Nature inspired fault tolerant task allocation in cloud computing using neural network model

Issue title: Soft Computing Applications

Guest editors: Valentina Emilia Balas

Article type: Research Article

Authors: Gupta, Punit^a | Rawat, Pradeep^{b; c} | Tripathi, Rajan Prasad^d | Mundra, Ankit^{e; *} | Mundra, Shikha^f | Goyal, Mayank Kumar^g | Kaur, Mandeep^g | Agarwal, Ruchi^h

Affiliations: [a] Department of Computer and Communication Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur, India | [b] Department of Computer Science & Engineering, Uttarakhand Technical University, Dehradun, India | [c] School of Computing, DIT University Dehradun, Dehradun, India | [d] Department of Information Technology and Engineering, Amity University, Tashkent, Uzbekistan | [e] Department of Information Technology, Manipal University Jaipur, Dehmi Kalan, Jaipur, India | [f] Department of Computer Science and Engineering, Manipal University Jaipur, Dehmi Kalan, Jaipur, India | [g] Department of Computer Science & Engineering, School of Engineering & Technology, Sharda University, Greater Noida, India | [h] Department of BCA, JIMS Engineering Management Technical Campus, Greater Noida, India

Correspondence: [*] Corresponding author. Ankit Mundra, Department of Information Technology, Manipal University Jaipur, Dehmi Kalan, Near GVK Toll Plaza, Jaipur, Rajasthan, India. E-mail: [email protected].

Abstract: Cloud computing in the current scenario comes with a large pool of resources, pay-per-use model and reliable infrastructure. Cloud optimization relies on resource optimization to improve the performance and reliability of the cloud. Fault in the cloud places an important role in defining the reliability of the cloud. The identification of fault is a challenging issue in a modular cloud environment. The researchers have developed various methods for the fault-aware scheduling of cloud resources. The fault-aware resource allocation includes static, dynamic, meta-heuristic, and learning-based approaches. In this article, we primarily focused on existing fault-aware resource allocation techniques and then we proposed a model that will primarily focus on fault forecast in tasks allocation. The projected model is based nature-inspired heuristic approach and intelligent artificial neural network. The fault-tolerant aware ANN-based proposed model focuses on performance improvement and reliability testing proactively. The proposed model surpasses the existing state of art methods for proactive and reactive fault-aware scheduling techniques in a large scale datacenter. The results and discussions section support the reliability assertion of the fault-tolerant aware human brain and nature-inspired model.

Keywords: ANN, Bat, cloud infrastructure, meta-heuristic, resource allocation

DOI: 10.3233/JIFS-219296

Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 2, pp. 1959-1968, 2022