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What Is AWS Auto Scaling? And When Should You Use It?

In this article, we cover exactly what AWS Auto Scaling is, how it works, its benefits, disadvantages, and more.

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Whether you’re growing rapidly and need to expand your infrastructure or demand has slowed and you need to scale down, AWS Auto Scaling can help. While manual scaling is time-intensive and costly, auto scaling is an automated process that adjusts capacity for predictable performance and costs.

Auto scaling can help you optimize how your application is used and reduce wastage and optimize cloud spend. While the learning curve may be high, auto scaling has many benefits which you can further enhance through load balancing. 

To help you better understand auto scaling and its benefits, this article will cover AWS Auto Scaling in full detail. We’ll cover exactly what AWS Auto Scaling is, how it works, its benefits, disadvantages, and more.

Table Of Contents

Related Article: The 11 Best Cloud Cost Management Tools In 2022

What Is AWS Auto Scaling? 

AWS Auto Scaling is a service that assists organizations in supervising AWS-based software and infrastructure. The service automatically adjusts capacity to maintain steady, predictable performance at the lowest possible cost. 

AWS Auto Scaling can increase and/or decrease the capacity of AWS services to optimize costs. The service will monitor all scalable cloud services and resources related to a user’s applications. These resources may include:

  • Aurora DB clusters and replicas
  • Elastic Compute Cloud Auto Scaling Groups
  • EC2 Spot Fleets
  • Elastic Container Service (ECS) components
  • Amazon Elastic Container Service
  • DynamoDB global secondary indexes and tables

However, the best way to understand auto scaling is through Amazon’s Elastic Compute Cloud (EC2) service. EC2 provides virtual servers or compute instances that you can use to host your application(s). These virtual servers are known as EC2 instances.  

An organization may have multiple instances with different specifications. Throughout the rest of this guide, we’ll focus on Amazon Auto Scaling from an EC2 context, to make the topic simpler to understand.

How Does AWS Auto Scaling Work?

Elasticity (or elastic computing) is one of the cloud’s greatest attributes. Auto scaling facilitates elasticity by automatically adding resources to meet new workload demand and reducing them when demand decreases. 

In the case of EC2, AWS will elastically scale your EC2 instances by launching new ones and terminating old unhealthy ones. You can use Amazon Auto Scaling to configure a certain number of instances using a set of defined scaling policies. 

If an EC2 instance status-check fails, AWS Auto Scaling will replace the instance. This helps you develop more resilient applications. However, this isn’t the only way auto scaling works. 

Auto scaling also utilizes performance-based metrics that are sent to CloudWatch. For instance, you might set performance metrics based on CPU thresholds. If CPU usage reaches a certain defined percentage, CloudWatch will notify AWS Auto Scaling. The auto scaling service will respond by launching an extra instance to handle the pressure and add more capacity. 

This is how auto scaling works at a very basic level. However, it also works very well with elastic load balancing. For instance, clients can connect to Amazon’s load balancer, which will distribute them to your EC2 instances. If an EC2 instance fails, the load balancer will re-route the connection to the next available healthy EC2 instance.

AWS Auto Scaling will be notified during this process and will terminate the instance and launch a new one.  

Configuring AWS Auto Scaling

When we configure EC2 auto scaling, there are two ways in which we can define the behavior of the instances that are launched. We can either use Launch Configurations or Launch Templates.

With a Launch Configuration, you must specify the Amazon Machine Image (AMI) and the instance type that you want to use. For instance, you can have a Linux 2 AMI type with a T3 micro instance type. Other configurable properties include the role, type of monitoring, tenancy, storage, security groups, IP Address Type, etc.     

Thus, we can define these details in the launch configuration, and the auto scaling group will use it to initiate your instances. One thing that should be noted about Launch Configurations is that you cannot edit them after they are saved. 

This means that if you want to modify your Launch Configuration settings, you’ll need to define a new one and update your auto scaling group to use it. 

Launch Templates are a newer alternative to Launch Configurations. While they’re very similar, they have a few additional features. For instance, they allow you:

  • To have multiple versions of a template
  • To edit your launch templates (you can’t edit saved Launch Configuration)
  • To use dedicated hosts
  • To use a combination of Spot and On-demand instances
  • To define more advanced settings such as shutdown behavior, termination protection, placement groups, etc.

