Research Area:  Machine Learning
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.
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Author(s) Name:  Abdullah Lakhan, Qurat-Ul-Ain Mastoi, Mohamed Elhoseny, Muhammad Suleman, Mazin Abed Mohammed Memon,
Journal name:  Enterprise Information Systems
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Publisher name:  Taylor and Francis
DOI:  https://doi.org/10.1080/17517575.2021.1883122
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Paper Link:   https://www.tandfonline.com/doi/abs/10.1080/17517575.2021.1883122?journalCode=teis20