Cost-Effective Node Scaling in Kubernetes on DigitalOcean?

Hey there!

The DigitalOcean Managed Kubernetes services comes with a built-in autoscaler that can automatically adjust the number of nodes in your cluster based on the workload. During off-peak hours, the autoscaler can scale down your Kubernetes nodes, reducing your costs.

Also in addition to that, you can use PodDisruptionBudget (PDB) which specifies the minimum number of replicas that an application can tolerate having during a voluntary disruption, relative to how many it is intended to have. For example, if you set the replicas value for a pod to 5, and set the PDB to 1, potentially disruptive actions like cluster upgrades and resizes occur with no fewer than four pods running.

You can check out the official documentation on how to do that here:

https://docs.digitalocean.com/products/kubernetes/how-to/autoscale/

In addition to the node autoscaling, you should also set up a Horizontal Pod Autoscaler (HPA) which scales the number of pods in your deployment based on observed CPU utilization (or other select metrics). This allows your services to handle more traffic when needed and scale down during quieter times.

You can follow the steps on how to do that here:

https://docs.digitalocean.com/products/kubernetes/how-to/set-up-autoscaling/

Basically, combining HPA with node autoscaling provides a very good approach to resource allocation, so that you don’t over-provision during low-demand periods but also scale accordingly whenever your load goes up.

Additionally, you should also look into setting resource requests and limits:

• requests - Specifies how much of a resource (such as CPU and memory resources) a pod is allowed to request on a node before being scheduled. If the node doesn’t have the available resources, the pod will not be scheduled. This prevents pods from being scheduled on nodes that are already under heavy workload.

• limits - Specifies the amount of resources (such as CPU and memory resources) a pod is allowed to utilize on a node. This prevents pods from potentially slowing down the work of other pods.

You can follow the steps on how to set that up here:

https://docs.digitalocean.com/products/kubernetes/concepts/best-practices/

And one more thing, you should also keep an eye on the basic metrics that come out of the box which includes CPU usage, load averages, bandwidth, and disk I/O. And if you want to take this a step further, consider setting up advanced monitoring as well:

https://docs.digitalocean.com/products/kubernetes/how-to/monitor-advanced/

If you have any other questions, feel free to ask!

- Bobby

To optimize node scaling in your DigitalOcean Kubernetes cluster while balancing cost and performance, consider these tips:

1. Cluster Autoscaler: Automatically adds/removes nodes based on workload needs. Use Pod Disruption Budgets (PDBs) to prevent downtime for critical services during scaling down.

2. Horizontal Pod Autoscaler (HPA): Scales pods based on CPU/memory usage, helping avoid scaling the whole node pool unnecessarily.

3. Node Affinity & Taints: Assign critical services to specific nodes, allowing you to scale down non-critical workloads safely.

4. KEDA: Use scheduled scaling (e.g., cron jobs) to adjust resources during off-peak times.

5. Spot Instances: Save on costs by running non-critical workloads on cheaper spot instances.

6. Monitor & Right-size: Tools like Prometheus, Grafana, and Kubecost can help track resource usage and recommend optimal node sizes.

By using these strategies, you can scale down during off-peak hours without affecting key services.