Authors: Archana Pandita; Prabhat Kumar Upadhyay; Nisheeth Joshi
Addresses: Department of Computer Science and Engineering, Birla Institute of Technology, Ras Al Khaimah, UAE ' Department of Electrical and Electronics Engineering, Birla Institute of Technology, Mesra, India ' Department of Computer Science, Banasthali University, Rajasthan, India
Abstract: The cloud has to commit with service level agreements (SLA) which ensures a specific level of performance and sets a penalty if SLA is violated by the provider. These days, managing and applying penalties have become an essential and critical issue for cloud computing. In this research, adaptive neuro-fuzzy inference system (ANFIS) is used to develop proactive fault prediction model which has been designed by utilising the power of machine learning and tested on the datasets to highlight the accurate models for faults prediction in the cloud environment. The suggested algorithm has achieved a percentage accuracy of 99.3% in detecting violations. The performance of the proposed model has been compared with Bayesian regularisation and scaled conjugate gradient methods which reflect the facts as obtained from the results that effectiveness of this scheme in terms of predicting system's violation is more effective.
Keywords: adaptive neuro-fuzzy inference system; ANFIS; cloud computing; cloud service; machine learning; service level agreement; SLA; violation; quality of service; QoS; prediction.
International Journal of Cloud Computing, 2022 Vol.11 No.4, pp.389 - 406
Received: 15 Feb 2020
Accepted: 31 Aug 2020
Published online: 09 Aug 2022 *