Multi-Level Event and Anomaly Correlation Based on Enterprise Architecture Information

Growing IT landscapes in and among enterprises face the challenge of increasing complexity, which complicates root cause analysis and calls for automated support. This paper presents an approach to correlate events, e.g. anomalies in multi-level monitoring stream data, for instance conversion rates or network load monitoring. Events, e.g. operational activities like application deployments and marketing activities can be taken into account, too. We exploit an Enterprise Architecture documented as a graph to focus on those correlations, where relationships are already known. Therefore, different data source types are identified. We present a minimal prototypical implementation called MLAC that shows first results of the feasibility of the approach, in particular to correlate events and level shift anomalies in an artificial web-shop setup. It includes a dynamic visualization of the correlations in the EA graph.