If you're running workloads in Kubernetes, you'll know that scalability is key to keeping things available and responsive. But there's a problem: when your cluster runs out of resources, the node autoscaler needs to spin up new nodes, and this takes anywhere from 5 to 10 minutes. That's a long time to wait when you're dealing with a traffic spike. One way to handle this is using low priority pods to create buffer nodes that can be preempted when your actual workloads need the resources.
The Problem
Cloud-native applications are dynamic, and workload demands can spike quickly. Automatic scaling helps, but the delay in scaling up nodes when you run out of capacity can leave you vulnerable, especially in production. When a cluster runs out of available nodes, the autoscaler provisions new ones, and during that 5-10 minute wait you're facing:
Increased Latency: Users experience lag or downtime whilst they're waiting for resources to become available.
Resource Starvation: High-priority workloads don't get the resources they need, leading to degraded performance or failed tasks.
Operational Overhead: SREs end up manually intervening to manage resource loads, which takes them away from more important work.
This is enough reason to look at creating spare capacity in your cluster, and that's where low priority pods come in.
The Solution
The idea is pretty straightforward: you run low priority pods in your cluster that don't actually do any real work - they're just placeholders consuming resources. These pods are sized to take up enough space that the cluster autoscaler provisions additional nodes for them. Effectively, you're creating a buffer of "standby" nodes that are ready and waiting.
When your real workloads need resources and the cluster is under pressure, Kubernetes kicks out these low priority pods to make room - this is called preemption. Essentially, Kubernetes looks at what's running, sees the low priority pods, and terminates them to free up the nodes. This happens almost immediately, and your high-priority workloads can use that capacity straight away. Meanwhile, those evicted low priority pods sit in a pending state, which triggers the autoscaler to spin up new nodes to replace the buffer you just used. The whole thing is self-maintaining.
How Preemption Actually Works
When a high-priority pod needs to be scheduled but there aren't enough resources, the Kubernetes scheduler kicks off preemption. This happens almost instantly compared to the 5-10 minute wait for new nodes.
Here's what happens:
Identification: The scheduler works out which low priority pods need to be evicted to make room. It picks the lowest priority pods first.
Graceful Termination: The selected pods get a termination signal (SIGTERM) and a grace period (usually 30 seconds by default) to shut down cleanly.
Resource Release: Once the low priority pods terminate, their resources are immediately released and available for scheduling. The high-priority pod can then be scheduled onto the node, typically within seconds.
Buffer Pod Rescheduling: After preemption, the evicted low priority pods try to reschedule. If there's capacity on existing nodes, they'll land there. If not, they'll sit in a pending state, which triggers the cluster autoscaler to provision new nodes.
This gives you a dual benefit: your critical workloads get immediate access to the nodes that were running low priority pods, and the system automatically replenishes the buffer in the background. Whilst your high-priority workloads are running on the newly freed capacity, the autoscaler is already provisioning replacement nodes for the evicted buffer pods. Your buffer capacity is continuously maintained without any manual work, so you're always ready for the next spike.
The key advantage here is speed. Whilst provisioning a new node takes 5-10 minutes, preempting a low priority pod and scheduling a high-priority pod in its place typically completes in under a minute.
Why This Approach Works Well
Now that you understand how the solution works, let's look at why it's effective:
Immediate Resource Availability: You maintain a pool of ready nodes that can rapidly scale up when needed. There's always capacity available to handle sudden load spikes without waiting for new nodes.
Seamless Scaling: High-priority workloads never face resource starvation, even during traffic surges. They get immediate access to capacity, whilst the buffer automatically replenishes itself in the background.
Self-Maintaining: Once set up, the system handles everything automatically. You don't need to manually manage the buffer or intervene when workloads spike.
The Trade-Off
Whilst low priority pods offer significant advantages for keeping your cluster responsive, you need to understand the cost implications. By maintaining buffer nodes with low priority pods, you're running machines that aren't hosting active, productive workloads. You're paying for additional infrastructure just for availability and responsiveness.
