Case Studies of Causal Discovery from IT Monitoring Time Series
It addresses the challenge of improving IT system reliability and performance for businesses, but the approach is incremental as it applies existing causal discovery methods to new domain-specific data.
This paper tackles the problem of applying causal discovery algorithms to IT monitoring time series data, which is complex due to issues like misalignment and missing values, and presents case studies that highlight benefits such as reducing downtime and identifying root causes, though no concrete numerical results are provided.
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.