Proactive API Performance and Failure Prediction Platform Using Ensemble Learning and Time-Series Forecasting

The architectural design, implementation, and evaluation detailed throughout this paper demonstrate the successful deployment of a proactive API Performance and Failure Prediction Platform. By intentionally migrating monitoring tasks away from reactive, post-crash frameworks to a decoupled, type-safe stack consisting of a React.js frontend, a Node.js orchestrator, and a Python FastAPI ML service, the platform enhances operational efficiency. The system eliminates tracking fragmentation by consolidating multi-dimensional telemetry into a secure database layer managed via Prisma ORM. Furthermore, by utilizing dual machine learning models (XGBoost and Linear Regression), the platform removes human calculation delay from risk assessment and future performance forecasting loops. The system delivers rapid runtime execution, providing SRE teams with granular, early-warning insights regarding system capacity boundaries and impending failure vectors. While the current implementation demonstrates robust predictive accuracy, localized validation has highlighted distinct architectural parameters that bound the current scope. Chief among these is the application's current reliance on an embedded database file structure, which is optimal for localized M.Tech demonstrations but limits high-throughput concurrency. Additionally, the platform focuses on generating critical dashboard alerts ($\ge 70\%$ risk scores) for human mitigation rather than triggering autonomous infrastructure remediation pipelines. To address these limitations, future scope and system scaling will prioritize the seamless migration of the data persistence layer to a distributed, highly available SQL cluster. Because the backend services are database-agnostic, the platform is structured for containerization utilizing Docker, allowing orchestrated deployments via Kubernetes to monitor production-grade microservice clusters natively. Extensible alerting pipelines will be introduced by embedding third-party API hooks to dispatch real-time status alerts directly via communication streams. Finally, the platform's self-healing capabilities will be expanded, bridging the predictive analytics layer with automated webhook controllers capable of provisioning cloud infrastructure resources dynamically in response to predicted capacity failures.

[1] T. Chen and C. Guestrin, "XGBoost: A Scalable Tree Boosting System," Proc. 22nd ACM SIGKDD Int. Conf. Knowledge Discovery and Data Mining (KDD), San Francisco, CA, USA, 2016, pp. 785–794. [2] G. James, D. Witten, T. Hastie, and R. Tibshirani, An Introduction to Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2021. [3] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York, NY, USA: Springer, 2009. [4] F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825–2830, 2011. [5] Google, Site Reliability Engineering: How Google Runs Production Systems. Sebastopol, CA, USA: O'Reilly Media, 2016. [6] D. Beyer et al., The Site Reliability Workbook. Sebastopol, CA, USA: O'Reilly Media, 2018. [7] S. Newman, Building Microservices, 2nd ed. Sebastopol, CA, USA: O'Reilly Media, 2021. [8] C. Richardson, Microservices Patterns. Shelter Island, NY, USA: Manning Publications, 2018. [9] M. Kleppmann, Designing Data-Intensive Applications. Sebastopol, CA, USA: O'Reilly Media, 2017. [10] R. Fielding, Architectural Styles and the Design of Network-Based Software Architectures, Ph.D. dissertation, University of California, Irvine, USA, 2000.