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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Published in:Scientific programming Vol. 2022; pp. 1 - 12
Main Authors: 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
Language:English
Published: New York Hindawi 05.01.2022
John Wiley & Sons, Inc
Subjects:
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 Access:Get full text
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Summary: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 in the IoHT environment over e-healthcare systems. The HMFO guarantees uniform resource assignment and enhanced quality of services (QoS). The model is trained with the Google cluster dataset such that it learns the instances of how a job is scheduled in cloud and the trained HMFO model is used to schedule the jobs in real time. The simulation is conducted on a CloudSim environment to test the scheduling efficacy of the model in hybrid cloud environment. The parameters used by this method for the performance assessment include the use of resources, response time, and energy utilization. In terms of response time, average run time, and lower costs, the hybrid HMFO approach has offered increased response rate with reduced cost and run time than other methods.
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ISSN:1058-9244
1875-919X
DOI:10.1155/2022/4100352