Failure detection and localization in component based systems by online tracking

The increasing complexity of today’s systems makes fast and accurate failure detection essential for their use in mission-critical applications. Various monitoring methods provide a large amount of data about system’s behavior. Analyzing this data with advanced statistical methods holds the promise of not only detecting the errors faster, but also detecting errors which are difficult to catch with current monitoring tools. Two challenges to building such detection tools are: the high dimensionality of observation data, which makes the models expensive to apply, and frequent system changes, which make the models expensive to update. In this paper, we present algorithms to reduce the dimensionality of data in a way that makes it easy to adapt to system changes. We decompose the observation data into signal and noise subspaces. Two statistics, the Hotelling T2 score and squared prediction error (SPE) are calculated to represent the data characteristics in signal and noise subspaces ...

• Type: Conference
• Year: 2005
• Where: KDD
• Authors: Haifeng Chen, Guofei Jiang, Cristian Ungureanu, Kenji Yoshihira
• Keywords: Data Mining, Failure Detection, KDD 2005, Noise Subspaces, Observation Data