An Automated Self-Healing Cloud Computing Framework for Resource Scheduling

• Published in: International journal of grid and high performance computing Vol. 13; no. 1; pp. 47 - 64
• Main Authors: Choudhury, Tanupriya, Dewangan, Bhupesh Kumar, Venkatadri M, Agarwal, Amit, Pasricha, Ashutosh
• Format: Journal Article
• Language: English
• Published: Hershey IGI Global 01.01.2021
• Subjects: Adaptation; Algorithms; Automation; Breakdowns; Cloud computing; Collaboration; Distributed processing; Fault tolerance; High performance computing; Middleware; Performance measurement; Resource scheduling; Scheduling; Task scheduling; India
• ISSN: 1938-0259; 1938-0267
• DOI: 10.4018/IJGHPC.2021010103

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.