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An e-commerce company runs an order-processing backend on Google Compute Engine instances. The CPU utilization metric is sampled every 60 seconds. During normal operation, background memory garbage collection creates transient CPU utilization spikes of up to 90% that resolve within 45 seconds.
The operations team currently suffers from alert fatigue due to frequent false alarms triggered whenever a single measurement exceeds 85% CPU utilization. You need to configure a Cloud Monitoring metric-threshold alerting policy that ignores these transient spikes and only alerts when CPU utilization remains continuously above 85% for at least 5 minutes.
How should you configure the condition settings in Cloud Monitoring?
In Google Cloud Observability (Cloud Monitoring), a metric-threshold condition relies on an alignment period (rolling window), an aligner function, and a retest window (duration). The alignment period groups raw metric samples into discrete time intervals and applies a mathematical function (such as mean or max) to produce regularized time-series values. The retest window enforces a temporal duration requirement, dictating that all aligned values within that window must continuously violate the threshold before Cloud Monitoring opens an incident and dispatches notifications.
mean aligner calculates the representative average utilization over each sample window.Configuring a 1-minute alignment period alongside a 5-minute retest window directly addresses the root cause of alert fatigue. It ensures that only prolonged, severe CPU saturation triggers on-call pages while allowing self-healing, transient tasks to execute harmlessly.
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