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; Suleman, Muhammad; Mazin, Memon &; Mohammed, Abed; Lakhan, Abdullah; Mastoi, Qurat-Ul-Ain; Elhoseny, Mohamed; Memon, Muhammad Suleman; Mohammed, Mazin Abed

Abstract

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.

• Journal Article Type: Article
• Acceptance Date: Jan 26, 2021
• Online Publication Date: Feb 15, 2021
• Publication Date: 2022
• Deposit Date: Jan 17, 2024
• Journal: Enterprise Information Systems
• Print ISSN: 1751-7575
• Electronic ISSN: 1751-7583
• Publisher: Taylor & Francis
• Peer Reviewed: Peer Reviewed
• Volume: 16
• Issue: 7
• Article Number: 1883122
• DOI: https://doi.org/10.1080/17517575.2021.1883122
• Keywords: Enterprise; system; partitioning; scheduling; iot; deep neural networks; workflow; resource management; mobile; fog; cloud
• Public URL: https://uwe-repository.worktribe.com/output/11617119