Here.
And welcome back.
Now in today's video, we'll be discussing about the easy to order Skilling.
Now, before we go to discuss more about it, it is important for us to understand the concept of scalability.
So scalability is basically an ability of a system to change in size depending upon the needs.
So it is very important that whatever infrastructure that you create, that infrastructure should be
able to ski to support that, changing traffic patterns within your environment.
So this can be better understood with this diagram year.
Well, you see that the traffic pattern during the night time is much more lesser when compared to their
daytime.
And this is something that you will see in many of the organization.
In fact, in one of the organization that I have invoking the V and basically M commerce based Web site.
And during the daytime used to have millions of users.
However, during knighting that means teens are too few, hundreds or few thousands.
So what you want is that during daytime you should have enough capacity of servers to serve millions
of users and during night time you do not want that high capacity.
So that at all cost can be looked into.
So in such cases, what you want is you want to relaunch and remove servers dynamically based on the
order load off your Web site.
So, for example, let's say that you have a base of two servers that needs that.
Two servers are always running 24/7 and you define two policies.
First policy is that if the average C.P.U utilization is created in six peoples and then add two more
instance.
However, if the average sepehr utilization is less than 30 percent, then remove two instance.
So when you have such policy, then venire, what a traffic load increases in your environment.
Automatically new servers get provisioned and in a similar way.
If the amount of traffic is less, then the new servers, that provision would be removed.
So that specific capability is achieved with the help both easy to auto scaling.
So the Amazon easy to order scaling helps you to maintain application availability and allow you to
automatically ag or remove instances according to the conditions that you define.
So if we're looking at this example scenario, you have a minimum of two easy two instance.
That means that do so as will always run.
Then you have a maximum of 10 ECPA instance.
So let's say that if there is a high amount of users who are visiting the website, what is the maximum
number of server that you want to launch?
Addition.
So that would be 10 and you're the threshold is 50 percent C.P.U.
So it can be like, if anything, the amount of C.P.U is higher than 50 percent.
Then you can board and launch a few more easy to instances.
So that's what the easy to giving this.
So let's do one thing.
This is more of a theoretical perspective.
Let's jump into the demo so that it can be better.
Mr..
So what I have in today's video is I have a auto's killing which is configured and you have one you
see two incidents which have been created with the help of the auto scaling group.
And what we have done is anytime I see you, utilization crosses a threshold.
The auto Scalabrine, go ahead and it will launch.
Few want easy two instances.
So before we go.
And let me quickly show you the configuration you're.
So let's quickly go to the new console.
So currently you see that we have a auto's giving group.
And if you look into this Ordos killing group under the automatic skiddy, you have a policy which has
been defined.
And within the policy, you see that this policy will be executed whenever the average C.P.U utilization
is at 50 percent at that date.
The action is AG or remove the capacity Yurick as required.
So what this will do is this will go ahead and launch few more instances.
Whenever I see bew, utilization is higher than the average of 50 percent.
And it will also go ahead and remove the easy two instances.
Whenever there's a view, utilization is lower.
So currently, for the easy to instance, if we look into the monitoring, the average CB utilization
is very less.
So what we do, we connect to that.
You see two instance and we manually increase the C.P.U load and we'll look into how exactly the auto
scaling behaves.
So let me just copy the IP, address your hand from their domain.
Let me go ahead and quickly connect to the instance.
So we'll be basically connecting to the instance from two taps.
I quickly show you why.
So let's go to the first tabua and let's also switch to loop in the second.
All.
So now we have the easy to instantiate lay in board that have that data available.
So let's quickly generate a law.
So I say Dev on zero.
And the output file would be dead of night pilot.
So what this will do it go ahead and generate zeros and it will store it to the output file of dev.
None.
So as soon as Gordon Brown this is basically a c.p.u intensive operation.
And if you run that top over here, you should see that this DEEDI come on or you're done.
See, beaute and utilization is ninety nine point nine nine.
So this will go ahead and increase the oral sepehr utilization.
So let's quickly wait for a moment here.
So basically, if you open up the C view utilization all year, you saw that it immediately spiked up
primarily because they're decomposed, is using a lot of C.P.U.
So let's quickly evade for a moment here.
So it has been few minutes.
Let's look into the C.P.U utilization yet again.
And now you see that it has crossed Heidrick posting pad for certain period.
Hundred percent is Constanta warrior.
So as expected, since our average C.P.U utilization is greater than 50 percent.
We should see a new easy two instance that should had been launched.
So let's quickly refresh here.
And you would see that you have one easy two instances that automatically got Longido here.
So this is what, Ordos killing defense.
Now in a similar way.
So this is what is rare for as this kill out operation.
So in a similar vein, whenever the average C.P.U utilization decreases, then automatically the new
easy to instant's will be terminated.
So let's also look into that.
So let's go ahead and quickly do a control, see?
And you would see that within that top.
You do not really see that BDK come our lawyer, primarily because we have stopped it so slowly.
The order load average of the Linux system will decrease.
And once it decreases, the auto scaling will monitor it.
And then it will go ahead and terminate that easy two instance.
So let's quickly wait for a moment here.
So it has been few minutes.
Let's look into the C.P.U utilization.
And as expected, high utilization is now almost zero.
So let's go ahead and do a refresh.
And you should see that the easy two instance is now terminated.
All right.
So I hope at a high level, all of you, you understood what Ordos Kitty does as far as the easy to
is concerned.
Now, at the initial level, it might look like a very simple and straightforward service.
Very Gordon defined SCDP utilization.
And based on that, the auto scaling along this code removes the server.
However, that is not so easy to auto scaling or forced a lot of features.
This allows us to control things in a very granular of it.
So they know you discuss about the scaling pipes that are available for easy too.
There are a lot of scaling data that are available.
So let's take an example of three.
So you have a scheduled scaling.
So instead.
Type of scaling the service, Cade, based on a specific schedule.
So, for example, every week the traffic to a Web application starts to increase on Wednesday.
That remains high on Thursday and starts to decrease on Friday.
So if this is ever then number one, you can go ahead and define the schedule scaling.
Then you also have a dynamic scaling value work and have to follow their demand curve for skating activities.
So this is something that we've already seen that their utilizes utilization is higher than 90 percent.
Then you, Gordon, launch new instances.
Now, you also have an interesting scaling, paperless predictive skating.
So predictive scaling basically makes use of machine learning algorithms to detect the changes in daily
and weekly patterns.
And then it would automatically adjust the auto skipping group based on the forecast.
So these are some of the sample scaling types that are available that you can make.
Is that easy to data?
A lot of others.
So the primary aim of this Pacifics life is to look into how good I knew that Idalis offers the auto
Skillings service based on wide variety of use cases of deep customers.
So that's the very high limit or view about that easy to auto's killing.
I hope this video has been informative for you, and I look forward to see you in the next video.