Auto Scaling Benefits And Disadvantages

Advantages of AWS Auto Scaling

AWS Auto Scaling has numerous benefits. These include:

  • Improved fault tolerance - AWS Auto Scaling allows you to monitor your applications. It can help you terminate any corrupted instances and automatically launch new ones.
  • Improved cost management - Scaling is done automatically through a set of threshold parameters. You can scale up or down depending on your organization’s requirements. This allows you to save money on personnel and equipment. It also gives you a centralized position to view all your scaling costs.
  • Reliability - Since scaling is done automatically, it’s incredibly efficient and reliable. Furthermore, whenever scaling is initiated, AWS will send notifications to your phone or email address.

Disadvantages of AWS Auto Scaling

Nothing is perfect. However, it would seem that AWS Auto Scaling has very few disadvantages. These include: 

  • Increased development complexity - Integrating auto scaling of any type may make code updates, deployments, and configurations more complicated. You’ll need a separate service to synchronize your code changes. In addition to this, if your application saves user input, it may be harder to capture and manage persistent data such as logs. 
  • Complicated setup - Setting up AWS Auto Scaling can be challenging. It may require hours of training and a bit of troubleshooting. Amazon could remedy this by making its graphical user interface more intuitive and user-friendly. 
  • Limited across region functionality - Adding and working with regions and availability zones can be quite complex and isn’t handled with much nuance. Amazon could provide users with more refined options to help organizations running multi-region applications with different usage scopes.

What Is An AWS Auto Scaling Group?

AWS Auto Scaling Groups are an integral part of the scaling process. In the case of EC2, they manage how instances are scaled using Launch Configurations and/or Launch templates. They scale out, scale in, and ensure that there are a minimum and maximum number of instances running. They’re also responsible for automatically registering new instances to the load balancer. 

In essence, the Auto Scaling Group (ASG) is a collection of EC2 instances. Thus, the size of the ASG is dependent on the capacity or the number of instances you’ve configured for the group. 

ASGs are free. You don’t pay for them, only the EC2 instances that are spun up and spun down. 

Load Balancing Vs. Auto Scaling

What is load balancing?

High-traffic applications and websites can serve thousands of users internationally each day. Load balancing describes the process of distributing workload evenly across multiple servers. A load balance sends requests to servers that can efficiently handle them to optimize speed and performance while preventing downtime.

For a long time, load balancers existed in the form of hardware in private data centers. However, thanks to the popularization of the cloud, load balancers have evolved. You may hear them referred to as application delivery controllers (ADCs).

They now provide additional capabilities such as security, acceleration, and authentication. In the context of Amazon EC2, a load balancer will distribute traffic among EC2 instances. The load balancer will spread a load across multiple downstream instances while exposing only a single point of access (DNS) to your application. Additionally, the load balancer will perform regular health checks on instances to ensure they are working correctly.

Amazon’s Elastic Load Balancing (ELB) can also provide SSL termination for your websites. This means that it will handle the termination and encryption of the connection between the client and your website. The ELB will also separate public traffic from private traffic and help you maintain permissions across regions.  

How does load balancing work with auto scaling?

Load balancing isn’t an alternative to auto scaling. In fact, it can help facilitate efficient auto scaling when the two work in unison. While load balancing will re-route connections from unhealthy instances, it still needs new instances to route connections to. 

Thus, auto scaling will initiate these new instances, and your load balancing will attach connections to them. That’s why having both AWS Elastic Load Balancing along with AWS Auto Scaling is worthwhile. 

Understanding Your AWS Costs

Above, we explored what AWS Auto Scaling is and how works with AWS Elastic Load Balancing. Understanding how services like these work and integrating them into your organization can help you optimize costs and save time. 

Being able to make informed cost and engineering decisions like this, though, first start by having complete visibility into your cloud spend. This is where a cloud cost intelligence platform, like CloudZero, can help.

CloudZero provides visibility into your cloud costs, enabling engineering teams to drill into cost data from a high level down to the individual components that drive their cloud spend — and see exactly what AWS services cost them the most and why.

With detailed cloud cost intelligence, organizations can make informed product, engineering, and business decisions. Schedule a demo today to see how CloudZero can put your engineering team in control of cloud spend.

Cody Slingerland

Author: Cody Slingerland

Cody Slingerland, a FinOps certified practitioner, is an avid content creator with over 10 years of experience creating content for SaaS and technology companies. Cody collaborates with internal team members and subject matter experts to create expert-written content on the CloudZero blog.


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