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An enterprise runs a stateful distributed database workload on Google Cloud where persistent volumes are experiencing rapid data growth. In past incidents, unexpected disk saturation led to read-only states and service disruptions.
You need to establish a proactive capacity planning and monitoring framework in Cloud Monitoring to track storage utilization trends, anticipate project storage quota constraints, and resize storage volumes before application availability is impacted.
Which strategy should you implement?
Deploy Database Center with Gemini Cloud Assist and rely exclusively on idle and over-provisioned cost recommendations to adjust database disk allocations.
Export all audit logs to BigQuery via a log sink and run scheduled daily SQL queries to detect when disk usage is projected to cross 95% within the next 24 hours.
Configure Cloud Monitoring alerting policies tracking kubernetes.io/pod/volume/utilization at an 85% threshold over a sustained duration, alongside project quota metrics, to trigger automated or proactive storage expansion workflows.
Configure alerting policies in Cloud Monitoring based solely on container/cpu/request_utilization crossing 85%, assuming storage capacity exhaustion directly mirrors CPU saturation trends.
Deploy Database Center with Gemini Cloud Assist and rely exclusively on idle and over-provisioned cost recommendations to adjust database disk allocations.
Export all audit logs to BigQuery via a log sink and run scheduled daily SQL queries to detect when disk usage is projected to cross 95% within the next 24 hours.
Configure Cloud Monitoring alerting policies tracking kubernetes.io/pod/volume/utilization at an 85% threshold over a sustained duration, alongside project quota metrics, to trigger automated or proactive storage expansion workflows.
This strategy implements proactive capacity tracking by monitoring storage metrics—specifically persistent volume utilization (kubernetes.io/pod/volume/utilization)—in Cloud Monitoring alongside Google Cloud project quota consumption. When volume utilization or quota allocations cross predefined thresholds, automated expansion workflows or engineering escalations are triggered well in advance of disk exhaustion.
StudyCerts
StudyCertskubernetes.io/pod/volume/utilization metric at an 85% threshold over a multi-minute duration (such as 5 minutes) identifies sustained capacity growth rather than temporary, transient spikes.Monitoring actual storage volume utilization metrics directly in Cloud Monitoring provides real-time alerting with configurable aggregation windows and durations. This ensures rapid, automated intervention before physical disk or quota saturation causes application downtime.
Configure alerting policies in Cloud Monitoring based solely on container/cpu/request_utilization crossing 85%, assuming storage capacity exhaustion directly mirrors CPU saturation trends.