CONFIDENCE APPROXIMATION-BASED DYNAMIC THRESHOLDS FOR ANOMALOUS COMPUTING RESOURCE USAGE DETECTION

Embodiments described herein provide dynamic thresholds for alerting users of anomalous resource usage of computing resources. The dynamic thresholds are based on the historical behavior of compute metrics (or a time series obtained therefor) associated with the computing resources and a detected seasonality in that time series. Based on characteristics of the time series, a model for generating dynamic thresholds is determined. The dynamic thresholds track the detected seasonality of the compute metrics. As utilization of the computing resources continue, the determined thresholds are applied to the compute metrics. If the determined thresholds are exceeded, an alert indicating an anomalous resource usage is provided to a user. The dynamic threshold may be adjusted (e.g., tightened or relaxed) based on a confidence level of the detected seasonality. This advantageously reduces the number of false alerts.