Automated Fault Detection and Diagnosis for Large-Scale Cloud Systems Based on Log Analysis

The fast expansion of large-scale cloud systems has presented hitherto unheard-of difficulties preserving system stability and dependability. Generated constantly throughout operation, system logs are an essential data source for failure identification and diagnostics. Nevertheless, the unstructured, high-dimensional, sequential character of log data and the natural complexity of cloud settings restrict the efficiency of conventional approaches. This work presents a new deep learning-based system for automated fault detection and diagnosis in order to solve these problems. Three main components comprise the framework: a Long Short-Term Memory (LSTM)-based sequence modeling architecture to capture temporal dependencies in $\log$ data, an efficient $\log$ parsing and preprocessing pipeline to transform raw logs into structured sequences, and a hierarchical fault classification mechanism for multi-level fault detection. With an accuracy of 97.3%, a precision of 92.5%, and an F1 score of 91.1%, experimental data shows how well the framework handles large-scale log data. Furthermore, appropriate for real-time fault detection, the model achieves great computational efficiency with an average inference time of $\mathbf{2. 3}$ milliseconds per log event. Emphasizing its practical possibilities in enhancing system dependability, the suggested framework beats conventional rule-based techniques and machine learning models.