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Auto Scaling

Why to Use Auto Scaling?

Auto scaling cloud resources helps in scaling up and down automatically. It basically works on the principle of adjusting according to the needs as and when they appear. The unexpected spikes like any predetermined schedule such as sales weekend or festivity events all lead to unusual customer traffic. Yes, auto scaling is quite similar to load balancing as both of the concept work on distributing workloads. Here are the usage of auto scaling-

Reduces Cloud Costs Effectively: At times of slow traffic, auto scaling scales down the resources which help in cutting down the overall costs.  

Improvement in the Performance: Performance improves automatically due to reduced latency as well as downtime. This is actually because the very exact number of resources is put into use and at the required time.  

No Deviation from the Core Business: Helps at refocusing all the IT resources towards the core business module which might have got busy in responding web traffic changes. 

Better IT Efficiency: Auto scaling is good at making run the non-time sensitive workloads on available servers at times of low traffic.


Types of Auto Scaling


Types of scaling depend on scaling strategy or scaling plan. Let’s get a good idea about all its types:


Dynamic Scaling:

It is very much differentiable from other scaling strategies as it creates target tracking scaling policies to direct the resources according to the scaling plan. As the changes happen, scaling policies adjust themselves accordingly along with the resources. Due to this the required capacity is utilized at the target value. It is similar to the air conditioners we use nowadays that are smart and work on AI, once you have set the temperature then there is no need to operate them again. They keep on adjusting the temperatures throughout the day.


Predictive Scaling:

As the name suggests, predictive scaling works on prediction. It keeps the track of all the actions of the past and analyses the resources based on those actions. The analysis done helps in planning out the resource allocation for upcoming 1 or 2 days. Thus, by this it makes sure that the resources are available even before your application is in need of it. One thing that is similar between predictive and dynamic scaling is that both of them is concerned about the target value and then plan the scaling strategy.


Steps Involved in the Scaling Process- 

- The service provider allocates a resource pool from the cloud to host a service.

- Users access the cloud-hosted service

- Service provider or cloud monitors the service, computing metrics such as resource utilization 

- If a metric, e.g. utilization is outside pre-defined limits, a scale in/out operation may be triggered.

- Compute the step size, i.e. number of virtual machines to allocate or release

- Trigger activation/deactivation of resources 

By this you would have understood it clearly that auto scaling is an important feature for most of the cloud computing solutions to manage the resource capacities. These algorithms are developed in the way that takes into account all the aspects of extending to shrinking of the resource’s pool capacity.


Advantages of Auto Scaling- 

There are multiple advantages that auto scaling brings on the table-

Better Fault Tolerance: Apart from distributing the resources, it plays the role of a doctor like for the cloud architecture. It terminates those instances that seem to be disruptive.

Better Availability: Keeps the application always available to meet all the capacities at the right time and in the right amount.

Better Cost Management: As you will only be paying for the capacities in use, it will terminate any kind of extra expenditure that might incur and saves a lot of money that can be put into use on the future development of the application.

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