A production-ready Learning Management System (LMS) deployed on Kubernetes with comprehensive orchestration features including self-healing, auto-scaling, zero-downtime deployments, and load balancing.

• 🔐 User Management: Role-based access control (Admin, Educator, Learner)
• 📚 Course Management: Create, update, archive courses with rich content
• 📊 Progress Tracking: Automated learner progress monitoring
• 🎖️ Certificate Generation: Automatic digital certificates on completion
• 📱 QR Code Integration: Quick course access via mobile devices
• 💬 Social Learning: Comments and interactions on courses
• 🔄 Zero-Downtime Deployments: Rolling updates with Kubernetes
• ⚡ Auto-Scaling: Dynamic scaling based on load
• 🛡️ Self-Healing: Automatic pod recovery and health checks

┌─────────────────────────────────────────────────────────┐
│                     Load Balancer                        │
│                  (NodePort: 30080)                       │
└────────────────────────┬────────────────────────────────┘
                         │
        ┌────────────────┼────────────────┐
        │                │                │
   ┌────▼─────┐    ┌────▼─────┐    ┌────▼─────┐
   │  Nginx   │    │  Django  │    │  Django  │
   │  Proxy   │    │  Web-1   │    │  Web-2   │
   └──────────┘    └─────┬────┘    └─────┬────┘
                         │                │
                    ┌────▼────────────────▼────┐
                    │       MySQL 8.0          │
                    │    (Persistent Volume)    │
                    └──────────────────────────┘

• 3 Linux VMs (1 master + 2 workers)
  • Master: 2 vCPU, 4GB RAM
  • Workers: 2 vCPU, 2GB RAM each
• Ubuntu 22.04 LTS or later
• Static IP addresses for external access
• SSH access to all nodes
• Docker (for image building)

git clone https://github.com/abhishektang/elevate-learning-k8s.git
cd elevate-learning-k8s

On Master Node:

cd All_mds
chmod +x install-k3s-master.sh
sudo ./install-k3s-master.sh

On Each Worker Node:

# Copy token from master: sudo cat /var/lib/rancher/k3s/server/node-token
chmod +x install-k3s-worker.sh
# Edit script with your master IP and token
sudo ./install-k3s-worker.sh

# Build Docker image
cd elevatelearning
docker build -t elevatelearning-web:latest .

# Import to K3s
sudo k3s ctr images import elevatelearning-web.tar

# Deploy to Kubernetes
cd k8s
kubectl apply -f namespace.yaml
kubectl apply -f configmap.yaml
kubectl apply -f mysql-deployment.yaml
kubectl apply -f django-deployment.yaml
kubectl apply -f nginx-deployment.yaml

http://<MASTER_IP>:30080/elevatelearning/home/

Default Admin Credentials:

• Username: admin
• Password: admin123

Run comprehensive tests to verify Kubernetes orchestration features:

chmod +x run-orchestration-tests.sh
./run-orchestration-tests.sh

Tests Included:

• ✅ Self-Healing: Automatic pod recovery
• ✅ Load Balancing: Traffic distribution across pods
• ✅ Auto-Scaling: Dynamic replica management (3→5→3)
• ✅ Rolling Updates: Zero-downtime deployments
• ✅ Rollback: Instant recovery to previous versions

Expected output: 5/5 tests passed ✨

elevate-learning-k8s/
├── elevatelearning/              # Django application
│   ├── elevatelearning/          # Project settings
│   ├── elevatelearningapp/       # Main app (views, models, URLs)
│   ├── templates/                # HTML templates
│   ├── k8s/                      # Kubernetes manifests
│   │   ├── namespace.yaml
│   │   ├── configmap.yaml
│   │   ├── mysql-deployment.yaml
│   │   ├── django-deployment.yaml
│   │   └── nginx-deployment.yaml
│   ├── Dockerfile                # Container image definition
│   ├── requirements.txt          # Python dependencies
│   ├── entrypoint.sh            # Container startup script
│   └── nginx.conf               # Nginx reverse proxy config
├── All_mds/                      # Documentation & scripts
│   ├── install-k3s-master.sh    # Master node setup
│   ├── install-k3s-worker.sh    # Worker node setup
│   ├── deploy-to-k8s.sh         # Deployment automation
│   ├── K3S_SETUP_GUIDE.md       # Cluster setup guide
│   ├── DEPLOYMENT.md            # Deployment instructions
│   ├── ARCHITECTURE.md          # Architecture documentation
│   └── PROJECT_PROPOSAL.md      # Project overview
├── run-orchestration-tests.sh   # Automated test suite
├── ORCHESTRATION_TEST_SCRIPT_README.md
├── QUICK_START_TESTS.md
├── .gitignore                   # Git ignore rules
└── README.md                    # This file

Key configurations in k8s/configmap.yaml:

CSRF_TRUSTED_ORIGINS: "http://<YOUR_IP>:30080"
ALLOWED_HOSTS: "*"
DB_HOST: "mysql-service"
DB_NAME: "elevatelearning_db"
DB_USER: "djangouser"
DEBUG: "False"

• ✅ CSRF Protection: Token-based form validation
• ✅ Secret Management: Kubernetes secrets for sensitive data
• ✅ Role-Based Access: Admin, Educator, Learner roles
• ✅ Network Policies: Pod-to-pod communication restrictions
• ✅ Security Headers: X-Frame-Options, CSP, HSTS

⚠️ Important: Never commit:

• SSH private keys
• Database passwords
• Django SECRET_KEY
• .env files

All sensitive files are excluded via .gitignore.

Health Check Endpoints:

• Liveness Probe: /elevatelearning/home/
• Readiness Probe: /elevatelearning/home/

Check Cluster Status:

kubectl get nodes -o wide
kubectl get pods -n elevatelearning -o wide
kubectl get services -n elevatelearning

This project is cloud-agnostic and can be deployed on:

• ✅ GCP - Compute Engine VMs
• ✅ AWS - EC2 instances with EKS or K3s
• ✅ Azure - Azure VMs with AKS or K3s
• ✅ On-Premises - Any Linux infrastructure

See documentation for migration guides.

• 📖 K3s Setup Guide - Complete cluster setup
• 🚀 Deployment Instructions - Step-by-step deployment
• 🏛️ Architecture Overview - System design
• ✅ Requirements Verification - Feature checklist
• 🔄 Orchestration Tests - Testing documentation

This is an academic project, but suggestions are welcome!

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (git commit -m 'Add amazing feature')
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request

This project is licensed under the MIT License - see the LICENSE file for details.

For issues or questions:

1. Check the documentation
2. Review troubleshooting guides
3. Open an issue on GitHub

⭐ Star this repo if you find it helpful! ⭐

Built with ❤️ using Django, Kubernetes, and Cloud Native technologies.