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  • Brock Peterson

Aria Operations Anomalies and Anomalous Metrics

If you've used the Aria Operations Troubleshooting Workbench you know there is a column for Anomalous Metrics.

Hovering over the Anomalous Metrics column header gives you a description: Ranked set of metrics which have shown patterns of drastic change within the selected scope and time. Well, what does that mean? Thes best description I've found for Anamolous Metrics was described here by Senior Staff Technical Marketing Manager John Dias. He described them as follows:

"Anomalous Metrics are statistically significant changes detected for all objects in scope during the selected time range. Metrics are considered anomalous if they result in a change of the mean of the datapoints for each window (i.e. not a single, large spike). Detection of anomalous metrics is done by analyzing data points through a sliding window, which is 1/4th of the time range.

Note that Anomalous Metrics and Dynamic Threshold (DT) violations are distinctly different. Anomalous metrics are not based on any historical behavior outside of the time range, and they also represent a significant change in the metric, not a momentary spike, as a DT violation might. Both are helpful in troubleshooting if you understand what they are showing."

You might also know there is a metric against each object called Total Anomalies, what are these?

Total Anomalies represent the number of metrics on that object (and its children) that have active symptoms or DT violations, it is not a symptom count but a metrics count. Keep that in mind when comparing Anomalous Metrics to Total Anomalies.


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