An e-commerce company runs an order processing application on Google Kubernetes Engine (GKE). The application experiences sudden, sharp traffic surges during flash sales and low activity overnight. The engineering team has identified the following requirements: The application must automatically scale the number of Pod replicas out and in based on incoming traffic volume to preserve responsiveness. Container CPU and memory resource requests must be continually right-sized over time to prevent over-allocation and optimize costs. The underlying VM node capacity must scale dynamically without manual intervention whenever new Pods cannot be scheduled due to insufficient compute resources. Autoscaling components must not compete or create scaling race conditions with one another. Which autoscaling architecture should you implement to satisfy these requirements?

Configure the Horizontal Pod Autoscaler (HPA) targeting custom traffic metrics, deploy the Vertical Pod Autoscaler (VPA) in recommendation mode to right-size Pod requests, and enable Cluster Autoscaler on the GKE node pools.: ### What this architecture is This architecture combines **Horizontal Pod Autoscaler (HPA)** for rapid workload horizontal scaling, **Vertical Pod Autoscaler (VPA)** in advisory mode for baseline resource right-sizing, and **Cluster Autoscaler (CA)** for dynamic node infrastructure provisioning. ### How it meets the requirements - **Responsive Workload Scaling**: HPA monitors traffic-based metrics (such as request rate) and automatically scales the number of Pod replicas up during spikes and down during quiet hours. - **Continuous Resource Right-Sizing**: Running VPA in `Off` (recommendation) mode analyzes historical CPU and memory utilization to provide optimal resource requests without restarting Pods dynamically or conflicting with HPA. - **Dynamic Node Provisioning**: Cluster Autoscaler monitors pending Pods that cannot be scheduled due to resource constraints and provisions new GKE worker nodes on demand, while deprovisioning underutilized nodes. - **Conflict Prevention**: Because HPA scales horizontally on traffic metrics while VPA operates in recommendation mode rather than actively mutating running Pod CPU/memory, race conditions and autoscaling loops between HPA and VPA are completely avoided. ### Key advantages and capabilities - **Multi-tiered autoscaling**: Seamless coordination between application layer elasticity and infrastructure layer scaling. - **Cost efficiency**: Prevents paying for idle VM instances overnight and eliminates over-provisioned container resource allocations. - **Stability**: Prevents autoscaler thrashing and ensures predictable scale-up behavior during flash sales. ### Why this is optimal This approach aligns with Google Cloud and Kubernetes best practices for multi-dimensional autoscaling. It ensures rapid horizontal elasticity under load while keeping infrastructure right-sized and preventing autoscaler metric collisions.

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Gke Automated Scaling Hpa Vpa Cluster Autoscaler

Section: Container orchestration

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An e-commerce company runs an order processing application on Google Kubernetes Engine (GKE). The application experiences sudden, sharp traffic surges during flash sales and low activity overnight. The engineering team has identified the following requirements:

The application must automatically scale the number of Pod replicas out and in based on incoming traffic volume to preserve responsiveness.
Container CPU and memory resource requests must be continually right-sized over time to prevent over-allocation and optimize costs.
The underlying VM node capacity must scale dynamically without manual intervention whenever new Pods cannot be scheduled due to insufficient compute resources.
Autoscaling components must not compete or create scaling race conditions with one another.

Which autoscaling architecture should you implement to satisfy these requirements?

Configure the Horizontal Pod Autoscaler (HPA) targeting custom traffic metrics, deploy the Vertical Pod Autoscaler (VPA) in recommendation mode to right-size Pod requests, and enable Cluster Autoscaler on the GKE node pools.

Configure the Horizontal Pod Autoscaler (HPA) based on CPU utilization and rely solely on the Horizontal Pod Autoscaler to trigger GKE node creation through compute engine instance groups.

Configure the Vertical Pod Autoscaler (VPA) in auto-update mode on CPU and memory, disable Horizontal Pod Autoscaler, and configure Bigtable autoscaling targets for node pools.

Configure both the Horizontal Pod Autoscaler (HPA) and Vertical Pod Autoscaler (VPA) with auto-update enabled on CPU utilization, and configure node auto-repair to scale GKE worker nodes.

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Gke Automated Scaling Hpa Vpa Cluster Autoscaler

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