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Observability
Observability
11 Mar 2025
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Gözlemlenebilirliği Optimize Etme

Gözlemlenebilirliği Optimize Etmek: İzleme Gürültüsünü Aşmak

Managing alerts from monitoring systems is one of the biggest operational challenges for IT teams. Due to incorrect threshold values and insufficient filtering mechanisms, false-positive alerts can cause teams to overlook critical incidents. Over time, these alerts become too frequent, leading to alert fatigue as a natural response from the team.

Understanding the Sources of Alert Noise

In IT infrastructures where multiple monitoring tools are used, the primary factors causing alert noise are:

  • Incorrect or low threshold values: For example, instead of generating an alert when CPU usage spikes to 90% for a few seconds, it is more logical to wait until it remains at that level for a certain duration.

  • Repeated alerts from different monitoring tools for the same event: For instance, if both Prometheus and Datadog send separate warnings for the same incident, these alerts should be consolidated.

  • Unnecessary or outdated rules: As the IT environment evolves, failing to update monitoring rules may result in alerts that are no longer relevant.


Strategies to Reduce Noise

a) Alert Filtering and Prioritization

With Parny’s Alarm Rules Engine, you can filter alerts from monitoring tools using regex or text matching. This allows you to:

  • Automatically ignore insignificant events.

  • Ensure that only critical events generate alerts.

  • Prioritize specific alerts and direct escalations through designated personnel.

  • Modify communication channels automatically, sending alerts via phone, SMS, or push notifications for critical situations.

  • Add priority tags to alerts to establish more effective prioritization.

By implementing these filtering mechanisms, your team can focus only on truly important alerts and eliminate unnecessary notification fatigue.


Adding New Rule


b) Preventing False-Positive Alerts

To minimize false positives, methods such as adaptive thresholding or dynamic anomaly detection can be applied.

For example:

  • Instead of setting a fixed CPU usage threshold, you can create threshold values that adjust based on historical usage trends.

  • Merge similar incidents to prevent duplicate alerts.

The goal is not just to reduce the number of alerts from monitoring systems, but to deliver the right alerts to the right people. By applying the above methods, you can gradually enhance your monitoring processes as part of your weekly work plans. However, it is important to remember that this is not a one-time task but rather a continuously improving process.