Kubernetes Multiple Schedulers: A Step-by-Step Guide to Implementing a Custom Scheduler

Introduction

Kubernetes is a powerful container orchestration platform, and its scheduler plays a critical role in managing workloads. The Kubernetes scheduler is responsible for assigning pods to nodes based on factors like resource availability and scheduling policies. While the default scheduler handles most use cases efficiently, there are scenarios where using multiple schedulers or a custom scheduler becomes necessary.

For instance, specific workloads might have unique requirements like dedicated resources, high-priority execution, or custom affinity/anti-affinity rules. This guide will walk you through the concept of Kubernetes schedulers, the need for multiple schedulers, and the process of implementing a custom scheduler with practical examples.

Section 1: Understanding Kubernetes Scheduling

What is a Kubernetes Scheduler?

The Kubernetes scheduler is a control plane component that assigns unscheduled pods to suitable nodes in a cluster. It evaluates nodes based on several criteria, including:

• Resource availability (CPU, memory, storage).
• Affinity/anti-affinity rules.
• Taints and tolerations.
• Pod priority and preemption.

How the Default Scheduler Works

1. Filter Nodes: The scheduler identifies nodes that meet the resource requirements of a pod.
2. Rank Nodes: It evaluates and scores the eligible nodes using predefined algorithms (e.g., least resource usage).
3. Assign Pod: The pod is bound to the highest-scoring node.

When to Use Multiple Schedulers

Using multiple schedulers is beneficial when workloads have distinct requirements:

• Real-time applications that need low-latency scheduling.
• Batch processing jobs that can run on lower-priority nodes.
• Custom policies, such as scheduling pods based on geographic location or energy efficiency.

Section 2: Overview of Custom Schedulers

What is a Custom Scheduler?

A custom scheduler is a user-defined component that overrides or supplements the default Kubernetes scheduler to meet specific needs.

Advantages of Custom Schedulers

• Flexibility: Implement specialized scheduling logic.
• Optimization: Tailor scheduling to workload characteristics.
• Separation: Avoid contention by isolating workloads with different requirements.

Kubernetes Scheduler Architecture

• Scheduler API: Interacts with the Kubernetes control plane to retrieve pod information and bind pods to nodes.
• Extensibility: Plugins and frameworks can be used to extend or customize scheduling behavior.

Section 3: Setting Up a Kubernetes Cluster

To create and test a custom scheduler, set up a Kubernetes cluster:

Using Minikube

1. Install Minikube:

   curl -LO https://storage.googleapis.com/minikube/releases/latest/minikube-linux-amd64
   sudo install minikube-linux-amd64 /usr/local/bin/minikube

1. Start the cluster:

   minikube start

1. Verify the cluster:

   kubectl get nodes

Alternatively, use cloud-based solutions like AWS EKS or Google Kubernetes Engine (GKE) for larger setups.

Section 4: Creating a Custom Scheduler

Step 1: Define Scheduler Requirements

Before implementing a custom scheduler, define the scheduling logic. For example:

• Schedule pods with specific labels to certain nodes.
• Ensure pods with high-priority annotations are scheduled first.

Step 2: Develop the Scheduler

Here’s a basic Go implementation for a custom scheduler:

Code Example: Simple Scheduler

package main

import (
    "context"
    "fmt"
    "log"

    corev1 "k8s.io/api/core/v1"
    metav1 "k8s.io/apimachinery/pkg/apis/meta/v1"
    "k8s.io/client-go/kubernetes"
    "k8s.io/client-go/tools/clientcmd"
)

func main() {
    kubeconfig := "/path/to/kubeconfig"
    config, err := clientcmd.BuildConfigFromFlags("", kubeconfig)
    if err != nil {
        log.Fatalf("Error loading kubeconfig: %v", err)
    }

    clientset, err := kubernetes.NewForConfig(config)
    if err != nil {
        log.Fatalf("Error creating Kubernetes client: %v", err)
    }

    // List unscheduled pods
    pods, err := clientset.CoreV1().Pods("").List(context.TODO(), metav1.ListOptions{
        FieldSelector: "spec.nodeName=",
    })
    if err != nil {
        log.Fatalf("Error listing unscheduled pods: %v", err)
    }

    for _, pod := range pods.Items {
        fmt.Printf("Found unscheduled pod: %s\n", pod.Name)
        // Simple scheduling logic: Assign to first available node
        nodes, _ := clientset.CoreV1().Nodes().List(context.TODO(), metav1.ListOptions{})
        if len(nodes.Items) > 0 {
            nodeName := nodes.Items[0].Name
            fmt.Printf("Assigning pod %s to node %s\n", pod.Name, nodeName)

            pod.Spec.NodeName = nodeName
            _, err := clientset.CoreV1().Pods(pod.Namespace).Update(context.TODO(), &pod, metav1.UpdateOptions{})
            if err != nil {
                log.Printf("Failed to schedule pod %s: %v", pod.Name, err)
            }
        }
    }
}

Step 3: Build and Deploy the Scheduler

1. Build the scheduler binary:

   go build -o custom-scheduler .

1. Create a Docker image:

   FROM golang:1.16 AS builder
   WORKDIR /app
   COPY . .
   RUN go build -o custom-scheduler .

   FROM alpine:latest
   COPY --from=builder /app/custom-scheduler /custom-scheduler
   ENTRYPOINT ["/custom-scheduler"]

1. Push the image to a container registry and deploy it in Kubernetes.

Step 4: Register the Custom Scheduler

Modify the pod specification to use your scheduler by adding an annotation:

apiVersion: v1
kind: Pod
metadata:
  name: example-pod
  annotations:
    scheduler.alpha.kubernetes.io/name: custom-scheduler
spec:
  containers:
  - name: busybox
    image: busybox
    command: ["sleep", "3600"]

Section 5: Testing the Custom Scheduler

1. Deploy Pods with Scheduler Annotation: Deploy a test pod or deployment targeting your scheduler.
2. Verify Scheduling: Use kubectl describe and kubectl logs to ensure the scheduler assigns pods correctly:

   kubectl get pods -o wide
   kubectl logs deployment/custom-scheduler

Section 6: Advanced Scheduling Techniques

Advanced custom scheduling can include:

• Custom Metrics: Schedule based on real-time metrics like latency or disk I/O.
• Priorities and Preemption: Ensure critical workloads are prioritized.
• Plugins: Use the Kubernetes scheduling framework to create plugins for additional functionality.

Section 7: Troubleshooting Common Issues

• Scheduler Logs: Check logs for errors or unhandled cases.
• Pod Events: Use kubectl describe pod to view conditions and events.
• Resource Conflicts: Ensure nodes have sufficient resources for pods.

Conclusion

Custom schedulers in Kubernetes offer flexibility and efficiency for managing workloads with unique requirements. By implementing a custom scheduler, you can optimize resource utilization, prioritize critical workloads, and tailor scheduling to specific business needs. Start experimenting with custom schedulers to unlock the full potential of Kubernetes.

Call to Action

• Try building your own custom scheduler using the examples provided.
• Explore Kubernetes documentation for in-depth insights into the Scheduler API.
• Share your custom scheduling use cases and learnings with the Kubernetes community!