用超图与微分方程联合建模微服务故障，精准定位根因。
Hypergraph and Latent ODE Learning for Multimodal Root Cause Localization in Microservices
- 通过可微超边构建高阶服务依赖关系，捕捉复杂交互。
- 基于ODE的时序编码器在不规则观测下持续追踪异常演化过程。
- 自适应融合日志、链路、指标等多模态数据，适合运维场景使用。
云原生微服务系统中的根因定位需建模复杂的服务依赖、不规则的时间动态及异构可观测性数据。我们提出HyperODE RCA，一个统一框架，结合超图注意力学习、潜在常微分方程与多模态交叉注意力融合，实现细粒度根因分析。方法通过可微超边构造学习高阶服务交互，利用ODE RNN编码器从不规则观测中捕捉连续异常演化，并通过上下文感知模态路由自适应融合日志、追踪、指标、实体和事件。进一步引入变分信息瓶颈、时间因果正则化与不变风险约束提升鲁棒性。在Tianchi AIOps基准上的实验显示，该方法在排序与分类性能上显著优于强基线，同时通过学习到的超图注意力保持可解释性。
原文摘要 · Abstract (English)
Root cause localization in cloud native microservice systems requires modeling complex service dependencies, irregular temporal dynamics, and heterogeneous observability data. We present HyperODE RCA, a unified framework that combines hypergraph attention learning, latent ordinary differential equations, and multimodal cross attention fusion for fine grained root cause analysis. The method learns higher order service interactions through differentiable hyperedge construction, captures continuous anomaly evolution from irregular observations with an ODE RNN encoder, and adaptively fuses logs, traces, metrics, entities, and events using context aware modality routing. We further improve robustness with a variational information bottleneck, temporal causal regularization, and invariant risk constraints. Experiments on the Tianchi AIOps benchmark show clear gains over strong baselines in ranking and classification performance, while preserving interpretability through learned hypergraph attention.
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