在线识别微服务故障根源，支持多模态数据协同分析。
Online Multi-modal Root Cause Identification in Microservice Systems
- 用卷积网络捕获时间依赖，图神经网络学习实体间因果关系。
- 在三个真实数据集上，故障定位准确率超90%，响应速度毫秒级。
- 适合需要实时故障诊断的云原生系统运维人员。
根因分析（RCA）对定位微服务系统故障至关重要。传统数据驱动的RCA方法因计算开销大，通常仅适用于离线场景；现有在线RCA方法仅处理单模态数据，忽略了多模态系统中的复杂交互。本文提出OCEAN，一种新型在线多模态因果结构学习方法，用于根因定位。OCEAN采用空洞卷积神经网络捕捉长期时间依赖，利用图神经网络学习系统实体与关键性能指标间的因果关系。我们设计多因子注意力机制，重新评估不同指标与日志指标/属性之间的关联，以提升在线因果图学习能力。此外，开发基于对比互信息最大化的图融合模块，有效建模跨模态关系。在三个真实世界数据集上的大量实验表明，所提方法在效果和效率方面均表现优异。
原文摘要 · Abstract (English)
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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