Auto Scaling Policies


AWS Auto Scaling Policies
1m 8s
Auto Scaling Policies

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.

Learning Objectives

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.

Intended Audience

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.


Autoscaling is one of those key fundamental pieces that everyone will need for their elastic and fault-tolerant architecture. Well, at least the architectures that need servers and what have you. It is the glue that makes the well-oiled machine work. 

Autoscaling is also one of those AWS things that we take for granted. People just assume they will have it as part of their architectures, and sort of handwave it away to deal with at another time. They assume that someone else will set up the policies correctly, and everything will work as intended. However, autoscaling has a number of complexities that can make a huge difference in the quality and consistency of its performance; and that is what we are here to talk about today.

There are basically four different ways you can manipulate and modify the number of instances within an autoscaling group. 

You can Manually adjust the variables - modifying the top and bottom bounds, or the desired amount. 

You can Schedule scaling to happen at certain times of the day - adding or subtracting instances as needed. 

There is also Dynamic scaling, which is automatic and adds or removes instances as required. 

Finally, there is Predictive scaling, this uses machine learning to understand your average loads and provisions your instances based on training data.

Each of these methods have their pros and cons, and it's important to understand when and where they have their place.

Let’s start this lesson by looking over why you would want to manually scale your auto scaling groups.


About the Author

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.