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.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | Enterprise; System; Partitioning; Scheduling; IoT; Deep neural networks; Workflow; Resource management; Mobile; Fog; Cloud |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
| Divisions: | Faculty of Computer Science & Information Technology |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 20 Oct 2023 04:37 |
| Last Modified: | 20 Oct 2023 04:37 |
| URI: | http://eprints.um.edu.my/id/eprint/41832 |
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