To detect weekly seasonality, at least three weeks of historical data are required. Other patterns like bi-hourly or semi-weekly seasonality might not be detected. The thresholds are selected in such a way that a deviation from these thresholds indicates an anomaly in the metric behavior.ĭynamic Thresholds can detect seasonality for hourly, daily, or weekly patterns. It detects patterns in the data such as seasonality (Hourly / Daily / Weekly), and is able to handle noisy metrics (such as machine CPU or memory) as well as metrics with low dispersion (such as availability and error rate).
How are the thresholds calculated?ĭynamic Thresholds continuously learns the data of the metric series and tries to model it using a set of algorithms and methods.
Learn more about how to configure Metric Alerts. How to configure alerts rules with Dynamic Thresholds?Īlerts with Dynamic Thresholds can be configured through Metric Alerts in Azure Monitor.
Intuitive Configuration – Dynamic Thresholds allows setting up metric alerts using high-level concepts, alleviating the need to have extensive domain knowledge about the metric. The ML algorithm used in Dynamic Thresholds is designed to prevent noisy (low precision) or wide (low recall) thresholds that don’t have an expected pattern. Adapting to the metrics’ behavior over time and alerting based on deviations from its pattern relieves the burden of knowing the "right" threshold for each metric. Smart Metric Pattern Recognition – Using our ML technology, we’re able to automatically detect metric patterns and adapt to metric changes over time, which may often include seasonality (hourly / daily / weekly). Learn more about how to configure Metric Alerts with Dynamic Thresholds using templates. The scalable approach is especially useful when dealing with metric dimensions or when applying to multiple resources, such as to all subscription resources. You can use either Azure portal or the Azure Resource Manager API to create them. They give you fewer alerts to create and manage. Scalable Alerting – Dynamic threshold alert rules can create tailored thresholds for hundreds of metric series at a time, yet providing the same ease of defining an alert rule on a single metric. We would love to hear your feedback, keep it coming at Why and when is using dynamic condition type recommended? Once an alert rule is created, it will fire only when the monitored metric doesn’t behave as expected, based on its tailored thresholds.
It provides support of both a simple UI and operations at scale by allowing users to configure alert rules through the Azure Resource Manager API, in a fully automated manner. Metric Alert with Dynamic Thresholds detection leverages advanced machine learning (ML) to learn metrics' historical behavior, identify patterns and anomalies that indicate possible service issues.