An Intelligent Machine Learning Framework for Predictive Diagnostics in Cloud-Based Database Replication Systems

Abstract

Preemptive failure detection in database replication systems is an essential need for ensuring data consistency and system availability in distributed enterprise settings. This article describes a state-of-the-art machine learning framework that learns to predict replication anomalies before they appear as service-affecting failures. The suggested architecture consumes heterogeneous data streams from Oracle GoldenGate and MySQL replication scenarios, such as structured metrics and unstructured logs, and converts them into predictive features using advanced preprocessing methods. Three co-operating machine learning models—Random Forest for binary classification, Gradient Boosting for multi-class categorization, and Long Short-Term Memory networks for sequential pattern recognition—collaborate to detect elusive predictors of replication instability. The model shows marked improvement over conventional threshold-based monitoring in that it detects non-linear correlations and temporal dependencies within the telemetry data. It supports seamless integration with contemporary observability platforms, enabling real-time alerting and visualization of anomaly probability, allowing preemptive action by database administrators. The framework achieved 92.4% prediction accuracy and a 37% reduction in unplanned downtime in production-grade tests, outperforming conventional threshold-based monitoring. Comprehensive testing across production-grade environments validates the efficacy of the framework in lessening unplanned downtime without compromising on low false positive rates. This work provides a thorough methodology for using machine learning technologies for database reliability engineering that closes the gap between research and operational usage.