CausalRCA：基于因果推断的微服务应用精确细粒度根因定位
CausalRCA: Causal Inference based Precise Fine-grained Root Cause Localization for Microservice Applications
AI总结：
针对现有因果推断根因定位方法的局限性，提出CausalRCA框架，通过基于梯度的因果结构学习和根因推断实现微服务细粒度根因定位，准确率显著优于基线方法。
摘要：
有效定位性能异常的根因对于实现云端微服务应用的快速恢复和损失降低至关重要。根据可定位的根因粒度，服务运维人员可采取不同措施：例如，若仅能定位到故障服务（即粗粒度），则重启或迁移服务；若能定位到故障服务上的具体指标（即细粒度），则调整资源规模。此前研究主要聚焦于粗粒度的故障服务定位，目前细粒度根因定位（识别故障服务及对应指标）的研究热度日益增长。基于因果推断（CI）的方法近年来在根因定位领域受到广泛应用，但现有CI方法存在局限性，例如线性因果关系假设和严格的数据分布要求。为解决这些挑战，我们提出名为CausalRCA的框架，以实现细粒度、自动化、实时的根因定位。CausalRCA采用基于梯度的因果结构学习方法生成加权因果图，并通过根因推断方法定位根因指标。我们开展了粗粒度和细粒度根因定位实验，以评估CausalRCA的定位性能。实验结果表明，CausalRCA在定位准确率上显著优于基线方法：例如，故障服务内细粒度根因指标定位的平均AC@3为0.719，较基线方法平均提升10%；此外，平均Avg@5提升了9.43%。
英文摘要：
Effectively localizing root causes of performance anomalies is crucial to enabling the rapid recovery and loss mitigation of microservice applications in the cloud. Depending on the granularity of the causes that can be localized, a service operator may take different actions, e.g., restarting or migrating services if only faulty services can be localized (namely, coarse-grained) or scaling resources if specific indicative metrics on the faulty service can be localized (namely, fine-grained). Prior research mainly focuses on coarse-grained faulty service localization, and there is now a growing interest in fine-grained root cause localization to identify faulty services and metrics. Causal inference (CI) based methods have gained popularity recently for root cause localization, but currently used CI methods have limitations, such as the linear causal relations assumption and strict data distribution requirements. To tackle these challenges, we propose a framework named CausalRCA to implement fine-grained, automated, and real-time root cause localization. The CausalRCA uses a gradient-based causal structure learning method to generate weighted causal graphs and a root cause inference method to localize root cause metrics. We conduct coarse- and fine-grained root cause localization to evaluate the localization performance of CausalRCA. Experimental results show that CausalRCA has significantly outperformed baseline methods in localization accuracy, e.g., the average AC@3 of the fine-grained root cause metric localization in the faulty service is 0.719, and the average increase is 10% compared with baseline methods. In addition, the average Avg@5 has improved by 9.43%.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
