About the Dynamic Thresholds app

Note

The Dynamic Thresholds app is now available in beta release. You can expect frequent releases as new features are introduced as well as provide feedback as a customer during feature development.

Use the Dynamic Thresholds app to set up and manage dynamic thresholds for various metrics. The app leverages a deviation model that learns the historical behavior of your metrics, automatically establishing and adjusting flexible upper and lower boundaries. The app also offers:

• Configurable warning and critical severity levels for alert escalation.
• Adjustable training period to fine-tune adaptability.
• Noise reduction through metric smoothing and configurable alert delays.

Dynamic Thresholds is designed to combat alert fatigue by providing adaptive, data-driven anomaly detection. This ensures that notifications are focused on statistically significant deviations, allowing you to monitor your systems more effectively and respond only to genuine anomalies.

From the app’s main screen, you can:

• View a list of all existing dynamic threshold configurations.
• Edit an existing dynamic threshold configuration by clicking on a row.
• See the enabled/disabled status of each configuration. You can toggle the Enabled checkbox of a configuration directly from the table to activate or deactivate a configuration.
• Identify the name, metric, group, and model type for each configuration.

Define a new dynamic threshold configuration Copied

1. From the app’s main screen, click the Add New Dynamic Threshold button.

2. On the Source metric dropdown, select a specific metric to apply dynamic thresholds to. You can further filter the data for the selected metric.

3. Enable or disable Upper Thresholds or Lower Dynamic Thresholds. You need to select at least one option.

4. Drag the sliders for Warning and Critical levels to set your desired deviations above or below a certain value for your selected thresholds.

5. Adjust the Training window slider, where a higher value requires a larger deviation to trigger, while a lower value means it is more sensitive.

6. Adjust the Refresh frequency slider to determine how often the dynamic threshold model re-evaluates and updates its thresholds based on new data.

7. Apply Noise reduction to the threshold evaluation.

   Tip

   Choose **Alert delay** if you want to filter out fluctuations and reduce false positives, but be aware that there might be a slight delay in actual alert notification. You can configure for an alert to only be triggered if the threshold breach persists for a specified duration.
   

8. On the Dry run results panel, preview the dynamic threshold behavior with selected metrics and configurations. You can also search for dimensions and review the results.

9. Click Save to add your new dynamic threshold configuration.