Optimize Costs for Google Kubernetes Engine

Optimize Costs for Google Kubernetes Engine

Google BDL-OCGKE

USD 39.60

Complete the Optimize Costs for Kubernetes Engine quest, 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.

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USD 39.60

Managing a GKE Multi-tenant Cluster with Namespaces

Module 1

Managing a GKE Multi-tenant Cluster with Namespaces

Managing a GKE Multi-tenant Cluster with Namespaces

Google LIS-GOOGLE-2391
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
This lab explores best practices in managing and monitoring a multi-tenant cluster in order to optimize your costs.
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Exploring Cost-optimization for GKE Virtual Machines

Module 2

Exploring Cost-optimization for GKE Virtual Machines

Exploring Cost-optimization for GKE Virtual Machines

Google LIS-GOOGLE-2396
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
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.
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Understanding and Combining GKE Autoscaling Strategies

Module 3

Understanding and Combining GKE Autoscaling Strategies

Understanding and Combining GKE Autoscaling Strategies

Google LIS-GOOGLE-2397
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
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.
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GKE Workload Optimization

Module 4

GKE Workload Optimization

GKE Workload Optimization

Google LIS-GOOGLE-2402
USD 9.90
Google
Google Cloud Self-Paced Labs
English
30 days
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.
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