Open Access

ARTICLE

Hybrid Whale Optimization Algorithm for Resource Optimization in Cloud E-Healthcare Applications

Punit Gupta¹, Sanjit Bhagat², Dinesh Kumar Saini^1,*, Ashish Kumar², Mohammad Alahmadi³, Prakash Chandra Sharma¹

1 School of Computing & Information Technology, Manipal University Jaipur, Jaipur, 303007, India
2 School of Basic Sciences, Manipal University Jaipur, Jaipur, 303007, India
3 Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, 23218, Saudi Arabia

* Corresponding Author: Dinesh Kumar Saini. Email:

(This article belongs to the Special Issue: Edge Computing and Machine Learning for Improving Healthcare Services)

Computers, Materials & Continua 2022, 71(3), 5659-5676. https://doi.org/10.32604/cmc.2022.023056

Received 26 August 2021; Accepted 01 December 2021; Issue published 14 January 2022

Abstract

In the next generation of computing environment e-health care services depend on cloud services. The Cloud computing environment provides a real-time computing environment for e-health care applications. But these services generate a huge number of computational tasks, real-time computing and comes with a deadline, so conventional cloud optimization models cannot fulfil the task in the least time and within the deadline. To overcome this issue many resource optimization meta-heuristic models are been proposed but these models cannot find a global best solution to complete the task in the least time and manage utilization with the least simulation time. In order to overcome existing issues, an artificial neural-inspired whale optimization is proposed to provide a reliable solution for healthcare applications. In this work, two models are proposed one for reliability estimation and the other is based on whale optimization technique and neural network-based binary classifier. The predictive model enhances the quality of service using performance metrics, makespan, least average task completion time, resource usages cost and utilization of the system. From results as compared to existing algorithms the proposed ANN-WHO algorithms prove to improve the average start time by 29.3%, average finish time by 29.5% and utilization by 11%.

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