Understanding ASG Scaling Policies in AWS
5 min read
After setting up his Auto Scaling Group (ASG) to launch and terminate EC2 instances, Arjun was thrilled.
“Now my app scales automatically based on demand!”
But soon, he had questions:
“When exactly does AWS decide to scale?”
“Can I scale based on time, traffic, or forecasts?”
“What if I want more control?”
That’s when he dove into the world of ASG scaling policies.
🔁 What Are Scaling Policies?
Scaling policies define when and how your ASG should increase or decrease the number of EC2 instances.
You can scale based on:
Real-time metrics (CPU, requests)
Scheduled times (like every Friday at 5 PM)
Forecasts (machine learning-based predictions)
🧩 The 4 Types of Scaling Policies in AWS
1️⃣ Target Tracking Scaling
🧠 Simplest to use.
Arjun just picked a metric, like CPU utilization, and a target value, like 40%.
AWS then automatically scaled out or in to keep CPU near 40%.
✅ Easy to configure
✅ Automatically balances based on the target
📘 SAA Tip: Think “cruise control” for your EC2 fleet
2️⃣ Simple or Step Scaling
This gave Arjun more control.
He created CloudWatch alarms like:
If CPU > 70% → add 2 EC2 instances
If CPU < 30% → remove 1 EC2 instance
You can define scaling steps based on how far a metric deviates from a threshold.
📘 Simple Scaling = one scaling action per alarm
📘 Step Scaling = different responses based on severity
3️⃣ Scheduled Scaling
Arjun noticed that every Friday at 5 PM, traffic spiked.
So he configured a scheduled scaling policy:
“At 4:50 PM every Friday, increase min capacity to 10.”
✅ Great for predictable events like:
Business hours
Product launches
Weekly reporting jobs
4️⃣ Predictive Scaling (Magic from the Future)
This one blew Arjun’s mind.
AWS would analyze historical usage patterns, forecast future traffic, and scale in advance.
For example:
- Traffic spikes every Monday morning at 9 AM?
→ Predictive Scaling adjusts EC2 count before it happens.
📘 SAA Tip: Mention of “forecasting” or “historical usage” = Predictive Scaling
📊 Metrics You Can Scale On
Arjun learned that choosing the right metric was key.
| Metric | When to Use |
| CPU Utilization | Common default for compute-heavy workloads |
| RequestCountPerTarget (from ALB) | Great for web apps where traffic is tied to request volume |
| NetworkIn/Out | For apps handling large file uploads/downloads |
| Custom CloudWatch Metrics | App-specific logic like queue length, memory, or DB latency |
⏱️ Scaling Cooldown: Preventing Ping-Pong Scaling
After a scale-out or scale-in happens, AWS waits before taking another action.
This pause is called the cooldown period (default: 300 seconds or 5 minutes).
Why? So your system has time to stabilize and AWS doesn't scale up/down too aggressively.
📘 You can lower this if your EC2 instances launch fast (e.g., pre-baked AMIs).
🧠 Arjun’s Best Practices Cheat Sheet
| Tip | Why It Matters |
| Use ready-to-go AMIs | Reduces startup time; instances serve traffic faster |
| Enable detailed monitoring | Get metrics every 1 minute instead of 5 |
| Choose metrics that match your workload | CPU ≠ always best |
| Combine Target Tracking + Step Scaling | Simple baseline + fine-tuned controls |
| Avoid cooldown overlap with long launch times | Set cooldown accordingly |
🎯 Summary: Scaling Policy Quick View
| Type | Best For | Notes |
| Target Tracking | Easy, self-adjusting workloads | “Set and forget” |
| Step Scaling | Custom control | Use with CloudWatch alarms |
| Scheduled Scaling | Known traffic patterns | Manual, fixed times |
| Predictive Scaling | Repeating patterns | Forecast-based automation |
🚀 Arjun’s Final Architecture
Users
↓
Application Load Balancer (ALB)
↓
Auto Scaling Group (ASG)
↳ Min: 2, Desired: 4, Max: 10
↳ Scaling Policies:
• Target Tracking on CPU
• Step Scaling on request count
• Scheduled Scaling (Fridays)
• Predictive Scaling (enabled)
His app now handled:
🧠 Smarter scaling decisions
📈 Sudden spikes
🕒 Planned surges
💸 Lower costs during quiet hours
“Now my app scales like a pro — and I don’t even have to touch it.”