Detecting correlation between server resources for system management

Efficient system management requires continuous knowledge about the state of system and application resources that are typically represented through time series obtained by monitors. Capacity planning studies, forecasting, state aggregation, anomaly and event detection would be facilitated by evidence of data correlations. Unfortunately, the high variability characterizing most monitored time series affects the accuracy and robustness of existing correlation solutions. This paper proposes an innovative approach that is especially tailored to detect linear and non-linear correlation between time series characterized by high variability. We compare the proposed solution and existing algorithms in terms of accuracy and robustness for several synthetic and real settings characterized by low and high variability, linear and non-linear correlation. The results show that our proposal guarantees analogous performance for low variable time series, and improves state of the art in finding correlations in highly variable domains that are of interest for the application context.