In today’s fast-paced digital landscape, performance and resource optimization are key. When running workloads on Red Hat OpenShift Service on AWS (ROSA), it becomes crucial to dynamically scale resources based on demand — both at the pod and node levels. This is where autoscaling shines.
This article explains how node and pod autoscaling works in a ROSA cluster and how it enables efficient, responsive applications without manual intervention.
🌐 What Is Autoscaling?
Autoscaling in Kubernetes/OpenShift is the ability to automatically adjust computing resources:
Pod Autoscaling scales your application pods based on CPU/memory usage or custom metrics.
Node Autoscaling adds/removes worker nodes depending on the resource requirements of pods.
In ROSA, these mechanisms are tightly integrated with AWS infrastructure and OpenShift’s orchestration engine.
📦 Pod Autoscaling with Horizontal Pod Autoscaler (HPA)
🔹 How It Works:
The Horizontal Pod Autoscaler (HPA) increases or decreases the number of pod replicas in a deployment based on real-time metrics.
🧠 Key Metrics Used:
CPU utilization (most common)
Memory usage
Custom metrics via Prometheus Adapter
✅ Example Scenario:
A web app receives sudden traffic spikes. The HPA detects high CPU usage and automatically scales from 3 to 10 pods, ensuring uninterrupted user experience.
🖥️ Node Autoscaling with Cluster Autoscaler
🔹 What It Does:
Cluster Autoscaler automatically adjusts the number of nodes in the ROSA cluster. When pods can't be scheduled due to lack of resources, it triggers the provisioning of new nodes.
🔸 Likewise, it removes underutilized nodes to reduce cost and resource waste.
🔧 Integrated with AWS:
ROSA uses Amazon EC2 Auto Scaling Groups behind the scenes.
Ensures nodes are provisioned using the same security, networking, and IAM policies.
⚙️ How to Configure Autoscaling in ROSA (Without Deep Coding)
While this blog avoids detailed CLI configuration, here’s a conceptual workflow:
- Enable Cluster Autoscaler Ensure ROSA is using machine pools or machine sets.
Enable autoscaling ranges (min/max node counts).
Deploy Your Application
Deploy a sample app using a Kubernetes/OpenShift deployment.Enable Horizontal Pod Autoscaler
Define resource limits/requests in your pod spec (especially CPU).
Create an HPA object with threshold metrics.
- Observe Autoscaling in Action Use OpenShift Console or metrics dashboard to watch scale events.
During high load, see pods and nodes increase.
Once demand drops, resources scale back down.
📊 Benefits of Autoscaling in ROSA
✅ Optimized Resource Usage
Only run what you need — scale down during off-peak hours.
✅ Performance Assurance
Automatically meet SLAs by scaling up during peak loads.
✅ Cost Efficiency
No need to overprovision resources. Pay only for what you use.
✅ Cloud-Native Resilience
Elastic infrastructure aligned with cloud-native architecture principles.
📌 Final Thoughts
ROSA makes it simple to implement intelligent autoscaling strategies for both pods and nodes. By combining HPA and Cluster Autoscaler, you ensure that your application is highly available, cost-effective, and performance-optimized at all times.
As workloads grow and user demands fluctuate, autoscaling ensures your ROSA cluster remains lean, responsive, and production-ready.
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