What is AWS Auto Scaling?

Key takeaways:

• AWS Auto Scaling automatically adjusts resources (e.g., EC2 instances) based on demand, ensuring cost efficiency and performance.

• Includes Target Tracking, Step Scaling, and Scheduled Scaling to optimize resource allocation.

• Scales in during low-demand periods to reduce costs, while scaling out during peak traffic.

• Works with EC2, RDS, and EKS for seamless scaling across AWS services.

• Primarily supports specific services (e.g., EC2, ECS), lacks fine-grained control, and scaling actions may take time.

AWS Auto Scaling is a powerful feature of AWS that monitors the application and automatically adjusts the resources allocated to it. This process is done as per the demand to ensure the costs are as low as possible and that no server resources ever go to waste.

For example, let's imagine we run an e-commerce website. Throughout the year, our website may experience periods of average capacity. However, as the end of the year approaches, customer traffic rapidly increases. AWS Auto Scaling actively monitors our application's demand and automatically adjusts the resources allocated to our servers. During peak seasons, it scales out server capacity to handle the surge in traffic, guaranteeing a smooth ordering experience for all customers. Conversely, during low-demand periods, it scales in our resources, eliminating wastage and ensuring we only pay for the resources we use.

How Auto Scaling works

Auto Scaling requires predefined auto scaling policies that dictate when to scale in (remove instances) or scale out (add instances). These policies can be based on specific performance metrics, such as:

• Target Tracking Scaling: Automatically adjusts resources to maintain a specified target metric, like keeping CPU utilization at 60%.

• Step Scaling: Adjusts resources incrementally when certain thresholds are crossed, allowing you to scale in smaller steps.

• Scheduled Scaling: Scales resources at predefined times, perfect for predictable traffic patterns (e.g., increasing capacity for seasonal traffic spikes).

AWS Auto Scaling continuously monitors your application’s performance through Amazon CloudWatch, tracking metrics such as CPU utilization, memory usage, network traffic, and more. These metrics are used to determine if the system requires more or fewer resources to handle the current workload.

When the monitored metrics breach thresholds specified in the scaling policies, Auto Scaling triggers an action to either increase or decrease resources. For instance, if CPU usage consistently surpasses 80%, AWS Auto Scaling may launch additional EC2 instances to handle the increased load.

Once a scaling action is triggered, AWS Auto Scaling automatically adjusts the resources within your Auto Scaling Group (ASG). This could involve:

• Scaling out: Adding more EC2 instances to accommodate higher demand.

• Scaling in: Removing EC2 instances to prevent over-provisioning during low demand.

Get hands-on experience: Enhance your understanding of AWS Auto Scaling by exploring our Cloud Lab, Understanding Auto Scaling Group (ASG) in AWS.

AWS Auto Scaling pricing

AWS Auto Scaling itself is free of cost. However, you’ll have to pay for the AWS resources it uses and the resources it upscales.

Setting up AWS Auto Scaling on an EC2 instance

AWS Auto Scaling seamlessly integrates with various AWS services, including Amazon RDS (Relational Database Service) and Amazon EKS (Elastic Kubernetes Service). However, for the purpose of this Answer, we will focus on showing how Auto Scaling works with EC2 instances.

Note: We need an AWS account for the following steps.

We can set up AWS Auto Scaling on an EC2 instance with the following steps:

Step 1: Create a launch configuration

• Log in to the AWS Management Console and navigate to the EC2 service.

• Go to the "Launch Templates" section and click the "Create launch template" button.

• Give name to the template.

• Select the desired AMI (Amazon Machine Image) for our EC2 instance.

• Choose the instance type, and specify the necessary configuration details such as security groups, key pair, user data, etc.

• Review the configuration and click "Create launch template" to proceed.

Step 2: Configure the Auto Scaling Group (ASG)

• After creating the launch configuration, go to the "Auto Scaling Groups" section and click on "Create Auto Scaling group."

• Give name to the ASG and select the created launch template as the basis for our Auto Scaling Group.

• Click the “Next” button, select the appropriate networking settings, and click the “Next” button.

• Keep the default options selected on the “Configure advanced options” selected, and click the “Next” button.

• Specify the desired capacity for the group, which includes the minimum, maximum, and desired number of instances.

• Set up scaling policies based on our requirements. We can choose either target tracking scaling or step scaling based on our application's needs.

• Review the configuration and click "Create Auto Scaling Group" to create the group.

Benefits of AWS Auto Scaling

AWS Auto Scaling offers a range of benefits that make it an essential tool for managing cloud infrastructure efficiently. The key advantages include:

• AWS Auto Scaling ensures your application always has the right amount of resources by dynamically adjusting capacity based on demand.

• Cost optimization is a significant benefit of Auto Scaling, as it automatically scales down resources during low-traffic periods, reducing unnecessary costs.

• High availability is achieved by distributing resources across multiple Availability Zones and replacing unhealthy instances automatically.

• Flexibility is provided through customizable scaling policies, allowing users to choose between Target Tracking, Step Scaling, or a combination of strategies that best fit their application's needs.

Limitations of AWS Auto Scaling

Here is the list of the limitations of AWS Auto Scaling:

• Primarily supports EC2, ECS, DynamoDB, and Aurora; not all AWS resources.

• Works at a group level, not fine-grained control for individual resources.

• May take minutes for scaling actions, causing temporary performance issues.

• Setting up policies for complex scaling can be difficult.

• Doesn’t handle scaling for stateful apps well; manual session/data management is needed.