Alauda AI

Configuring fair sharing

Fair sharing is a preemption strategy that is used to achieve an equal or weighted share of borrowable resources between the tenants of a cohort. Borrowable resources are the unused nominal quota of all the cluster queues in a cohort.

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Cluster queue weights

Share values are represented as the weight value in a ClusterQueue object. Share values are important because they allow administrators to prioritize specific job types or teams. Critical applications or high-priority teams can be configured with a weighted value so that they receive a proportionally larger share of the available resources. Configuring weights ensures that unused resources are distributed according to defined organizational or project priorities rather than on a first-come, first-served basis.

The weight value, or share value, defines a comparative advantage for the cluster queue when competing for borrowable resources. Generally, Alauda Build of Kueue admits jobs with a lower share value first. Jobs with a higher share value are more likely to be preempted before those with lower share values.

Example cluster queue with a fair sharing weight configured

apiVersion: kueue.x-k8s.io/v1beta2
kind: ClusterQueue
metadata:
  name: cluster-queue
spec:
  namespaceSelector: {}
  resourceGroups:
  - coveredResources: ["cpu"]
    flavors:
    - name: default-flavor
      resources:
      - name: cpu
        nominalQuota: 9
  cohort: example-cohort
  fairSharing:
    weight: 2

If you don't set the weight, the default value is 1. A weight value of 0 represents an infinite share value. This means that the cluster queue is always at a disadvantage compared to others, and its workloads are always the first to be preempted when fair sharing is enabled.