2V0-71.23 Question 292
Select 3You are deploying a mission-critical application on a Kubernetes cluster managed by VMware Tanzu. The application experiences varying levels of traffic throughout the day, requiring it to automatically scale based on resource usage. Which of the following configurations would enable autoscaling for this application effectively?
- A
Set up a Horizontal Pod Autoscaler (HPA) based on CPU utilization metrics.
- B
Deploy a Vertical Pod Autoscaler (VPA) to dynamically adjust pod CPU and memory requests.
- C
Manually monitor resource usage and scale pods using the kubectl scale command.
- D
Configure custom metrics in Kubernetes and integrate them with the Horizontal Pod Autoscaler (HPA).
- E
Enable Cluster Autoscaler to automatically add or remove worker nodes based on the cluster's resource demands.
Show answer and explanation
Correct answers: A, B, D
Explanation
To enable effective application autoscaling in a Kubernetes cluster managed by VMware Tanzu, you can use Horizontal Pod Autoscaler (HPA) for scaling pods based on metrics like CPU, memory, or custom-defined metrics. Additionally, Vertical Pod Autoscaler (VPA) ensures that individual pods have appropriate resource allocations to handle traffic variations. Custom metrics integration with HPA further enhances scaling capabilities. While Cluster Autoscaler is useful for managing worker node resources, it is not directly responsible for application-level autoscaling.
- A. Correct.
Correct. Horizontal Pod Autoscaler (HPA) allows the application to scale out (or in) by dynamically adjusting the number of pod replicas based on metrics like CPU or memory utilization.
- B. Correct.
Correct. Vertical Pod Autoscaler (VPA) adjusts resource requests like CPU and memory for individual pods, ensuring optimal performance during varying traffic loads.
- C. Incorrect.
Incorrect. Manually scaling pods may address resource demands temporarily, but it does not enable automatic scaling, which is the core requirement in this scenario.
- D. Correct.
Correct. Custom metrics can be configured to work with HPA, allowing the application to scale based on specific business or application metrics in addition to standard CPU/memory metrics.
- E. Incorrect.
Incorrect. While Cluster Autoscaler adjusts the number of worker nodes in the cluster, it does not directly handle application autoscaling. It is a complementary feature rather than a direct solution for this scenario.