AIOps: Predictive Monitoring, Root-Cause Analysis, and Automated Response Across Hybrid Cloud Infrastructure

Hybrid-cloud platforms generate interdependent metrics, logs, traces, deployment events, and service-level objectives, challenging isolated threshold-based operations. This article specifies and tests an integrated artificial-intelligence-for-IT-operations (AIOps) workflow for incident prediction, root-cause ranking, and risk-aware response. A completely synthetic telemetry benchmark containing 5,400 service-window records was generated with a disclosed seed; chronological separation retained 1,400 records for held-out evaluation. Logistic regression, random forest, and histogram gradient boosting were compared using receiver-operating-characteristic area (ROC-AUC), average precision (PR-AUC), and Brier score. Three diagnostic rankings and four remediation policies were evaluated on the same synthetic incident outcomes. Logistic regression achieved ROC-AUC 0.709 and PR-AUC 0.611, exceeding the nonlinear baselines in this simulation. Raw symptom ranking achieved Top-1 localization of 0.777, exceeding the context-adjusted alternatives; all approaches had Top-3 scores close to 0.991. A risk-calibrated response reduced constructed scenario costs from 298,546 to 261,239 units but increased false interventions. The study therefore emphasizes calibrated decision costs, auditable root-cause hypotheses, rollback gates, and the distinction between simulation consistency and verified service reliability. No real hybrid-cloud incident, remediation success, or recovery-time reduction is claimed.