Bayesian Model-Based Prediction of Service Level Agreement Violations for Cloud Services

Abstract

Cloud SLAs are contractually binding agreements between cloud service providers and cloud consumers. For cloud service providers, it is essential to prevent SLA violations as much as possible to enhance customer satisfaction and avoid penalty payments. Therefore, it is desirable for providers to predict possible violations before they happen. We propose an approach for predicting SLA violations, which uses measured datasets (QoS of used services) as input for a prediction model. As a feature of cloud service, we consider response-time to predict violations of SLA. The prediction model is based on Naive Bayesian Classifier, and trained using historical SLA datasets. We present the basics of our prediction approach, and also determine the most effective combinations of features for prediction, and briefly validate our approach, using a detailed real SLA datasets of cloud services. Experiments result show that the Bayesian method achieves higher accuracy compared with other prediction methods.