AutoRecover: Predictive IT Incident Resolution Through AI-Driven Automation in Cloud Infrastructure

Abstract

Cloud infrastructure environments experience recurring service disruptions arising from resource exhaustion, workload variability, configuration errors, network instability, and cascading component failures. Conventional incident management approaches, including static threshold monitoring, rule-based remediation, and reactive anomaly detection, frequently exhibit limited predictive capability, delayed recovery, and inadequate adaptation to evolving operational conditions. This study proposes AutoRecover, a novel predictive incident resolution algorithm integrating multivariate time-series forecasting, explainable machine learning, probabilistic failure prediction, and reinforcement learning-based automated remediation. The proposed architecture combines Long Short-Term Memory (LSTM) networks for temporal infrastructure monitoring, Extreme Gradient Boosting (XGBoost) for incident risk classification, and Proximal Policy Optimization (PPO) for adaptive recoveryaction selection. A confidence-calibrated decision mechanism integrates predictive failure probabilities, infrastructure dependency relationships, operational risk, and recovery costs to determine appropriate remediation strategies. AutoRecover is designed to predict impending infrastructure failures, identify probable incident causes, and autonomously execute recovery operations, including service restarts, workload migration, resource scaling, and container rescheduling. Its effectiveness will be investigated using historical cloud monitoring datasets, controlled fault injection, and reproducible simulation experiments. Comparative benchmarking against Random Forest, Support Vector Machine, standalone LSTM, XGBoost, and conventional rule-based automation will evaluate incident prediction accuracy, precision, recall, F1-score, false alarm rate, mean time to detection, mean time to recovery, service availability, and computational overhead. Statistical significance testing, ablation analysis, and explainable artificial intelligence techniques will establish the contribution of individual architectural components. The proposed framework seeks to improve predictive reliability, minimize incident resolution delays, and enhance cloud service resilience through proactive, risk-aware, and continuously adaptive incident management.