This project demonstrates my expertise in implementing dynamic autoscaling solutions in Kubernetes using the Horizontal Pod Autoscaler (HPA). Through hands-on implementation, I"ve created a robust system that automatically adjusts application resources based on demand, showcasing modern cloud-native architecture principles and DevOps best practices.
- Implement production-grade autoscaling using Kubernetes HPA
- Master resource management with precise CPU and memory configurations
- Deploy scalable NGINX workloads with optimal performance
- Configure advanced metrics-based scaling policies
- Demonstrate deep understanding of Kubernetes orchestration
The project implements a sophisticated autoscaling architecture that dynamically manages application resources:
graph TD
HPA[Horizontal Pod Autoscaler] -->|Reads Metrics| MS[Metrics Server]
HPA -->|Scales| D[Deployment]
D -->|Manages| RS[ReplicaSet]
RS -->|Creates| P1[Pod 1]
RS -->|Creates| P2[Pod 2]
RS -->|Creates| P3[Pod 3]
RS -->|Creates| PN[Pod N]
- Container Orchestration: Kubernetes 1.28+
- Web Server: NGINX (Latest)
- Resource Management: Kubernetes HPA
- Metrics: Kubernetes Metrics Server
- Infrastructure: Compatible with any Kubernetes cluster (local or cloud)
🐳 Prerequisites
- Kubernetes cluster (1.28+)
- kubectl CLI tool
- Metrics Server installed
- Basic understanding of Kubernetes concepts
⚙️ Installation & Setup
-
Clone the repository:
git clone https://github.com/TheToriqul/k8s-autoscaling.git cd k8s-autoscaling
-
Deploy the NGINX application:
kubectl apply -f nginx-deployment.yaml
-
Configure autoscaling:
kubectl apply -f nginx-hpa.yaml
- Advanced Kubernetes autoscaling mechanisms and architecture
- Resource optimization techniques for containerized applications
- Metric-based scaling strategies and implementation
- Production-grade deployment configurations
- Performance monitoring and optimization in Kubernetes
- Cloud-native architecture design principles
- DevOps best practices for scalable applications
- System reliability engineering concepts
- Performance optimization strategies
- Infrastructure automation techniques
View Planned Improvements
- Custom metrics implementation for more granular scaling
- Integration with cloud provider-specific autoscaling features
- Advanced monitoring and alerting setup
- Performance benchmarking tools
- Automated testing framework for scaling behaviors
- Cost optimization analysis tools
Contributions are welcome! Feel free to open an issue or submit a pull request.
- 📧 Email: [email protected]
- 📱 Phone: +65 8936 7705, +8801765 939006
- 🌐 LinkedIn: @TheToriqul
- 🐙 GitHub: @TheToriqul
- 🌍 Portfolio: TheToriqul.com
- Poridhi for providing comprehensive learning resources
- The Kubernetes community for excellent documentation
- NGINX team for maintaining a reliable web server image
Thank you for exploring my Kubernetes autoscaling project. This implementation demonstrates my capabilities in cloud-native technologies and DevOps practices. Happy scaling! 🚀