Time-Series Learning for Proactive Fault Prediction in Distributed Systems with Deep Neural Structures
It addresses proactive fault prediction for distributed systems, which is an incremental improvement over existing methods.
This paper tackles fault prediction in distributed systems by proposing a method using GRU and attention mechanisms for temporal feature learning, achieving improved accuracy, F1-score, and AUC compared to mainstream time-series models in experiments on real-world cloud data.
This paper addresses the challenges of fault prediction and delayed response in distributed systems by proposing an intelligent prediction method based on temporal feature learning. The method takes multi-dimensional performance metric sequences as input. We use a Gated Recurrent Unit (GRU) to model the evolution of system states over time. An attention mechanism is then applied to enhance key temporal segments, improving the model's ability to identify potential faults. On this basis, a feedforward neural network is designed to perform the final classification, enabling early warning of system failures. To validate the effectiveness of the proposed approach, comparative experiments and ablation analyses were conducted using data from a large-scale real-world cloud system. The experimental results show that the model outperforms various mainstream time-series models in terms of Accuracy, F1-Score, and AUC. This demonstrates strong prediction capability and stability. Furthermore, the loss function curve confirms the convergence and reliability of the training process. It indicates that the proposed method effectively learns system behavior patterns and achieves efficient fault detection.