Volcano

related-skills: cncf-argo, cncf-artifact-hub, cncf-aws-eks, cncf-azure-aks

Volcano in Cloud-Native Engineering

Category: Scheduling & Orchestration
Status: Active
Stars: 2,800
Last Updated: 2026-04-22
Primary Language: Go
Documentation: Batch scheduling infrastructure for Kubernetes

Purpose and Use Cases

Volcano is a core component of the cloud-native ecosystem, serving as for Kubernetes

What Problem Does It Solve?

Volcano addresses the challenge of batch job scheduling in Kubernetes. It provides advanced scheduling, gang scheduling, and queue management.

When to Use This Project

Use Volcano when need batch scheduling, require gang scheduling, or manage ML workloads. Not ideal for simple deployments or when ML training jobs, batch processing, or complex scheduling requirements.

Key Use Cases

• ML Training Job Scheduling
• Batch Job Management
• Gang Scheduling
• Queue-based Resource Management
• Multi-tenant Job Scheduling

Architecture Design Patterns

Core Components

• Scheduler: Core scheduling engine
• Job Controller: Job lifecycle management
• Queue Controller: Queue management
• Scheduler Plugin: Extensible scheduling plugins
• Resource Manager: Resource allocation

Component Interactions

1. Job → Scheduler: Job requests scheduling
2. Scheduler → Node: Scheduler allocates nodes
3. Queue → Scheduler: Queue enforces resource limits
4. Job → Pod: Job creates pods

Data Flow Patterns

1. Job Submission: Job created → Job controller → Scheduler
2. Scheduling Decision: Scheduler evaluates → Allocates nodes
3. Pod Creation: Job creates pods → Pods scheduled
4. Queue Enforcement: Queue enforces limits → Scheduler respects

Design Principles

• Batch Support: Full batch job support
• Gang Scheduling: All-or-nothing scheduling
• Extensible: Plugin-based architecture
• Fairness: Fair resource sharing

Integration Approaches

Integration with Other CNCF Projects

• Kubernetes: Core platform
• Kube-batch: Scheduling backend
• PyTorch: ML workloads
• TensorFlow: ML workloads

API Patterns

• Job CRD: Job definition
• Queue CRD: Queue definition
• Scheduling Profile: Scheduling profile
• Scheduler Plugin: Plugin interface

Configuration Patterns

• Volcano Config: Volcano configuration
• Queue Config: Queue configuration
• Job Config: Job configuration
• Scheduler Config: Scheduler settings

Extension Mechanisms

• Custom Scheduler Plugins: Add scheduling plugins
• Custom Queues: Custom queue logic
• Custom Job Types: Custom job definitions

Common Pitfalls and How to Avoid Them

Misconfigurations

• Queue Resource: Queue resource limits not enforced
  • How to Avoid: Configure queue resources, monitor usage
• Priority Issues: Priority inversion
  • How to Avoid: Configure priorities, check queue priorities

Performance Issues

• Job Status: Job status not updating
  • How to Avoid: Check job controller, verify pod statuses
• Queue Conflict: Queue conflicts
  • How to Avoid: Configure queue priorities, avoid conflicts

Operational Challenges

• Plugin Issues: Plugin loading issues
  • How to Avoid: Verify plugin configuration, check logs
• Scaling Issues: Scheduler scalability
  • How to Avoid: Scale scheduler, optimize configuration

Security Pitfalls

Coding Practices

Idiomatic Configuration

• Job Design: Design jobs for batch scheduling
• Queue Management: Manage queues effectively
• Priority Configuration: Configure job priorities

API Usage Patterns

• volcano: Volcano CLI
• kubectl apply: Apply job configurations
• volcano schedctl: Scheduler CLI
• kubectl describe: Describe job status

Observability Best Practices

• Job Metrics: Track job performance
• Scheduler Metrics: Monitor scheduler health
• Queue Metrics: Track queue utilization

Testing Strategies

• Integration Tests: Test job scheduling
• Gang Tests: Test gang scheduling
• Performance Tests: Validate scheduling performance

Development Workflow

• Local Development: Use kind or minikube
• Debug Commands: Check volcano logs
• Test Environment: Set up test cluster
• CI/CD Integration: Automate testing
• Monitoring Setup: Configure observability
• Documentation: Maintain documentation

Fundamentals

Essential Concepts

• Job: Batch job definition
• Queue: Resource queue
• Scheduler: Scheduling engine
• Gang: Gang scheduling group
• Priority: Priority level
• Queue: Resource queue
• Plugin: Scheduling plugin
• Profile: Scheduling profile

Terminology Glossary

• Job: Batch job
• Queue: Resource queue
• Gang: Gang group
• Priority: Priority level
• Scheduler: Scheduler engine

Data Models and Types

• Job: Job definition
• Queue: Queue definition
• SchedulingProfile: Scheduling profile
• Plugin: Scheduler plugin

