Predicting locally manageable resource failures of high availability clusters

Abstract

Critical services from domains as diverse as finance, manufacturing and healthcare are often delivered by complex enterprise applications (EAs). High-availability clusters (HACs) are software-managed IT infrastructures that enable these EAs to operate with minimum downtime. This paper presents a novel Bayesian decision network model to improve the failure detection capabilities of the HACs components using a comprehensive set of characteristics for the analysed component. The model then combines these characteristics to predict whether the failure of this component can be managed locally at the failed component level without propagating the failure to upper-level components and causing a complete system failure. By improving the detection capabilities and predicting locally manageable failures, the model improves the decision-making process of HACs, and has the potential to reduce the downtime and improve availability for the applications protected by HACs. The model uses the capabilities of the Bayesian decision networks, which combines Bayesian networks with the utility theory, to assign weights to different characteristics and consolidate the related variables to output the result. The model evaluation in a realistic testbed environment with three servers, an established HAC and a well-known EA shows that the model can improve the area under the Receiver Operating Characteristic (ROC) curve for prediction of locally manageable failures by up to 9.05% compared to the baseline HAC results.