The paper proposes an integrated methodological approach to modeling and forecasting Oracle DBMS performance degradation under high-load industrial environments. Against the backdrop of digital transformation and the consolidation of the AIOps paradigm, there is a shift from reactive monitoring toward proactive intelligent support mechanisms focused on early detection of adverse indicators and incident prevention. A central role is assigned to the integration of multidimensional telemetry: wait events, performance metrics of the input/output (I/O) subsystem, temporal profiles of memory consumption in the SGA and PGA areas, as well as behavioral characteristics of key background processes, including LGWR, DBWR, and ARCH, are taken into account. The proposed methodology for the first time combines classical Oracle diagnostics (AWR, ASH, V$ views) with machine learning and AIOps approaches to predict future degradations before they manifest at the user level.

The architecture of a predictive solution is described, based on a combination of machine learning methods (Random Forest, XGBoost) and deep learning models (LSTM, BERT). This composition ensures robust extraction of latent patterns in operational data streams and enables detection of anomalous states 7–12 minutes before the formation of a critical failure. Substantial attention is devoted to the reconstruction of causal relationships between infrastructural parameters and application-level indicators, which makes it possible to interpret degradation not as a set of disparate symptoms, but as a result of the interaction of resources, internal DBMS mechanisms, and the load profile. Empirical verification on experimental data demonstrates a significant improvement in operational diagnostics, including a MTTD 63%. The material is intended for practicing database operations specialists, designers of high-load solutions, and researchers developing intelligent methods for IT operations.

Oracle Database, predictive analytics, machine learning, wait events, SGA, PGA, input/output performance, AIOps, background processes, production environments, performance degradation, anomaly detection, root cause analysis

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