Scalability capabilities with appropriate use cases (for example, Amazon EC2 Auto Scaling, AWS Auto Scaling)

In AWS, scalability can ensure that your application handles varying loads efficiently — whether you are expecting a consistent traffic increase or fluctuating, unpredictable spikes. AWS offers several services that aid in implementing scalability, including Amazon EC2 Auto Scaling and AWS Auto Scaling. Below, we explore these services, providing use cases that highlight their practical applications.

Amazon EC2 Auto Scaling

Amazon EC2 Auto Scaling ensures the right number of Amazon EC2 instances are running to handle the load of your application. It enables you to increase or decrease the number of instances automatically, based on criteria you define such as CPU usage, network traffic, or custom metrics.

Use Cases

• E-commerce sites during sales events: For retail websites, sales events like Black Friday or Cyber Monday can lead to traffic surges. By setting up EC2 Auto Scaling, they can maintain performance by automatically adding more instances during peak times and scaling down during quieter periods.
• Gaming servers: Multiplayer games with variable player counts can use Amazon EC2 Auto Scaling to manage server fleets and maintain an optimal gaming experience by adjusting resources in real-time based on the number of active players.
• Microservice architectures: For applications built with a microservices approach, each service can scale independently based on its particular load, ensuring efficient resource utilization.

AWS Auto Scaling

AWS Auto Scaling goes beyond EC2 instances, allowing you to manage scaling for multiple resources across multiple services. It includes resources such as Amazon EC2 instances, Amazon ECS tasks, Amazon DynamoDB tables, and Amazon RDS databases, all in one interface.

Use Cases

• Managing peak loads across services: Consider a web application that uses EC2 for compute, DynamoDB for database operations, and ECS for container management. AWS Auto Scaling can simultaneously adjust each service in response to the application’s actual demand, providing a smooth user experience.
• Running batch processing jobs: For workloads like data processing or video transcoding that may require intense compute for intermittent periods, AWS Auto Scaling can dynamically adjust resources to complete jobs faster without overprovisioning.
• Handling predictable work patterns: In cases where workload patterns are predictable (like a heavy-use period at the end of each month), AWS Auto Scaling can schedule scaling actions, such as increasing the number of database read replicas in anticipation.

Comparison between Amazon EC2 Auto Scaling and AWS Auto Scaling:

Both Amazon EC2 Auto Scaling and AWS Auto Scaling allow you to define target tracking scaling policies where you set a target value for a specific metric. For example, you might set a target CPU utilization of 70%. When CPU utilization deviates, the scaling action is triggered to bring it back to the target value.

Below is an example of how you could configure target tracking scaling for an EC2 Auto Scaling group using AWS CLI:

aws autoscaling put-scaling-policy –auto-scaling-group-name my-auto-scaling-group –policy-name my-target-tracking-scaling-policy –policy-type TargetTrackingScaling –target-tracking-configuration file://configuration.json

configuration.json might look like this:

{
“PredefinedMetricSpecification”: {
“PredefinedMetricType”: “ASGAverageCPUUtilization”
},
“TargetValue”: 70.0,
“DisableScaleIn”: false
}

In this configuration, the Auto Scaling group increases or decreases the number of EC2 instances to maintain an average CPU utilization of 70%.

Conclusion

Scalability is foundational to the AWS cloud, and it provides powerful tools for architects to ensure resources match the demand. Cases for using Amazon EC2 Auto Scaling generally revolve around scalable compute capacity, whereas AWS Auto Scaling finds its strengths in coordinating scaling policies across a variety of AWS services. By understanding and leveraging these capabilities, you can create resilient, efficient, and cost-effective architectures for your applications.