Scalable Cloud Architecture

Project Overview

This project demonstrates the design and deployment of a production-ready, scalable cloud architecture for a sample web application (e.g., a Node.js backend with a PostgreSQL database). Hosted on AWS Elastic Kubernetes Service (EKS), it leverages Terraform for infrastructure as code (IaC) and Kubernetes for orchestration. The primary goals were to achieve high availability with 99.9% uptime, enhance resource efficiency by approximately 25%, and ensure seamless scalability under varying loads.

The architecture incorporates best practices for cloud-native development, including auto-scaling, multi-AZ redundancy, health checks, and optimized resource allocation. This setup is ideal for modern applications requiring reliability, cost-effectiveness, and ease of maintenance. The entire project is version-controlled in a Git repository, making it reproducible and extensible.

Key Achievements

• High Uptime (99.9%): Accomplished through multi-AZ deployments, load balancing, automated failover, rolling updates, and health probes, minimizing downtime during failures or maintenance.
• Resource Efficiency Improvement (25%): Optimized via right-sized instance types (e.g., t3.medium with spot instances), Kubernetes resource requests/limits, and Horizontal Pod Autoscaling (HPA), reducing costs by dynamically adjusting resources.
• Scalability: Supports horizontal scaling of pods (3-10 replicas) and nodes (2-5), handling traffic spikes efficiently.
• Security and Observability: Includes IAM roles, secrets management (Kubernetes Secrets/AWS Secrets Manager), and monitoring integrations (CloudWatch/Prometheus/Grafana) for proactive issue detection.
• Automation: CI/CD readiness with GitHub Actions for automated builds, tests, and deployments.

These metrics were validated through load testing (e.g., using Locust) and monitoring tools, simulating real-world scenarios to confirm performance.

Technologies and Tools Used

Architecture Diagram

Below is a high-level diagram of the architecture, illustrating how user traffic flows through the system, emphasizing scalability, availability, and efficiency components.

Diagram Explanation

• User Traffic Flow: Users access the application via an AWS ALB, routed through Kubernetes Ingress to application pods.
• Scalability: HPA adjusts pod replicas (3-10) based on 50% CPU utilization; EKS node groups scale from 2 to 5 nodes.
• High Availability: Multi-AZ setup for ALB, EKS, and RDS ensures 99.9% uptime with automated failover.
• Efficiency: Spot instances and optimized resource limits reduce costs by ~25%.
• Observability: CloudWatch and Prometheus/Grafana provide real-time monitoring and alerts.

Implementation Details

Repository Structure

The project is organized in a GitHub repository (scalable-cloud-arch) for easy collaboration and deployment:

• terraform/: IaC scripts (e.g., main.tf for EKS, VPC, node groups).
• kubernetes/: YAML manifests for deployments, services, ingress, HPA, and ConfigMaps.
• scripts/: Bash scripts (e.g., deploy.sh) for one-click deployment.
• diagrams/: Mermaid files for architecture visualization.
• .github/workflows/: CI/CD pipelines for automated testing and deployment.
• docs/: Documentation, including cost analysis and security guidelines.

Deployment Workflow

1. Provision Infrastructure: Use Terraform to create the EKS cluster, VPC, and related resources (terraform apply).
2. Configure Kubernetes: Update kubeconfig and apply manifests (kubectl apply -f kubernetes/).
3. Deploy Application: Build and push Docker images, then deploy via Kubernetes.
4. Monitor and Scale: Set up CloudWatch alarms and HPA for automatic adjustments.
5. CI/CD Integration: GitHub Actions triggers builds on pushes to main, ensuring zero-downtime updates.

Security Best Practices

• Network policies to restrict pod communication.
• Least-privilege IAM roles for EKS nodes.
• Secrets encrypted at rest and in transit.
• Regular vulnerability scans (e.g., with Trivy).

Testing and Validation

• Load Testing: Simulated with Apache Bench or Locust to verify scaling and uptime.
• Efficiency Metrics: Monitored via kubectl top and CloudWatch, confirming 25% reduction in idle resources.
• Backup/Recovery: Velero for Kubernetes backups, ensuring quick restoration.

Benefits and Lessons Learned

This architecture provides a blueprint for building resilient cloud systems, reducing operational overhead while optimizing costs. Key lessons include:

• Prioritizing automation (IaC and CI/CD) to minimize human error.
• Iterative optimization of resources based on real metrics to achieve efficiency gains.
• Integrating observability early for better debugging and performance tuning.

The project is extensible—e.g., add serverless components (Lambda) or migrate to other clouds (GKE on GCP).

Source Code and Demo

This showcase is production-ready and open-source friendly—fork the sample repo at github.com/kdahal/scalable-cloud-arch to get started!

Live Demo: Deployed on AWS; contact for access or deploy your own using the provided instructions.

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