Real-Time Failure Prediction in Large-Scale Enterprise Applications Using Deep Learning Techniques
Abstract
The ability to predict system failures in real-time is critical for maintaining the reliability and availability of large-scale enterprise applications. Traditional monitoring approaches often rely on predefined thresholds and are limited in their capacity to detect complex patterns of failure. This paper presents a deep learning-based framework for real-time failure prediction in enterprise applications. By utilizing techniques such as Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs), the system analyzes a wide range of time-series data, including system logs, resource utilization, and transaction performance, to identify early indicators of failure. The proposed model continuously learns from historical data and adapts to changes in system behavior, providing proactive alerts and recommendations for preventive measures. Experimental results demonstrate the framework’s effectiveness in identifying potential failures with high accuracy, reducing false positives, and minimizing downtime. This AI-driven approach offers a scalable and adaptive solution for real-time failure prediction, enabling organizations to improve system resilience, enhance operational efficiency, and reduce the cost of unplanned outages.
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