Cloud Native Evaluator Application: Based on Devops pipeline

Manual evaluation of academic submissions in universities often suffers from latency, scalability bottlenecks, and security vulnerabilities. To address these challenges, we propose a cloud-native evaluator application that integrates DevOps pipelines for automated security scanning, Kubernetes-driven scalability, and a responsive web interface. The system employs SonarQube for static code analysis and Snyk for dependency vulnerability detection within a GitLab CI/CD pipeline, ensuring secure and compliant deployments. The frontend, designed using Figma and built with React And Tailwind CSS, offers an intuitive user interface for real-time plagiarism checks and evaluator dashboards. The backend leverages AWS services, including DynamoDB for NoSQL data storage, RDS for structured data management, VPC for network isolation, and CloudFront CDN to minimize latency. Kubernetes orchestrates containerized workloads, enabling horizontal auto-scaling to accommodate fluctuating demand during peak academic evaluation periods. Prometheus and Grafana provide real-time monitoring and logging, ensuring system reliability and performance visibility. Experimental results demonstrate a 60% reduction in deployment latency through optimized CI/CD stages, 98% accuracy in pre-deployment vulnerability detection, and seamless scalability to 1,000+ concurrent users with Kubernetes auto-scaling. The integration of SonarQube and Snyk reduced critical security risks by 85% compared to traditional manual audits. Additionally, the CloudFront CDN improved page load times by 40%, enhancing user experience for geographically distributed evaluators. This approach bridges the gap between academic evaluation efficiency and enterprise-grade security, offering a robust framework for institutions transitioning to cloud-native architectures. Future work includes extending the model to multi-cloud environments and incorporating AI-driven anomaly detection for suspicious activity monitoring.