‫Okay, so we've seen how Auto Scaling Groups works
‫but let's have a look at the different Scaling Strategy
‫for your Auto Scaling Groups.
‫So the first one is to do Manual Scaling.
‫Which is when we update the size
‫of a Auto Scaling Group manually.
‫And this is when for example,
‫we changed the capacity from one to two,
‫or back from two to one.
‫Then we can define some Scaling Strategies such as
‫Dynamic Scaling to respond
‫to changing demands automatically.
‫So we have different type
‫of scaling policies within Dynamic Scaling,
‫we have the Simple and the Step Scaling
‫which is the idea is
‫that whenever a CloudWatch alarm is triggered for example,
‫you say whenever the average CPU utilization
‫of all my EC2 instance goes over 70% for five minutes,
‫then add two units to capacity to my ASG.
‫Or when another alarm for example,
‫say whenever the CPU utilization is less
‫than 30% for 10 minutes.
‫Then remove one unit of capacity in my ASG.
‫This would be Simple or Step Scaling
‫because we define the trigger,
‫and then we define how many units we add or remove.
‫Then we have Target Tracking Scaling,
‫which is a very easy way of defining a scaling policy.
‫The example is to say,
‫hey I want the average CPU utilization
‫of all the EC2 instances in my ASG
‫to stay at around 40% on average
‫and then the ASG will scale automatically
‫to make sure that you stay around that target of 40%.
‫And we have also Scheduled Scaling.
‫So this is when we know
‫that changes are going to happen ahead of time.
‫So we anticipate a scaling based on known users pattern.
‫And for example we're saying,
‫hey we know that's on Friday at 5:00 PM.
‫People are going to do sports betting maybe who knows,
‫before the soccer game,
‫and so please increase the minimum capacity
‫to 10 EC2 instances in my ASG
‫at 5pm on Friday.
‫This could be a Scheduled Scaling
‫and there's one last type of scaling.
‫that is definitely appearing on the exam
‫which is called Predictive Scaling.
‫So this one uses Machine Learning
‫to predict future traffic ahead of time
‫so there's some algorithms,
‫they will look at the past traffic patterns,
‫and it will forecast what will happen
‫to traffic based on the past patterns.
‫And so the idea is that
‫it's called predictive because we predict
‫what the load will be over time,
‫and maybe the load is just on a daily basis
‫it peaks for three hours.
‫So this is the kind of things that
‫Predictive Scaling will pick up, okay.
‫And it will automatically provision
‫the right number of EC2 instances
‫in advance to match that predicted period.
‫So this is what the graphs you see on the right hand side.
‫This is very helpful
‫when you have time-based patterns
‫and you just want to have an easy,
‫without any intervention type of scaling
‫trust strategies
‫that is powered by Machine Learning,
‫then that would be Predictive Scaling.
‫So that's it for this lecture.
‫I hope you liked it.
‫And remember the strategies.
‫I will see you in the next lecture.