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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Vydáno v:	Scientific programming Ročník 2022; s. 1 - 12
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York Hindawi 05.01.2022 John Wiley & Sons, Inc
Témata:	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
On-line přístup:	Získat plný text
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