Multi-modal Causal Structure Learning and Root Cause Analysis
Lecheng Zheng Zhengzhang Chen Jingrui He Haifeng Chen
Effective root cause analysis (RCA) is vital for swiftly restoring services,
minimizing losses, and ensuring the smooth operation and management of complex
systems. Previous data-driven RCA methods, particularly those employing causal
discovery techniques, have primarily focused on constructing dependency or
causal graphs for backtracking the root causes. However, these methods often
fall short as they rely solely on data from a single modality, thereby
resulting in suboptimal solutions. In this work, we propose Mulan, a unified
multi-modal causal structure learning method for root cause localization. We
leverage a log-tailored language model to facilitate log representation
learning, converting log sequences into time-series data. To explore intricate
relationships across different modalities, we propose a contrastive
learning-based approach to extract modality-invariant and modality-specific
representations within a shared latent space. Additionally, we introduce a
novel key performance indicator-aware attention mechanism for assessing
modality reliability and co-learning a final causal graph. Finally, we employ
random walk with restart to simulate system fault propagation and identify
potential root causes. Extensive experiments on three real-world datasets
validate the effectiveness of our proposed framework.