Kubernetes Scheduler: How It Works & Best Practices

Kubernetes is a powerful container orchestration system, and one of its core components is the Scheduler. The Kubernetes Scheduler is responsible for placing pods onto the most suitable nodes in a cluster based on various constraints and policies. Understanding its inner workings is crucial for optimizing workloads and ensuring efficient resource utilization.

In this post, we’ll explore:

• Extending and customizing the scheduler
• What the Kubernetes Scheduler is
• How it makes scheduling decisions
• The scheduling workflow

What is the Kubernetes Scheduler?

The Kubernetes Scheduler is a control plane component that assigns unscheduled pods to available worker nodes. When a pod is created without a specific node assignment, the scheduler evaluates available nodes and determines the most appropriate one based on scheduling policies and resource constraints.

Key Responsibilities of the Scheduler:

• Assigning new pods to nodes
• Considering resource availability (CPU, memory, etc.)
• Applying scheduling constraints (taints, tolerations, affinity, etc.)
• Ensuring load balancing across the cluster

Kubernetes Scheduler Workflow

The scheduling process can be broken down into three key phases:

1. Filtering

The scheduler first filters out nodes not meeting the pod’s requirements. This process, called predicate filtering, ensures that only nodes capable of running the pod are considered.

Some common filtering criteria include:

• Node taints and tolerations (e.g., pods must tolerate taints to be scheduled on certain nodes)
• Node selector labels (ensuring the pod runs on a specific set of nodes)
• Resource availability (CPU, memory, ephemeral storage, etc.)
• Pod affinity and anti-affinity (ensuring co-location or separation of workloads)

2. Scoring

Once filtering is complete, the remaining nodes are ranked based on priority scores. The scheduler applies different scoring functions to determine the best-fit node.

Examples of scoring functions:

• LeastRequestedPriority → Prefer nodes with the most available resources
• BalancedResourceAllocation → Prefer nodes with a balanced resource usage
• NodeAffinityPriority → Prefer nodes that match node affinity rules
• InterPodAffinityPriority → Prefer nodes that improve pod locality

Each node is assigned a score (0-100), and the highest-scoring node is selected.

3. Binding

Finally, the scheduler binds the pod to the selected node by creating a Binding API object. The Kubelet on the chosen node then picks up the pod and starts running the container.

Advanced Scheduling Features

Kubernetes provides several advanced scheduling mechanisms to optimize workload placement:

Node Affinity & Anti-Affinity

• Node Affinity allows you to specify preferred or required nodes for pod scheduling.
• Pod Affinity/Anti-Affinity ensures that certain pods run together or separately.

affinity:
  nodeAffinity:
    requiredDuringSchedulingIgnoredDuringExecution:
      nodeSelectorTerms:
        - matchExpressions:
            - key: kubernetes.io/e2e-az-name
              operator: In
              values:
                - us-east-1a

This ensures the pod is scheduled only in us-east-1a.

Taints & Tolerations

Taints prevent pods from being scheduled on certain nodes unless they have a matching toleration.

tolerations:
  - key: "dedicated"
    operator: "Equal"
    value: "gpu-node"
    effect: "NoSchedule"

Custom Schedulers

If the default scheduler doesn’t meet your needs, you can create a custom scheduler.

• Scheduler Extender → Intercept the scheduling process and modify behavior
• Custom Scheduler → Write a completely new scheduler in Go language.

Example: Assigning a custom scheduler to a pod:

schedulerName: "my-custom-scheduler"

Monitoring and Debugging the Scheduler

To troubleshoot scheduling issues, you can check:

Events:

kubectl get events --sort-by=.metadata.creationTimestamp

Pod Description:

kubectl describe pod <pod-name>

Scheduler Logs:

kubectl logs -n kube-system kube-scheduler

Real-World Use Cases of Kubernetes Scheduler

High-Performance Computing (HPC) Workloads

Scenario: A financial services company needs to run intensive data processing jobs that require high-performance compute (HPC) nodes.

Solution:

• Use taints & tolerations to restrict scheduling to GPU-powered nodes.
• Implement node affinity to ensure jobs are placed on high-speed, low-latency nodes.
• Apply custom scoring functions to prioritize nodes with the least CPU/memory contention.

Multi-Tenant SaaS Platforms

Scenario: A SaaS provider runs applications for multiple customers and wants to isolate workloads while optimizing resource usage.

Solution:

• Implement resource quotas to prevent noisy neighbors from consuming excessive resources.
• Use node pools with different labels to ensure customers’ workloads are scheduled separately.
• Apply pod anti-affinity rules to prevent multiple workloads from a single tenant running on the same node, improving fault tolerance.

AI & Machine Learning Pipelines

Scenario: A machine learning team needs to train large models using GPU nodes while ensuring inference workloads are processed efficiently.

Solution:

• Use taints & tolerations to reserve GPU nodes for training jobs.
• Apply node affinity to place inference workloads on CPU-optimized nodes.
• Implement a custom scheduler to dynamically allocate GPUs based on model training priority.

The Kubernetes Scheduler is a crucial component for managing workloads efficiently. Understanding its decision-making process helps in optimizing cluster performance, reducing resource contention, and ensuring high availability. By leveraging advanced features like node affinity, taints, and custom schedulers, you can fine-tune scheduling to meet specific application requirements.