Lifecycle Management

• Job Submission: Job created → Job controller → Scheduled → Running
• Scheduling: Job submitted → Scheduler evaluates → Nodes allocated
• Gang Scheduling: Gang all ready → Gang started
• Queue Enforcement: Job submitted → Queue checks → Job scheduled

State Management

• Job State: Pending, running, or completed
• Queue State: Active or paused
• Scheduler State: Running or degraded
• Pod State: Scheduled, running, or pending

Scaling and Deployment Patterns

Horizontal Scaling

• Scheduler Scaling: Scale scheduler replicas
• Job Scaling: Scale jobs across queues
• Node Scaling: Add nodes to cluster

High Availability

• Scheduler HA: Multiple scheduler replicas
• Job HA: Job replica management
• Queue HA: Queue redundancy

Production Deployments

• Cluster Setup: Deploy volcano cluster
• Queue Configuration: Configure queues
• Network Configuration: Configure network
• Security Setup: Enable RBAC
• Monitoring Setup: Configure metrics
• Logging Setup: Centralize logs
• Resource Quotas: Set resource limits
• Performance Tuning: Optimize scheduling

Upgrade Strategies

• Volcano Upgrade: Upgrade volcano components
• Queue Migration: Migrate queue configurations
• Testing: Verify functionality

Resource Management

• CPU Resources: CPU limits
• Memory Resources: Memory limits
• Storage Resources: Storage configuration
• Network Resources: Network configuration

Additional Resources

• Official Documentation: https://volcano.sh/docs/
• GitHub Repository: Check the project's official documentation for repository link
• CNCF Project Page: cncf.io/projects/cncf-volcano/
• Community: Check the official documentation for community channels
• Versioning: Refer to project's release notes for version-specific features

Troubleshooting

Common Issues

1. Deployment Failures

   • Check pod logs for errors
   • Verify configuration values
   • Ensure network connectivity

2. Performance Issues

   • Monitor resource usage
   • Adjust resource limits
   • Check for bottlenecks

3. Configuration Errors

   • Validate YAML syntax
   • Check required fields
   • Verify environment-specific settings

4. Integration Problems

   • Verify API compatibility
   • Check dependency versions
   • Review integration documentation

Getting Help

• Check official documentation
• Search GitHub issues
• Join community channels
• Review logs and metrics Content generated automatically. Verify against official documentation before production use.

Examples

Basic Configuration

# Basic configuration example
apiVersion: v1
kind: ConfigMap
metadata:
  name: {{project_name}}-config
  namespace: default
data:
  # Configuration goes here
  config.yaml: |
    # Base configuration
    # Add your settings here

Kubernetes Deployment

# Kubernetes deployment for {{project_name}}
apiVersion: apps/v1
kind: Deployment
metadata:
  name: {{project_name}}
  namespace: default
spec:
  replicas: 1
  selector:
    matchLabels:
      app: {{project_name}}
  template:
    metadata:
      labels:
        app: {{project_name}}
    spec:
      containers:
      - name: {{project_name}}
        image: {{project_name}}:latest
        ports:
        - containerPort: 8080
        resources:
          limits:
            memory: "128Mi"
            cpu: "500m"

Kubernetes Service

# Kubernetes service for {{project_name}}
apiVersion: v1
kind: Service
metadata:
  name: {{project_name}}
  namespace: default
spec:
  selector:
    app: {{project_name}}
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8080
  type: ClusterIP

When to Use

Use this skill when:

• Integrating a CNCF project into Kubernetes infrastructure — You need to configure, deploy, or troubleshoot a cloud-native tool within a cluster
• Designing cloud-native architecture — You are selecting and integrating CNCF tools to solve specific infrastructure challenges
• Resolving operational issues — A CNCF component is misbehaving, underperforming, or needs configuration changes

Core Workflow

1. Assess Requirements — Understand the use case, scale, integration needs, and existing infrastructure. Checkpoint: Document requirements, constraints, and success criteria.

2. Design Architecture — Plan component interactions, data flow, and deployment strategy using cloud-native best practices. Checkpoint: Verify the architecture addresses all requirements and follows CNCF conventions.

3. Implement & Configure — Create manifests, configurations, and deployment scripts. Include resource limits, health checks, and observability hooks. Checkpoint: Validate all YAML against schema and test in a staging environment.

4. Deploy & Monitor — Apply manifests to the cluster, verify component health, and confirm observability is working. Checkpoint: Confirm all pods/services are running, probes passing, and metrics/alerts configured.

Constraints

MUST DO

• Include at least one complete working YAML manifest example
• Note when content is auto-generated vs. manually verified
• Reference relevant CNCF project documentation

MUST NOT DO

• Deploy manifests without testing in a staging environment first
• Use deprecated API versions (e.g., apps/v1beta1)
• Omit resource limits and requests in Kubernetes manifests