CloudPulse: Predictive Analytics for Cloud Infrastructure Failure Prevention Using Machine Learning

Abstract

The increasing complexity of cloud computing infrastructures, characterized by dynamic workload allocation, heterogeneous computing resources, distributed service dependencies, and fluctuating operational demands, has intensified the challenges associated with infrastructure failure prediction and prevention. Conventional monitoring systems predominantly rely on static thresholds, reactive fault detection, and isolated performance indicators, which frequently limit their ability to identify emerging failures and initiate preventive interventions. This study proposes CloudPulse, a novel uncertainty-aware, attentionenhanced temporal ensemble learning algorithm, designed to predict infrastructure failures, estimate failure probabilities, identify critical failure indicators, and support proactive resource management in cloud environments. The proposed CloudPulse architecture integrates multivariate telemetry preprocessing, temporal feature extraction, bidirectional long shortterm memory (BiLSTM), temporal convolutional networks (TCN), attention-based feature fusion, gradient-boosted decision trees, and adaptive risk calibration within a unified predictive framework. A multi-objective optimization function is formulated to minimize failure prediction error, false alarm rates, detection latency, and computational overhead while maximizing failure recall and preventive intervention effectiveness. CloudPulse is designed for comparative evaluation against Random Forest (RF), Support Vector Machine (SVM), Extreme Gradient Boosting (XGBoost), standalone BiLSTM, and TCN models using publicly available cloud infrastructure telemetry datasets and reproducible failure-injection experiments. The experimental framework incorporates CPU utilization, memory consumption, disk input/output, network throughput, system logs, resource saturation, and service response latency as predictive variables. Performance evaluation will employ accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve (ROC-AUC), precision–recall AUC, prediction lead time, false alarm rate, and inference latency. Comparative performance graphs, confusion matrices, ROC curves, precision–recall curves, temporal failure probability plots, and statistical significance tests will be used to evaluate predictive reliability and operational effectiveness. An ablation study will quantify the contributions of temporal feature extraction, attention-based fusion, uncertainty estimation, and adaptive risk calibration. The central research hypothesis is that the proposed hybrid architecture can improve early failure identification and reduce unnecessary preventive interventions compared with conventional machine learning and standalone deep learning approaches. The anticipated contribution is a reproducible, explainable, and computationally efficient predictive maintenance framework capable of supporting resilient cloud infrastructure operations. Claims of superior predictive performance will be established through empirical benchmarking rather than assumed from the proposed architecture.