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Google Associate Cloud Engineer: How Can You Optimize Resource Limits for Microservices in GKE?

By: Author Alex Lim

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Learn the best practice for right-sizing memory and CPU limits for hundreds of microservices running in a Google Kubernetes Engine (GKE) cluster.

Table of Contents

Question

You have several hundred microservice applications running in a Google Kubernetes Engine (GKE) cluster. Each microservice is a deployment with resource limits configured for each container in the deployment. You’ve observed that the resource limits for memory and CPU are not appropriately set for many of the microservices. You want to ensure that each microservice has right sized limits for memory and CPU. What should you do?

A. Configure a Vertical Pod Autoscaler for each microservice.
B. Modify the cluster’s node pool machine type and choose a machine type with more memory and CPU.
C. Configure a Horizontal Pod Autoscaler for each microservice.
D. Configure GKE cluster autoscaling.

Answer

A. Configure a Vertical Pod Autoscaler for each microservice.

Explanation

In this scenario, you’re dealing with a large number of microservices running in a GKE cluster, and you’ve noticed that the resource limits for memory and CPU are not appropriately set for many of these microservices. The goal is to ensure that each microservice has right-sized limits for memory and CPU.

The Vertical Pod Autoscaler (VPA) is the ideal solution for this problem. Here’s why:

  1. Purpose of VPA: The Vertical Pod Autoscaler is designed to automatically adjust the CPU and memory reservations for your pods to help “right-size” your applications. It analyzes the historical and current CPU and memory usage of your pods and can automatically update the resource requests of your pods vertically.
  2. Automatic Optimization: VPA can learn the resource usage patterns of your microservices over time and adjust the resource requests accordingly. This ensures that each microservice gets the resources it needs without over-provisioning.
  3. Efficiency: By right-sizing the resource limits, VPA helps improve cluster resource utilization and can potentially reduce costs by avoiding over-allocation of resources.
  4. Individual Microservice Handling: VPA can be configured for each microservice independently, allowing for fine-grained control over resource allocation.

Let’s examine why the other options are not as suitable:

B. Modifying the cluster’s node pool machine type: This is a cluster-wide change that doesn’t address the individual needs of each microservice. It could lead to over-provisioning and increased costs.

C. Configuring a Horizontal Pod Autoscaler: While HPA is useful for scaling the number of pod replicas based on CPU or custom metrics, it doesn’t adjust the resource limits of individual pods, which is what’s needed in this scenario.

D. Configuring GKE cluster autoscaling: Cluster autoscaling adjusts the number of nodes in your cluster based on pod scheduling needs, but it doesn’t address the resource allocation within pods.

In conclusion, the Vertical Pod Autoscaler (Option A) is the most appropriate solution for ensuring that each microservice in your GKE cluster has right-sized limits for memory and CPU. It provides an automated, efficient, and granular approach to resource optimization for your microservices.

Google Associate Cloud Engineer certification exam practice question and answer (Q&A) dump with detail explanation and reference available free, helpful to pass the Google Associate Cloud Engineer exam and earn Google Associate Cloud Engineer certification.