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An online retail company hosts its core ordering service on Google Kubernetes Engine (GKE). The application experiences predictable, massive traffic spikes every morning at the start of business hours, followed by minimal activity overnight.
To ensure responsiveness during the morning rush, the team currently maintains a high static minReplicas value in the Horizontal Pod Autoscaler (HPA) configuration. However, management noticed that this setting prevents the cluster from scaling down during off-peak night hours, resulting in substantial unnecessary infrastructure costs.
You need to design a scaling architecture that handles the predictable morning surges without overpaying for idle capacity overnight.
Which scaling strategy should you implement?
Keep the high static HPA minReplicas configuration and set the GKE cluster node pool autoscaler minimum node limit to zero.
Configure the Vertical Pod Autoscaler (VPA) in Auto mode to increase Pod CPU and memory requests overnight while disabling the Horizontal Pod Autoscaler.
Set the HPA minReplicas to 1 and rely strictly on reactive target CPU utilization thresholds to scale up instances when the morning traffic arrives.
Implement scheduled autoscaling by exporting time-based custom metrics to Cloud Monitoring and configuring the HPA with the Custom Metrics adapter to scale pods proactively before business hours.
Keep the high static HPA minReplicas configuration and set the GKE cluster node pool autoscaler minimum node limit to zero.
Configure the Vertical Pod Autoscaler (VPA) in Auto mode to increase Pod CPU and memory requests overnight while disabling the Horizontal Pod Autoscaler.
Set the HPA minReplicas to 1 and rely strictly on reactive target CPU utilization thresholds to scale up instances when the morning traffic arrives.
Implement scheduled autoscaling by exporting time-based custom metrics to Cloud Monitoring and configuring the HPA with the Custom Metrics adapter to scale pods proactively before business hours.
Scheduled autoscaling on Google Kubernetes Engine combines standard Kubernetes autoscaling mechanisms with time-based triggers. Because standard Horizontal Pod Autoscaling (HPA) reacts dynamically to resource metrics like CPU or memory utilization, it can suffer from a slight lag during sudden, sharp traffic surges. By utilizing Cloud Monitoring custom metrics alongside scheduled jobs (such as CronJobs), you can programmatically publish metric values at specific times to scale application replicas up ahead of business hours and down during low-traffic windows.
This solution directly balances performance and cost. Rather than keeping expensive infrastructure running all night, scheduled custom metrics anticipate regular traffic spikes while letting standard dynamic scaling handle unexpected daytime fluctuations.
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