document: 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 respectively . Instead of tracking the original data , we use a sequentially discounting expectation maximization ( SDEM ) algorithm to learn the distribution of the two extracted statistics . A failure event can then be detected based on the abnormal change of the distribution . Applying our technique to component interaction data in a simple e-commerce application shows better accuracy than building independent profiles for each component . Additionally , experiments on synthetic data show that the detection accuracy is high even for changing systems .
doc_bio_tags: B I O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O