Deep neural network-based application partitioning and scheduling for hospitals and medical enterprises using IoT assisted mobile fog cloud
Document Type
Article
Publication Date
7-3-2022
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.
Keywords
Enterprise, System, Partitioning, Scheduling, IoT, Deep neural networks, Workflow, Resource management, Mobile, Fog, Cloud
Divisions
fsktm
Funders
None
Publication Title
Enterprise Information Systems
Volume
16
Issue
7
Publisher
TAYLOR & FRANCIS LTD
Publisher Location
2-4 PARK SQUARE, MILTON PARK, ABINGDON OR14 4RN, OXON, ENGLAND