Multi-modal Causal Structure Learning and Root Cause Analysis

TL;DR

This paper introduces Mulan, a multi-modal causal structure learning framework that enhances root cause analysis by integrating diverse data modalities, leveraging contrastive learning, and employing fault propagation simulation for improved accuracy.

Contribution

Mulan is the first unified multi-modal causal learning method for root cause analysis, combining log representation, contrastive learning, and attention mechanisms.

Findings

Outperforms existing single-modality methods on real-world datasets.

Effectively captures complex relationships across multiple data modalities.

Accurately identifies root causes in complex systems.

Abstract

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.