Earn a skill badge by completing the Optimize Costs for Kubernetes Engine, where you learn about the following tools and techniques to help optimize resource usage and eliminate unnecessary costs on Google Kubernetes Engine (GKE): create and manage a multi tenant cluster, monitor resource usage by namespace, configure cluster and pod autoscaling, configure load balancing, and set up liveness and readiness probes. The videos and labs in this quest explore best practices for running cost-optimized Kubernetes applications on GKE.
A skill badge is an exclusive digital badge issued by Google Cloud in recognition of your proficiency with Google Cloud products and services and tests your ability to apply your knowledge in an interactive hands-on environment. Complete this skill badge quest, and the final assessment challenge lab, to receive a skill badge that you can share with your network.
Understanding your GKE Costs
Monitoring your GKE costs
Managing a GKE Multi-tenant Cluster with Namespaces
This lab explores best practices in managing and monitoring a multi-tenant cluster in order to optimize your costs.
Virtual machines in GKE
Exploring Cost-optimization for GKE Virtual Machines
In this hands-on lab, youâll learn how to determine and select the the most cost effective machine type for a GKE application. You will also explore the pros and cons of a multi-zonal cluster.
Autoscaling with GKE: Overview and pods
Autoscaling with GKE: Clusters and nodes
Understanding and Combining GKE Autoscaling Strategies
In this lab you will explore the benefits of different Google Kubernetes Engine autoscaling strategies, like Horizontal Pod Autoscaling and Vertical Pod Autoscaling for pod-level scaling, and Cluster Autoscaler and Node Auto Provisioning for node-level scaling.
Application optimization for GKE
Optimize GKE to run your application
GKE Workload Optimization
This lab demonstrates how optimization in your cluster's workloads can lead to an overall optimization of your resources and costs. It walks through a few different workload optimization strategies such as container native load balancing, application load testing, readiness and liveness probes, and pod disruption budgets.
Optimize Costs for Google Kubernetes Engine: Challenge Lab
This lab offers a series of challenges that involve deploying, scaling, and maintaining a cluster application while optimizing resource usage.