Deep Learning–Driven Predictive Analytics for Proactive IT System Failure Detection and Operational Optimization
DOI:
https://doi.org/10.51903/ec7yda26Keywords:
Deep Learning, IT System Failure, Operational Optimization, Machine LearningAbstract
The increasing complexity of modern IT infrastructures has introduced significant challenges in maintaining system reliability and operational stability. Traditional monitoring mechanisms often rely on rule-based alerts and threshold-based detection, which are limited in their ability to identify complex patterns associated with emerging system failures. This study proposes a deep-learning–driven predictive analytics framework for proactive detection of IT system failures and operational optimization. The proposed framework integrates operational monitoring data acquisition, data preprocessing, deep learning–based prediction, and operational intelligence within a unified architecture designed for intelligent infrastructure monitoring. A deep neural network model is trained on operational monitoring data, comprising system performance metrics and event logs, to identify early indicators of potential system instability. Experimental evaluation demonstrates that the proposed model achieves strong predictive performance, outperforming several baseline machine learning approaches, including Logistic Regression, Support Vector Machine, and Random Forest. The results indicate that deep learning techniques can effectively capture complex relationships within operational monitoring data and support early failure prediction. The proposed framework provides a practical approach for integrating predictive analytics into IT monitoring systems, enabling more proactive infrastructure management and improved operational resilience.References
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Copyright (c) 2026 Azed Yayah Durrotun NIhayah, Khaling Mothelsang, Go Eun Myeong (Author)
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