Configure pod placement by topology

This guide will show how to place pods in particular node, zone, region, cloud, etc., using labels and advanced Kubernetes scheduling features. Kubernetes supports this by using:

• Node selector
• Node affinity and anti-affinity
• Topology spread constraints

All of these methods require special labels to be present on each Kubernetes node.

Supported labels

CAST AI supports the following labels:

Highly-available pod scheduling

Pods can be scheduled in a highly-available fashion by using the topology spread constraints feature. CAST AI supports these fault-domains, i.e. topology keys:

• topology.kubernetes.io/zone - enables your pods to be spread between availability zones, taking advantage of cloud redundancy.

!!! note ""
CAST AI will only create nodes in different fault-domains when the whenUnstatisfiable property equals DoNotSchedule. The value ScheduleAnyway means that the spread is just a preference, so the autoscaler will keep bin-packing those pods, which might result in all of them being scheduled on the same fault-domain.

The deployment described below will be spread and scheduled on all availability zones supported by your cluster:

apiVersion: apps/v1
kind: Deployment
metadata:
  labels:
    app: az-topology-spread
  name: az-topology-spread
spec:
  replicas: 30
  selector:
    matchLabels:
      app: az-topology-spread
  template:
    metadata:
      labels:
        app: az-topology-spread
    spec:
      topologySpreadConstraints:
        - maxSkew: 1
          topologyKey: topology.kubernetes.io/zone
          whenUnsatisfiable: DoNotSchedule
          labelSelector:
            matchExpressions:
              - key: app
                operator: In
                values:
                  - az-topology-spread
      containers:
        - image: nginx
          name: nginx

Scheduling on nodes with locally attached SSD

Storage optimized nodes have local SSDs backed by nvme drivers which provide higher throughput and lower latency than standard disks. It's an ideal choice for workloads that require efficient local storage.

Pods can request a storage optimized node by defining a node selector (or a required node affinity) and toleration for label scheduling.cast.ai/storage-optimized. Furthermore, pods can control the amount of available storage by specifying ephemeral storage resource requests. If resource requests aren't specified, CAST AI will still provision a storage optimized node but the available storage amount will be the lowest possible based on the cloud offerings.

Currently supported clouds for storage optimized nodes:

• AWS
• GCP

The pod described below will be scheduled on a node with locally attached SSD disks.

apiVersion: v1
kind: Pod
metadata:
  name: demopod
spec:
  nodeSelector:
    scheduling.cast.ai/storage-optimized: "true"
  tolerations:
    - key: scheduling.cast.ai/storage-optimized
      operator: Exists
  containers:
  - name: app
    image: nginx
    resources:
      requests:
        ephemeral-storage: "2Gi"
    volumeMounts:
    - name: ephemeral
      mountPath: "/tmp"
  volumes:
    - name: ephemeral
      emptyDir: {}

Scheduling on compute optimized nodes

Compute optimized instances are ideal for compute-bound applications that benefit from high-performance processors.
They offer the highest consistent performance per core to support real-time application performance.

The pod described below will be scheduled on a compute optimized instance.

apiVersion: v1
kind: Pod
metadata:
  name: demopod
spec:
  nodeSelector:
    scheduling.cast.ai/compute-optimized: "true"
  containers:
  - name: app
    image: nginx