How to Handle Sudden Traffic Spikes on Amazon EKS

In today’s cloud-native world, unpredictable workloads are the norm, not the exception. When running Kubernetes clusters on Amazon EKS, one common challenge that teams face is ensuring that mission-critical applications can scale immediately.

Since we are using Spot Instances for the workload, there may be situations where a node is unavailable at the time of pod deployment. In such cases, the pod will remain in an unscheduled state until a new node is provisioned.

Once the new node is available, the pod will then be scheduled and deployed. Please note that this provisioning process may take some time, depending on the availability of Spot Instances.

The Problem: Delays When It Matters Most

Your Amazon EKS cluster might scale beautifully during normal load, but when a new deployment hits, perhaps due to a new product development, your high-priority workloads can end up stuck waiting for new nodes to become available.

So what happens when your high-priority pods can’t find available compute? They sit unscheduled, waiting for nodes to spin up — and this delay can translate directly into downtime, poor user experience, or SLA penalties.

Let’s break this down as a practical Problem → Root Cause → Solution, so you can walk away with an approach you can deploy today.

By default, Cluster Autoscaler works reactively: it adds nodes only when unschedulable pods are detected. But spinning up new EC2 instances takes time.

The Root Cause: Reactive Scaling and No Buffer Capacity

The problem isn’t that Kubernetes or Amazon EKS are flawed — it’s just how the autoscaling mechanism works. Here’s what happens under the hood:

• Node creation is not instant: Even with fast instance types, provisioning can take several minutes.
• No spare capacity: If there are no idle nodes or reserved compute, there’s nowhere for new pods to run.
• No eviction policy: If all pods have equal priority, Kubernetes can’t decide which to evict for urgent workloads.

Together, this creates a gap where your high-priority services are left waiting — exactly when you can’t afford it.

The Solution: Over-Provisioning with Cluster Autoscaler and Pod Priority

The answer is to turn reactive scaling into proactive scaling. This is done by combining:

• Cluster Autoscaler
• Kubernetes Pod Priority & Preemption
• Low-priority placeholder pods

This approach is called Over-Provisioning. It works like this: you deploy low-priority pods that occupy spare capacity on your nodes. When critical workloads arrive, Kubernetes evicts the placeholders immediately to make room. Meanwhile, Cluster Autoscaler detects that the freed capacity is gone and spins up new nodes to replenish the buffer.

Result? Your high-priority workloads start instantly — no waiting, no lag.

How To Set It Up: A Practical Guide

Here’s how you can put this into action on Amazon EKS:

1. Create or Use an Amazon EKS Cluster
If you don’t have an Amazon EKS cluster yet, create one with a managed node group. A typical setup might be:

Instance Type: t3.medium
Minimum Size: 2
Maximum Size: 10
Desired Size: 2

Pick appropriate add-ons for networking and autoscaling.

2. Configure IAM OIDC Provider

Enable OIDC to allow Kubernetes workloads to assume IAM roles securely:

eksctl utils associate-iam-oidc-provider \

 --region us-east-1 \
 --cluster <Cluster-Name> \
 --approve

3. Create an IAM Role for Cluster Autoscaler

Create a new IAM role:

• Use Web Identity with the OIDC provider.
• Attach AmazonEKSClusterAutoscalerPolicy or a custom policy with autoscaling and EC2 permissions.
• Update the trust relationship so the cluster-autoscaler service account can assume this role.

4. Install Cluster Autoscaler

Add the official Helm chart and deploy:

Confirm the cluster-autoscaler pod is running.

5. Define Priority Classes and Workloads

Create three YAML files:

• priority-classes.yaml: Defines low and high-priority classes. 
• low-load-pods.yaml: Runs low-priority placeholder pods.
• force-high-priority.yaml: Deploys high-priority pods to simulate a spike.

6. Apply them:

kubectl apply -f priority-classes.yaml
kubectl apply -f low-load-pods.yaml

Simulate a Spike and Watch Autoscaling in Action

Apply the high-priority workload:

kubectl apply -f force-high-priority.yaml

The high-priority pods will evict the low-priority placeholders immediately, freeing up resources. Cluster Autoscaler notices the change and provisions new nodes, so the placeholders can be rescheduled and the buffer stays ready for the next spike.

What To Expect

• High-priority workloads are scheduled immediately.
• Placeholder pods are evicted and rescheduled when new capacity is ready.
• The cluster scales out to maintain the buffer for next time.

A Few Trade-Offs To Consider

Like every powerful tool, Over-Provisioning has some caveats:

• EC2 nodes still take time to spin up, so placeholder pods buy time but don’t eliminate provisioning delay entirely. 
• Low-priority pods may starve if traffic remains high.
• Idle nodes can increase costs during the scale-down grace period.

Best Practices To Make It Work Smoothly

• Size placeholder pods carefully to match your expected workloads.
• Use clear PriorityClasses to control eviction.
• Deploy over-provisioning pods in a separate namespace for easy management.
• Tune scale-down timers to avoid cost spikes from idle nodes.
• Monitor autoscaler logs and node usage regularly to catch inefficiencies early.

Helpful References

Amazon EKS Best Practices: Cluster Autoscaler
Kubernetes Node Autoscaling

Final Thoughts

Over-Provisioning is a practical, proven pattern for teams that care about high availability. It helps bridge the gap between your cluster’s current capacity and sudden surges in demand, ensuring your mission-critical workloads run without delay.

If you rely on Amazon EKS for production, adding this pattern to your toolkit is a smart move — delivering a smoother user experience when it matters most.

Running Amazon EKS at scale? Want hands-on support for your Amazon EKS deployments? Let our experts handle the complexity - explore our Kubernetes management services.

Related Resources: 

How a Major Amazon EKS Upgrade Solved a Customer's Production Nightmares

How we helped a Production-Scale Team reduce EKS Costs & boost Efficiency