Pod Compatibility for Joulie

Joulie uses a single pod annotation to express workload placement intent:

joulie.io/workload-class: performance | standard

The scheduler extender reads this annotation and steers pods accordingly. No node affinity rules are needed.

Workload classes

If no annotation is present, the extender treats it as standard.

Performance pod

Add the joulie.io/workload-class: performance annotation. The scheduler extender will reject eco nodes for this pod.

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apiVersion: v1
kind: Pod
metadata:
  name: my-critical-service
  annotations:
    joulie.io/workload-class: performance
spec:
  containers:
  - name: app
    image: ghcr.io/example/app:latest

Standard pod (default)

No annotation is required. The extender scores performance nodes higher but does not reject eco nodes.

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apiVersion: v1
kind: Pod
metadata:
  name: my-workload
spec:
  containers:
  - name: app
    image: ghcr.io/example/app:latest

You can also be explicit:

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apiVersion: v1
kind: Pod
metadata:
  name: my-workload
  annotations:
    joulie.io/workload-class: standard
spec:
  containers:
  - name: app
    image: ghcr.io/example/app:latest

GPU resource requests

GPU scheduling resources (nvidia.com/gpu, amd.com/gpu) are independent from Joulie workload classes.

• Request GPU resources as usual in pod/container resources.
• Set joulie.io/workload-class to express your placement intent.
• Joulie GPU capping is node-level (not per-container GPU slicing).

Example: a performance GPU inference pod:

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apiVersion: v1
kind: Pod
metadata:
  name: gpu-inference
  annotations:
    joulie.io/workload-class: performance
spec:
  containers:
  - name: inference
    image: ghcr.io/example/inference:latest
    resources:
      limits:
        nvidia.com/gpu: "1"