These buffer nodes consume compute resources you're paying for, even though they're only running placeholder workloads. The decision for your organisation comes down to whether the improved responsiveness and elimination of that 5-10 minute scaling delay justifies the extra cost. For production environments with strict SLA requirements or where downtime is expensive, this trade-off is usually worth it. However, you'll want to carefully size your buffer capacity to balance cost with availability needs.
Setting It Up
Step 1: Define Your Low Priority Pod Configurations
Start by defining low priority pods using the PriorityClass resource. This is where you create configurations that designate certain workloads as low priority.
Here's what that configuration looks like:
apiVersion: scheduling.k8s.io/v1
kind: PriorityClass
metadata:
name: low-priority
value: 0
globalDefault: false
description: "Priority class for buffer pods"
---
apiVersion: apps/v1
kind: Deployment
metadata:
name: buffer-pods
namespace: default
spec:
replicas: 3 # Adjust based on how much buffer capacity you need
selector:
matchLabels:
app: buffer
template:
metadata:
labels:
app: buffer
spec:
priorityClassName: low-priority
containers:
- name: buffer-container
image: registry.k8s.io/pause:3.9 # Lightweight image that does nothing
resources:
requests:
cpu: "1000m" # Size these based on your typical workload needs
memory: "2Gi" # Large enough to trigger node creation
limits:
cpu: "1000m"
memory: "2Gi"The key things to note here:
- The PriorityClass has a value of 0, which is lower than the default priority for regular pods (typically 1000+)
- We're using a Deployment rather than individual pods so we can easily scale the buffer size
- The pause image is a minimal container that does basically nothing - perfect for a placeholder
- The resource requests are what matter - these determine how much space each buffer pod takes up
- You'll want to size the CPU and memory requests based on your actual workload needs
Step 2: Deploy the Low Priority Pods
Next, deploy these low priority pods across your cluster. Use affinity configurations to spread them out and let Kubernetes manage them.
Step 3: Monitor and Adjust
You'll want to monitor your deployment to make sure your buffer nodes are scaling up when needed and scaling down during idle periods to save costs. Tools like Prometheus and Grafana work well for monitoring resource usage and pod status so you can refine your setup over time.
Best Practices
Right-Sizing Your Buffer Pods: The resource requests for your low priority pods need careful thought. They need to be big enough to consume sufficient capacity that additional buffer nodes actually get provisioned by the autoscaler. But they shouldn't be so large that you end up over-provisioning beyond your required buffer size. Think about your typical workload resource requirements and size your buffer pods to create exactly the number of standby nodes you need.
Regular Assessment: Keep assessing your scaling strategies and adjust based on what you're seeing with workload patterns and demands. Monitor how often your buffer pods are getting evicted and whether the buffer size makes sense for your traffic patterns.
Communication and Documentation: Make sure your team understands what low priority pods do in your deployment and what this means for your SLAs. Document the cost of running your buffer nodes and why you're justifying this overhead.
Automated Alerts: Set up alerts for when pod eviction happens so you can react quickly and make sure critical workloads aren't being affected. Also alert on buffer pod status to ensure your buffer capacity stays available.
Wrapping Up
Leveraging low priority pods to create buffer nodes is an effective way to handle resource constraints when you need rapid scaling and can't afford to wait for the node autoscaler. This approach is particularly valuable if you're dealing with workloads that experience sudden, unpredictable traffic spikes and need to scale up immediately - think scenarios like flash sales, breaking news events, or user-facing applications with strict SLA requirements.
However, this isn't a one-size-fits-all solution. If your workloads are fairly static or you can tolerate the 5-10 minute wait for new nodes to provision, you probably don't need this. The buffer comes at an additional cost since you're running nodes that aren't doing productive work, so you need to weigh whether the improved responsiveness justifies the extra spend for your specific use case.
If you do decide this approach fits your needs, remember to keep monitoring and iterating on your configuration for the best resource management. By maintaining a buffer of low priority pods, you can address resource scarcity before it becomes a problem, reduce latency, and provide a much better experience for your users.
This approach will make your cluster more responsive and free up your operational capacity to focus on improving services instead of constantly firefighting resource issues.
Microsoft