The increasing complexity and dynamism of modern networks pose significant challenges for effective fault management. Temporal network faults, characterized by their evolving nature and cascading effects, are particularly difficult to diagnose. Traditional root cause analysis (RCA) methods often struggle with the high dimensionality, non-linearity, and temporal dependencies inherent in network monitoring data. This paper proposes a novel framework, ESN4TRCA—an echo state network for temporal root cause analysis, for identifying the root causes of temporal network faults. ESN4TRCA leverages the inherent capabilities of echo state networks (ESNs), a paradigm of reservoir computing, in modeling complex temporal dynamics with remarkably low training overhead. We formulate the temporal RCA problem as a sequence classification task, where sequences of multivariate key performance indicators (KPIs) and alarm data are mapped to their underlying root causes. The proposed framework encompasses modules for data preprocessing, ESN model construction specifically tailored for heterogeneous network fault data, and a robust inference mechanism. We introduce specific mathematical formulations for the leaky-integrator reservoir dynamics and the output weight training via ridge regression, optimized for the RCA context. Comprehensive experiments are conducted on both a synthetic dataset generated using the NS-3 network simulator and a real-world public dataset. The results demonstrate that ESN4TRCA significantly outperforms state-ofthe-art RCA methods, including traditional machine learning approaches and other recurrent neural network architectures like LSTMs and GRUs, in terms of accuracy, F1-score, and robustness to noise, while maintaining superior computational efficiency. The study highlights the potential of ESNs as a powerful and practical tool for advanced automated network fault management.
Zhang et al. (Thu,) studied this question.