This lab provides the basic hands-on experience of Amazon EC2 Auto Scaling -- setting up Auto Scaling to automatically launch compute instances in response to conditions that you specify. You will use Auto Scaling via the AWS console to create the basic infrastructure of a Launch Configuration and an Auto Scaling group. You will test the configuration by terminating a running instance and viewing the results as Auto Scaling responds by scaling up and starting another instance. For the lab to function as written, please DO NOT change the auto assigned region.
Dev Ops best practices make use of multiple deployments to manage application deployment scenarios. This lab provides practice in scaling and managing containers to accomplish common scenarios where multiple heterogeneous deployments are used.
This lab covers how to use AWS CloudFormation to provision a web application with a number of supporting AWS products and services, including Auto Scaling Groups, Amazon EC2 Instances, Elastic Load Balancers, and more. It also covers how to use AWS CloudFormation to manage infrastructure and reconfigure Auto Scaling Groups.
This lab helps you learn how to use the basic features of AWS OpsWorks, an application management service offered by AWS, to deploy a Ruby photo sharing application. This lab will show you how to use layers (instance blueprints that define the resources and software configuration for common technologies such as Ruby) to create new Amazon EC2 instances; deploy and update applications from common source repositories; use lifecycle events to automate tasks; scale the application using Elastic Load Balancing and time or load-based instance scaling; monitor instance metrics; and manage user permissions.
Scientists, developers, and other technologists from many different industries are taking advantage of AWS to perform big data analytics and meet the challenges of the increasing volume, variety, and velocity of digital information. AWS offers a portfolio of cloud computing services to help you manage big data by reducing costs, scaling to meet demand, and increasing the speed of innovation. In this quest, you’ll learn to work with advanced services for Big Data.
If you’re looking to take your Google Cloud application to the next level, look no further than Deployment Manager. By automating the creation of GCP resources and services, Deployment Manager lets you focus on developing rather than maintaining. In this advanced-level quest, you will get hands on practice with Deployment Manager by building custom templates, automating Python and Jinja application instances, and scaling custom networks.
In this advanced-level quest, you will learn how you to harness serious GCP power and infrastructure. The hands-on labs will give you use cases, and you will be tasked with implementing scaling practices utilized by Google’s very own Solutions Architecture team. From developing enterprise grade load balancing and autoscaling, to building continuous delivery pipelines, Google Cloud Solutions I: Scaling your Infrastructure will teach you best practices for taking your GCP projects to the next level.
This lab describes how to deploy an autoscaling Compute Engine instance group that is automatically scaled using a custom Stackdriver metric
This lab introduces the basics of Auto Scaling, highlighting multiple Auto Scaling use cases and the command-line tools used for Auto Scaling configuration. After completing this lab you will have configured and tested an elastic web farm which automatically scales capacity to accommodate load. In addition you will have explored a steady state use case in which Auto Scaling is used to maintain high availability of critical resources.