Online Tracking of Component Interactions for Failure Detection and Localization in Distributed Systems

This paper proposes a novel failure-detection approach that can handle high-dimensional observation and frequent system changes. We extract two statistics from the subspace decomposition of observations, and use the mixture of Gaussians to model their probability density. Instead of monitoring the original data, the density model of extracted statistics is adaptively updated and examined regularly to detect failures. We also present a localization method to identify the faulty components once the failure happens. Applying our technique to monitor the component interactions in an e-commerce application shows satisfactory results in detecting a variety of injected failures.