Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems

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Arivazhagan, N. and Somasundaram, K. and Vijendra Babu, D. and Gomathy Nayagam, M. and Bommi, R. M. and Mohammad, Gouse Baig and Kumar, Puranam Revanth and Natarajan, Yuvaraj and Arulkarthick, V. J. and Shanmuganathan, V. K. and Srihari, K. and Ragul Vignesh, M. and Prabhu Sundramurthy, Venkatesa and Pallikonda Rajasekaran, M (2022) Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems. Scientific Programming, 2022. pp. 1-12. ISSN 1058-9244

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Abstract

Task scheduling in Internet of Health Things (IoHT) is crucial for reducing makespan. This study proposes a Hybrid Moth Flame Optimization (HMFO) approach for cloud computing-based e-healthcare systems. HMFO ensures uniform resource allocation and improved QoS. The model was trained using Google cluster dataset and evaluated on CloudSim, demonstrating better response time, resource utilization, energy efficiency, and reduced costs compared to other methods. © 2022 Elsevier B.V., All rights reserved.

Item Type: Article
Divisions: Engineering and Technology > Aarupadai Veedu Institute of Technology, Chennai > Electronics & Communication Engineering
Depositing User: Unnamed user with email techsupport@mosys.org
Last Modified: 02 Dec 2025 09:36
URI: https://vmuir.mosys.org/id/eprint/3030

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