Case Studies of Causal Discovery from IT Monitoring Time Series
Ali Aït-Bachir Charles K. Assaad Christophe de Bignicourt Emilie Devijver Simon Ferreira Eric Gaussier Hosein Mohanna Lei Zan
Information technology (IT) systems are vital for modern businesses, handling
data storage, communication, and process automation. Monitoring these systems
is crucial for their proper functioning and efficiency, as it allows collecting
extensive observational time series data for analysis. The interest in causal
discovery is growing in IT monitoring systems as knowing causal relations
between different components of the IT system helps in reducing downtime,
enhancing system performance and identifying root causes of anomalies and
incidents. It also allows proactive prediction of future issues through
historical data analysis. Despite its potential benefits, applying causal
discovery algorithms on IT monitoring data poses challenges, due to the
complexity of the data. For instance, IT monitoring data often contains
misaligned time series, sleeping time series, timestamp errors and missing
values. This paper presents case studies on applying causal discovery
algorithms to different IT monitoring datasets, highlighting benefits and
ongoing challenges.