Heteroscedastic models to track relationships between management metrics

Modern software systems expose management metrics to help track their health. Recently, it was demonstrated that correlations among these metrics allow faults to be detected and their causes localized. In particular, linear regression models have been used to capture metric correlations. We show that for many pairs of correlated metrics in software systems, such as those based on Java Enterprise Edition (JavaEE), the variance of the predicted variable is not constant. This behaviour violates the assumptions of linear regression, and we show that these models may produce inaccurate results. In this paper, leveraging insight from the system behaviour, we employ an efficient variant of linear regression to capture the non-constant variance. We show that this variant captures metric correlations, while taking the changing residual variance into consideration. We explore potential causes underlying this behaviour, and we construct and validate our models using a realistic multi-tier enterprise application. Using a set of 50 fault-injection experiments, we show that we can detect all faults without any false alarm.