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

Considering task dependencies, the balancing of the Internet of Health Things (IoHT) scheduling is considered important to reduce the make span rate. In this paper, we developed a smart model approach for the best task schedule of Hybrid Moth Flame Optimization (HMFO) for cloud computing integrated...

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Veröffentlicht in:	Scientific programming Jg. 2022; S. 1 - 12
Hauptverfasser:	Arivazhagan, N., Somasundaram, K., Vijendra Babu, D., Gomathy Nayagam, M., Bommi, R. M., Mohammad, Gouse Baig, Kumar, Puranam Revanth, Natarajan, Yuvaraj, Arulkarthick, V. J., Shanmuganathan, V. K., Srihari, K., Ragul Vignesh, M., Prabhu Sundramurthy, Venkatesa
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Hindawi 05.01.2022 John Wiley & Sons, Inc
Schlagworte:	Algorithms Artificial neural networks Cloud computing Computer centers Employment End users Energy utilization Health care Internet Internet of Things Linear programming Neural networks Optimization Performance assessment Population Quality of service Response time Run time (computers) Schedules Scheduling Task scheduling Workloads
ISSN:	1058-9244, 1875-919X
Online-Zugang:	Volltext
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