An Automated Self-Healing Cloud Computing Framework for Resource Scheduling

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Author(s): Bhupesh Kumar Dewangan (School of Engineering, Department of Computer Science and Engineering, O.P. Jindal University, India), Venkatadri M. (Amity University, India), Amit Agarwal (Dr. A. P. J. Abdul Kalam Institute of Technology, India), Ashutosh Pasricha (Schlumberger Asia Services Ltd., India)and Tanupriya Choudhury (University of Petroleum and Energy Studies, India)
Copyright: 2021
Volume: 13
Issue: 1
Pages: 18
Source title: International Journal of Grid and High Performance Computing (IJGHPC)
Editor(s)-in-Chief: Emmanuel Udoh (Sullivan University, USA)and Ching-Hsien Hsu (Asia University, Taiwan)
DOI: 10.4018/IJGHPC.2021010103

Keywords: Computer Science & IT / Grid & High Performance Computing / High Performance Computing / Information Science Reference

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Abstract

In cloud computing, applications, administrations, and assets have a place with various associations with various goals. Elements in the cloud are self-sufficient and self-adjusting. In such a collaborative environment, the scheduling decision on available resources is a challenge given the decentralized nature of the environment. Fault tolerance is an utmost challenge in the task scheduling of available resources. In this paper, self-healing fault tolerance techniques have been introducing to detect the faulty resources and measured the best resource value through CPU, RAM, and bandwidth utilization of each resource. Through the self-healing method, less than threshold values have been considering as a faulty resource and separate from the resource pool. The workloads submitted by the user have been assigned to the available best resource. The proposed method has been simulated in cloudsim and compared the multi-objective performance metrics with existing methods, and it is observed that the proposed method performs utmost.

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An Automated Self-Healing Cloud Computing Framework for Resource Scheduling

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