The CAST AI autoscaler supports running your workloads on Spot/Preemptible instances. This guide will help you configure and run it in 5 minutes.
When to use: spot instances are optional
When a pod is marked only with
tolerations, the Kubernetes scheduler could place such a pod/pods on regular nodes as well.
tolerations: - key: scheduling.cast.ai/spot operator: Exists
When to use: only use spot instances
If you want to make sure that a pod is scheduled on spot instances only, add
nodeSelector as well as per the example below.
The autoscaler will then ensure that only a spot instance is picked whenever your pod requires additional workload in the cluster.
tolerations: - key: scheduling.cast.ai/spot operator: Exists nodeSelector: scheduling.cast.ai/spot: "true"
When to use: there's a need to minimize workload interruptions
Autoscaler is able to identify which instance types are less likely to be interrupted. You can set a default reliability value cluster-wide in spot instance policy. If you want to control that per-workload, e.g. leave most const-efficient value globally and only choose more stable instances for specific pods, define this in deployment configuration by setting
scheduling.cast.ai/spot-reliability label on the pod.
Here's an example how it's done for the typical deployment:
spec: template: metadata: labels: scheduling.cast.ai/spot-reliability: 10
Reliability is measured by "what is the percentage of reclaimed instances during trailing month for this instance type". This tag specifies an upper limit - all instances below specified reliability value will be considered.
The value is a percentage (range is 1-100), and the meaningful values are:
5: most reliable category; by using this value you'll restrict autoscaler to use only the narrowest set of spot instance types
15: reasonable value range to compromise between reliability and price;
25and above: typically most instances fall into this category,.
For AWS, have a look at Spot instance advisor to get an idea which instances correspond to which reliability category.
Step-by-step deployment on Spot Instance¶
In this step-by-step guide, we demonstrate how to use Spot Instances with your CAST AI clusters.
To do that, we will use an example NGINX deployment configured to run only on Spot/Preemptible instances.
1. Enable relevant policies¶
To start using Spot instances autoscaler enable the following policies under the
Policies menu in the UI:
Spot/Preemptible instances policy
- This policy allows the autoscaler to use spot instances
Unschedulable pods policy
- This policy requests an additional workload to be scheduled based on your deployment requirements (i.e. run on spot instances)
2. Example deployment¶
Save the following yaml file, and name it:
apiVersion: apps/v1 kind: Deployment metadata: name: nginx-deployment labels: app: nginx spec: replicas: 1 selector: matchLabels: app: nginx template: metadata: labels: app: nginx spec: nodeSelector: scheduling.cast.ai/spot: "true" tolerations: - key: scheduling.cast.ai/spot operator: Exists containers: - name: nginx image: nginx:1.14.2 ports: - containerPort: 80 resources: requests: cpu: '2' limits: cpu: '3'
2.1. Apply the example deployment¶
kubeconfig set in your current shell session, you can execute the following (or use other means of applying deployment files):
kubectl apply -f ngninx.yaml
2.2. Wait several minutes¶
Once the deployment is created, it will take up to several minutes for the autoscaler to pick up the information about your pending deployment and schedule the relevant workloads in order to satisfy the deployment needs, such as:
- This deployment tolerates spot instances
- This deployment must run only on spot instances
3. Spot Instance added¶
- You can see your newly added spot instance in the cluster node list.
3.1. AWS instance list¶
Just to double-check, go to the AWS console and check that the added node has the
Lifecycle: spot indicator.