Gke Multidimensional Autoscaling Hpa Vpa Ca

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You are architecting an autoscaling strategy for a latency-critical event processing application deployed on Google Kubernetes Engine (GKE) Standard. The application experiences sudden spikes in message volume from a Google Cloud Pub/Sub subscription and variable memory usage during payload deserialization.

You need to scale the container replicas based on queue depth, optimize container resource sizing without metric contention, and ensure the underlying cluster infrastructure dynamically provisions compute capacity when Pods cannot be scheduled.

Which autoscaling configuration should you implement?

Configure the Horizontal Pod Autoscaler (HPA) to scale on average CPU utilization percentage, configure the Vertical Pod Autoscaler (VPA) in Auto mode to adjust CPU requests, and enable Compute Engine Managed Instance Group autoscaling directly on the node pool.

Configure the Horizontal Pod Autoscaler (HPA) to scale on custom memory utilization percentage without specifying container resource requests, and configure Cluster Autoscaler to monitor real-time node memory utilization.

Define explicit container CPU and memory resource requests, configure the Horizontal Pod Autoscaler (HPA) using an External metric from Pub/Sub, allow Vertical Pod Autoscaler (VPA) to adjust memory resources, and enable GKE Cluster Autoscaler on the node pools.

Define container resource limits without setting requests, configure HPA to scale on Pub/Sub metrics, and enable both GKE Cluster Autoscaler and Compute Engine autoscaling policies simultaneously on the underlying MIG.

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Gke Multidimensional Autoscaling Hpa Vpa Ca

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