This course explores the various auto scaling policies that exist within AWS. We'll cover what each of the policies do, their strengths and weaknesses, and when best to use them. Understanding the ins and outs of these policies will help you save a lot of money and keep your customers happy by removing latency and downtime.
By the end of this course, you will understand how each of the AWS auto scaling policies works and in what situations they perform best.
This course is recommended for solutions architects and developers who are working on creating highly available systems within AWS.
To get the most out of this course, you should already have a basic working knowledge of AWS.
Understanding how the various autoscaling policies work and function together will be a great boon to any architecture.
Manual scaling, for example, allows you to get ahead of an event you know is coming. This is perfect for when you are about to run a TV advert, and you know thousands of new visitors might be checking out your site.
Dynamic auto scaling works wonders for the day-to-day scaling of your infrastructure. Your system can reactively adjust to the load placed on it. It will deploy and retire instances as needed based on tracking metrics you have assigned.
We also have the ability to try and look into the future with predictive auto scaling. This policy uses machine learning to forecast future needs and deploys instances ahead of time. This can help reduce issues before they happen.
And finally, we talked about scheduled scaling. This policy has a fit within many architectures and businesses that have strict time periods of work. With scheduled scaling you can make sure non-critical systems are online and offline when they need to be, saving money by reducing waste.
Anywhoo, that brings us to the end of this course. My name is Will Meadows and I'd like to thank you for spending your time here learning about auto scaling policies. Hopefully, you have a better understanding of when to use each type of policy, or even combinations of policies.
If you have any feedback, positive or negative, please contact us here at email@example.com. Cheers!
William Meadows is a passionately curious human currently living in the Bay Area in California. His career has included working with lasers, teaching teenagers how to code, and creating classes about cloud technology that are taught all over the world. His dedication to completing goals and helping others is what brings meaning to his life. In his free time, he enjoys reading Reddit, playing video games, and writing books.