Causal analysis for ML-based Anomaly

Container Observability provides an automated RCA that is triggered if a problem is detected for ML-based anomalies or event failure.

The Fishbone RCA figure provides information on the current problem, configuration, and events. It also provides information based on the historical data-based dependencies and explanations from the behavior model.

The bones indicate the metrics, configurations, and resources of containers and their related categories.

The bone highlighted in red color indicates the causes of why the anomaly was detected.

It further helps you to understand the possible cause(s) of the failure or problem suggesting necessary remedial actions that can be taken to resolve the problem.

The Fishbone figure helps with the root cause details for the anomaly detected by the ML in the system.

The bones in the figure represent the related categories and configurations of the “my-sql” App Object.

The red lines highlighted indicate the cause of anomalies detected by ML in the system compared to its set of Key-Metrics captured every 24 hours. It also provides information based on the historical data-based dependencies and explanations from the behavior model.

Click on the bones highlighted with red color. A pop-up chart will display the exact cause of the anomaly and metric chart widgets to analyze the affected metric behavior.

Causal Analysis

Machine Learning (ML) alerts