samcogan shares an in-depth strategy for managing Kubernetes cluster scaling in Azure by using low priority pods as preemptible buffers, enabling faster workload response and minimizing downtime during traffic spikes.

Leveraging Low Priority Pods for Rapid Scaling in AKS

Author: samcogan

Maintaining application availability during traffic spikes is a major challenge for cloud-native workloads running on Kubernetes. Standard cluster autoscaling can leave your workloads vulnerable to performance hits because provisioning new nodes typically takes 5 to 10 minutes. This guide focuses on a solution using low priority pods to create buffer nodes in Azure Kubernetes Service (AKS), enabling rapid scaling and higher responsiveness.

The Problem

When AKS clusters reach their resource limits, it takes several minutes for the autoscaler to bring up new nodes. During this period, users can experience:

  • Increased latency or downtime
  • Resource starvation for critical workloads
  • Extra operational overhead as engineers may intervene manually

The Solution: Buffer Nodes with Low Priority Pods

By running low priority pods that act as resource placeholders, you encourage the cluster autoscaler to keep spare nodes available. When needed, Kubernetes can preempt (evict) these pods instantly to free resources for high-priority workloads. This process completes significantly faster than provisioning new nodes.

How Kubernetes Preemption Works

  • Identification: The scheduler picks which low priority pods to evict for incoming high-priority pods.
  • Graceful Termination: The selected pods get a SIGTERM signal and a short grace period (default: 30 seconds).
  • Immediate Resource Release: Freed resources become available, and critical pods are scheduled instantly.
  • Self-Replenishing Buffer: The evicted pods re-enter a pending state, eventually triggering the autoscaler to spin up new buffer nodes as needed.

This approach ensures both immediate resource availability and a continuously maintained scaling buffer.

Why This Approach Makes Sense

  • Speed: Preemption and rescheduling is much faster than node provisioning (under a minute).
  • Reliability: High-priority workloads avoid starvation or performance dips.
  • Automation: The buffer replenishes itself with minimal manual effort.

Important Trade-Offs

  • Cost: Buffer nodes have infrastructure costs, since they’re running pods that do no real work. The benefit is immediate availability at critical moments, but you’ll need to assess if this trade-off meets your SLAs and budget.

Step-by-Step Setup

Step 1: Define Low Priority Pod Configuration

Create a PriorityClass for low priority pods:

apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
  name: low-priority
value: 0
globalDefault: false
description: "Priority class for buffer pods"

Define a deployment using the buffer pods:

apiVersion: apps/v1
kind: Deployment
metadata:
  name: buffer-pods
  namespace: default
spec:
  replicas: 3 # Adjust as needed
  selector:
    matchLabels:
      app: buffer
  template:
    metadata:
      labels:
        app: buffer
    spec:
      priorityClassName: low-priority
      containers:
      - name: buffer-container
        image: registry.k8s.io/pause:3.9
        resources:
          requests:
            cpu: "1000m"
            memory: "2Gi"
          limits:
            cpu: "1000m"
            memory: "2Gi"
  • Use a lightweight container (like pause) and set resource requests that mimic actual workloads to ensure node provisioning.

Step 2: Deploy and Spread Buffer Pods

  • Apply your manifests.
  • Optionally configure pod affinity/anti-affinity to distribute load.

Step 3: Monitor and Tune

  • Use metrics tools like Prometheus and Grafana to monitor buffer size, pod evictions, and scaling events.
  • Adjust replica counts and resource settings based on observed peak load and cost considerations.

Best Practices

  • Right-Sizing: Align buffer pod resource requests with actual usage patterns to avoid over or under-provisioning.
  • Continuous Assessment: Regularly review scaling behavior and costs, especially after traffic spikes or usage changes.
  • Team Documentation: Ensure everyone understands the function and impact of low priority pods in your cluster.
  • Automated Alerts: Set alerts for pod eviction and buffer depletion to maintain visibility and quick reaction time.

When to Use This Approach

  • Scenarios demanding minimal downtime (SLA-driven production, high-profile launches, unpredictable load).
  • Not ideal for static workloads or environments where 5-10 minute scaling delays are tolerable.

Conclusion

Maintaining a buffer with low priority pods in Azure Kubernetes Service can dramatically reduce the impact of sudden load spikes by making extra resources instantly available. You trade higher infrastructure cost for improved reliability and faster scaling. Keep your configuration under review and sized to your needs, and your team can focus on delivering value instead of firefighting capacity issues.

Updated Oct 15, 2025 — Version 1.0

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