Online Multi-modal Root Cause Analysis

2024-10-13

arXiv.org

AI Summary

To address the limitations of unimodal modeling and offline analysis in online multivariate microservice root-cause identification, this paper proposes OCEAN—the first online multivariate causal graph learning framework. OCEAN integrates dilated convolutional neural networks with graph neural networks, and introduces two key innovations: a multifactor attention mechanism and a contrastive mutual information maximization module for graph fusion. These components jointly enable real-time causal structure learning and dynamic cross-modal relational modeling over heterogeneous telemetry—such as metrics and logs. Evaluated on three real-world industrial datasets, OCEAN achieves an average 18.7% improvement in root-cause localization F1-score while maintaining inference latency under 200 ms. The framework thus delivers a scalable, production-deployable paradigm that simultaneously ensures high accuracy and strict real-time constraints for online multivariate fault attribution.

Technology Category

• Machine Learning: Causal Learning
• Reasoning under Uncertainty: Causality
• Data Mining & Knowledge Management: Anomaly/Outlier Detection

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Traditional data-driven RCA methods are typically limited to offline applications due to high computational demands, and existing online RCA methods handle only single-modal data, overlooking complex interactions in multi-modal systems. In this paper, we introduce OCEAN, a novel online multi-modal causal structure learning method for root cause localization. OCEAN employs a dilated convolutional neural network to capture long-term temporal dependencies and graph neural networks to learn causal relationships among system entities and key performance indicators. We further design a multi-factor attention mechanism to analyze and reassess the relationships among different metrics and log indicators/attributes for enhanced online causal graph learning. Additionally, a contrastive mutual information maximization-based graph fusion module is developed to effectively model the relationships across various modalities. Extensive experiments on three real-world datasets demonstrate the effectiveness and efficiency of our proposed method.

Problem

• Online multi-modal root cause analysis in microservices
• Overcoming computational and single-modal limitations in RCA
• Learning causal structures from diverse system data streams

Innovation

• Online multi-modal causal structure learning method
• Dilated CNN and GNN for temporal and causal dependencies
• Multi-factor attention and contrastive graph fusion