Docker and k8s: Scheduler Profiles

In Kubernetes, the kube-scheduler is a key component that ensures the efficient allocation of resources. With Kubernetes 1.18 and later versions, you can use Scheduler Profiles to fine-tune the scheduling process according to specific workload requirements.

What are Scheduler Profiles?

A Scheduler Profile allows you to configure the behavior of the kube-scheduler. Each profile is associated with a separate scheduling queue, allowing multiple profiles to coexist in a single kube-scheduler instance.

Why Use Scheduler Profiles?

Scheduler Profiles provide a high level of customization for scheduling in Kubernetes. By tailoring the scheduling process to the specific needs of your workloads, you can optimize resource usage and improve the performance and stability of your applications.

Configuring Scheduler Profiles

To use Scheduler Profiles, you need to modify the kube-scheduler's configuration file. Here's an example:

apiVersion: kubescheduler.config.k8s.io/v1beta1
kind: KubeSchedulerConfiguration
profiles:
  - schedulerName: default-scheduler
    plugins:
      score:
        disabled:
        - name: NodeResourcesLeastAllocated
        enabled:
        - name: NodeResourcesMostAllocated
  - schedulerName: throughput-scheduler
    plugins:
      score:
        disabled:
        - name: NodeResourcesLeastAllocated
        enabled:
        - name: NodeResourcesBalancedAllocation

In this example, we've defined two profiles: default-scheduler and throughput-scheduler. Each profile has a different set of scoring plugins enabled, which will influence how the scheduler ranks nodes for Pod placement.

Applying Scheduler Profiles

In your Pod specifications, you can use the schedulerName field to specify which scheduler profile should be used. Here's an example:

apiVersion: v1
kind: Pod
metadata:
  name: custom-scheduler-pod
spec:
  schedulerName: throughput-scheduler
  containers:
  - name: custom-scheduler-container
    image: my-container-image

In conclusion, Scheduler Profiles offer a powerful tool for optimizing scheduling in Kubernetes. They provide the flexibility to customize the scheduling process to better suit the unique requirements of your workloads.