Online Multi-modal Root Cause Analysis

• URL: http://arxiv.org/abs/2410.10021v1
• Date: Sun, 13 Oct 2024 21:47:36 GMT
• Title: Online Multi-modal Root Cause Analysis
• Authors: Lecheng Zheng, Zhengzhang Chen, Haifeng Chen, Jingrui He,
• Abstract summary: Root Cause Analysis (RCA) is essential for pinpointing the root causes of failures in microservice systems. Existing online RCA methods handle only single-modal data overlooking, complex interactions in multi-modal systems. We introduce OCEAN, a novel online multi-modal causal structure learning method for root cause localization.
• Score: 61.94987309148539
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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