Auto Scaling and Load Balancing
Make your EC2-based backend resilient and elastic by combining Auto Scaling Groups with an Application Load Balancer to handle changing traffic.
Auto Scaling and Load Balancing is a free AWS for Backend Developers (EC2, S3, RDS, Lambda) lesson on CoddyKit — lesson 4 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the AWS for Backend Developers (EC2, S3, RDS, Lambda) learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
From One Server to Many
One EC2 instance is a single point of failure. Backends run an Auto Scaling Group of instances behind a load balancer that spreads requests across them.
The Launch Template
An ASG needs a recipe for the instances it spins up. A launch template defines the AMI, instance type, and startup configuration.
Anatomy of an Auto Scaling Group
An Auto Scaling Group holds a minimum, desired, and maximum count. It keeps the desired number running and replaces unhealthy instances on its own.
Health Checks
The ASG runs health checks: when an instance fails, it terminates that one and launches a replacement, holding the fleet at the desired size.
Why a Load Balancer?
A load balancer gives clients one stable endpoint while spreading requests across healthy instances. The ALB works at HTTP and routes by path or host.
Listeners and Target Groups
Two ALB pieces: a listener watches a port (like 80) for connections, and a target group is the pool of instances it forwards to.
Connecting the ASG to the ALB
The ASG auto-registers its instances with the target group, so new instances start getting traffic and terminated ones are removed — no manual wiring.
Scaling Policies
A scaling policy adjusts the desired count by metric. Target tracking is simplest: hold average CPU near, say, 50% and AWS adds or removes instances to keep it.
A Python Scaling Simulation
This snippet models target tracking — comparing current CPU to the target to compute the new instance count.
current_cpu = 80
target_cpu = 50
current_count = 4
desired = round(current_count * current_cpu / target_cpu)
print('Scale to', desired, 'instances')Multi-AZ for High Availability
Spread the ASG across multiple Availability Zones. If one AZ goes down, instances in the others keep serving and the ALB stops routing to the failed zone.
Cost and Cooldowns
Scaling out costs money, and rapid up-down thrashing wastes it. A cooldown pauses further scaling so the fleet stabilizes before reacting again.
Quick Check
A traffic spike hits your fleet — what does target tracking do, and how does the ALB keep responses flowing?
Recap: Elastic, Resilient Backends
Recap: launch templates define instances, Auto Scaling Groups self-heal around min/desired/max, and an ALB with target-tracking and multi-AZ adds elasticity and resilience.
Frequently asked questions
Is the “Auto Scaling and Load Balancing” lesson free?
Yes — the full text of “Auto Scaling and Load Balancing” is free to read here on the web, and the AWS for Backend Developers (EC2, S3, RDS, Lambda) course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the AWS for Backend Developers (EC2, S3, RDS, Lambda) course, upgrade to CoddyKit PRO.
What will I learn in “Auto Scaling and Load Balancing”?
Make your EC2-based backend resilient and elastic by combining Auto Scaling Groups with an Application Load Balancer to handle changing traffic. You practise AWS for Backend Developers (EC2, S3, RDS, Lambda) with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start AWS for Backend Developers (EC2, S3, RDS, Lambda)?
No prior experience is required. AWS for Backend Developers (EC2, S3, RDS, Lambda) on CoddyKit is structured for beginners through advanced learners; this is — lesson 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Auto Scaling and Load Balancing” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this AWS for Backend Developers (EC2, S3, RDS, Lambda) lesson?
Yes. Every AWS for Backend Developers (EC2, S3, RDS, Lambda) lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- Introduction to AWS for Backend
- Launching Your First EC2 Instance
- Connecting & Basic EC2 Management
- Auto Scaling and Load Balancing