.2021.1883122
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Lakhan, Abdullah
Mastoi, Qurat-Ul-Ain
Elhoseny, Mohamed
Memon, Muhammad Suleman
Mohammed, Mazin Abed
Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud
description 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.
format Article
author Lakhan, Abdullah
Mastoi, Qurat-Ul-Ain
Elhoseny, Mohamed
Memon, Muhammad Suleman
Mohammed, Mazin Abed
author_facet Lakhan, Abdullah
Mastoi, Qurat-Ul-Ain
Elhoseny, Mohamed
Memon, Muhammad Suleman
Mohammed, Mazin Abed
author_sort Lakhan, Abdullah
title Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud
title_short Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud
title_full Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud
title_fullStr Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud
title_full_unstemmed Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud
title_sort deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using iot assisted mobile fog cloud
publisher TAYLOR & FRANCIS LTD
publishDate 2022
url http://eprints.um.edu.my/41832/
_version_ 1781704561890689024

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