Fault detection and localization in distributed systems using invariant relationships

Recent advances in sensing and communication technologies enable us to collect round-the-clock monitoring data from a wide-array of distributed systems including data centers, manufacturing plants, transportation networks, automobiles, etc. Often this data is in the form of time series collected from multiple sensors (hardware as well as software based). Previously, we developed a time-invariant relationships based approach that uses Auto-Regressive models with eXogenous input (ARX) to model this data. A tool based on our approach has been effective for fault detection and capacity planning in distributed systems. In this paper, we first describe our experience in applying this tool in real-world settings. We also discuss the challenges in fault localization that we face when using our tool, and present two approaches - a spatial approach based on invariant graphs and a temporal approach based on expected broken invariant patterns - that we developed to address this problem.