Abstract
Service-level agreement (SLA) is a contract between the cloud service provider and consumer which includes terms and conditions of service parameters. The cloud service provider has to commit to service-level agreements, which ensures a specific quality of performance. A certain level of penalty is set if the provider performs SLA violations. Managing and applying penalties has become a critical issue for cloud computing. It is found to be of paramount importance that the violations are predicted well in advance so that the necessary measures can be taken. In this research work, various proactive SLA prediction models were designed, utilising the power of machine learning. We have used real-world data sets to highlight the accurate models for violation prediction in a cloud environment. Seeing violation prediction as a classification problem where incoming requests need to be predicted for the violation, we have used Bayesian regularised artificial neural network (BRANN) on different samples of real-world data set. Both the models show remarkable performance for predicting SLA violation. BRANN shows a significantly good average accuracy of 97.6%.
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Pandita, A., Upadhyay, P.K., Joshi, N. (2021). Prediction of Service-Level Agreement Violation in Cloud Computing Using Bayesian Regularisation. In: Hassanien, A., Bhatnagar, R., Darwish, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2020. Advances in Intelligent Systems and Computing, vol 1141. Springer, Singapore. https://doi.org/10.1007/978-981-15-3383-9_21
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