Learn more about Proactive Cluster Autoscaling from our experts. Our Kubernetes Support team is here to help you with your questions and concerns.

Proactive Cluster Autoscaling | An Intro

If scaling nodes in a Kubernetes cluster is taking too long with the default settings, it is time to learn how to size the cluster nodes and proactively create nodes for quicker scaling.

For instance, when our Kubernetes cluster is low on resources, the Cluster Autoscaler sets up a new node and then adds it to the cluster. Then, the cloud provider has to create a VM from scratch, set it up and connect it to the cluster.

This process hardly takes a few minutes. However, during those few minutes, our app can get overwhelmed with traffic.

Fortunately, we can easily fix this. It involves configuring the cluster autoscaler to set up an extra node rather than being reactive.

This is where we use a big placeholder pod. This pod will do nothing but triggers the autoscaler in advance.

Kubernetes nodes reserve memory and CPU for kubelet, OS, eviction threshold, etc. When we exclude them from the total node capacity, we will get an idea about the actual resources allocatable for pods.

This is when we have to make a placeholder pod using all of them.

Although the placeholder pod competes for resources, we can remove it as and when we need more space with a Pod PriorityClass.

As the pod placeholder pod has a priority of -1, all other pods have precedence. In other words, the placeholder is evicted when the cluster runs out of space.

Benefits of Proactive Cluster Autoscaling

• By scaling the cluster ahead of time, resources are already available when the workload increases, preventing performance degradation or resource bottlenecks.
• Proactive scaling offers better resource allocation.
• By anticipating workload changes and scaling the cluster accordingly, proactive autoscaling can improve system reliability and availability.

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Conclusion

To conclude, our Support Techs introduced us to Proactive Cluster Autoscaling and its benefits.