Cloud-Internet of Health Things (IOHT) Task Scheduling Using Hybrid Moth Flame Optimization with Deep Neural Network Algorithm for E Healthcare Systems
Citations Over TimeTop 10% of 2022 papers
Abstract
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.
Related Papers
- → An Introduction of CloudSim Simulation tool for Modelling and Scheduling(2020)27 cited
- → Modelling resource heterogeneities in cloud simulations and quantifying their accuracy(2019)26 cited
- → Cloud Computing Modeling and Simulation using CloudSim Environment(2019)20 cited
- Application of the Cloudsim Toolkit in Virtual Machine Placement(2015)
- → SIMULATION-BASED NETWORK FAULT INJECTION IN THE CLOUDSIM PLUS CLOUD SIMULATION ENVIRONMENT(2023)