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Difficulty: EasyDeploying and Managing Google Kubernetes Engine (GKE) Clusters and Workloads

A cloud engineer manages a stateless application deployed as a Kubernetes Deployment on a Google Kubernetes Engine (GKE) cluster. During peak business hours, CPU usage increases significantly. The engineer needs to automatically scale the number of running application Pods up or down based on CPU utilization metrics. Which Kubernetes resource should the engineer configure?

  1. HorizontalPodAutoscalerAnswer
  2. B
    Cluster Autoscaler
  3. C
    GKE Autopilot node allocation policy
  4. D
    Spot Node Pool with auto-provisioning

Answer

The HorizontalPodAutoscaler (HPA) resource should be configured to scale the number of Pod replicas based on CPU utilization.
The HorizontalPodAutoscaler (HPA) automatically adjusts the number of Pod replicas in a Deployment based on targeted metrics such as CPU utilization.

Step-by-Step Solution

1
Identify the requirement
The requirement is to automatically scale the number of application Pods (workload replicas) in response to CPU utilization metrics.
Scaling Pod count requires a workload-level autoscaler rather than node-level infrastructure management.
2
Evaluate Kubernetes scaling mechanisms
HorizontalPodAutoscaler monitors Pod metrics (like target CPU utilization percentage) and adjusts the Deployment's replica count accordingly.
HPA is specifically designed for scaling container workloads horizontally.

Key Concept

Horizontal Pod Autoscaler vs. Cluster Autoscaler in GKE
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Topics

Deploying and Managing Google Kubernetes Engine (GKE) Clusters and Workloads