Instructor: So are the backbone of Auto Scaling
is the AWS Auto Scaling Service.
So it's going to be available for all the
scalable resources in AWS.
So Auto Scaling groups obviously to allow you
to launch or terminate EC2 instances.
But also Spot Fleet requests,
to launch or terminate instances
from a Spot Fleet request itself.
And to automatically replace instances that get interrupted
for price or capacity reasons.
You have Amazon ECS that will be leveraging Auto Scaling
to adjust the ECS service desired count up and down.
DynamoDB tables for your tables or your global
secondary indexes to adjust the WCU and RCU.
So write capacity units and read capacity units over time.
And Aurora will be using Auto Scanning as well
for Dynamic Read Replica Auto Scaling, okay?
And obviously, maybe other services as well
will be added over time to the Auto Scaling service in AWS.
So we have Scaling Plans and it can be dynamic scaling.
We start knowing it already to track a target itself.
So without dynamic scaling,
you have the same capacity over time.
But with dynamic scaling,
you start adjusting the capacity over time.
And so you stabilize the utilization of your service itself.
So you can optimize for availability in which case
40% of your research utilization will be the target.
You can balance, and so this will be about 50% utilization.
Or if you want to optimize for cost,
then you will have 70% of resource utilization.
But obviously the closer you get to 100%,
the less efficient your scaling is going to be
because you're going to reach a performance bottleneck.
And obviously you can choose your own metric
and your own target value
if you didn't want to follow the recommendations
provided by AWS.
And options for dynamic scaling is to disable scale-in.
So that means you can only scale out, but not scale-in.
You can specify the cool-down period,
as well as the warmup time for the ASG.
And opposite to dynamic scaling is predictive scaling.
So we know this already.
So with predictive scaling, the idea is that
you're going to analyze the historical loads
using a machine learning algorithm done by AWS for you,
and then a forecast will be generated
and then scheduling extras will be taken automatically
based on that forecast.