Optimize Costs for Google Kubernetes Engine
Advanced 12 Steps 1 day 35 Credits
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.
Prerequisites:This quest explores intermediate to advanced concepts about Google Kubernetes Engine cluster optimization and assumes the student already knows basic concepts around cluster creation and management. If you are new to Google Kubernetes Engine, it's recommended to first take the Kubernetes in Google Cloud quest.
This lab explores best practices in managing and monitoring a multi-tenant cluster in order to optimize your costs.
In this hands-on lab, you’ll 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.
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.
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.
This lab offers a series of challenges that involve deploying, scaling, and maintaining a cluster application while optimizing resource usage.