Information-theoretic modeling for tracking the health of complex software systems

Abstract

Stable correlation models are effective in detecting errors in complex software systems. However, most studies assume a specific mathematical form, typically linear, for the underlying correlations. In practice, more complex non-linear relationships exist between metrics. Moreover, most inter-metric correlations form clusters rather than simple pairwise correlations. These clusters provide additional information for error detection and offer the possibility for optimization. We address these issues by adopting the Normalized Mutual Information as a similarity measure. We also employ the entropy of metrics in clusters to monitor system state. Our approach does not require learning specific correlation models, thus reducing computation overhead.