In recent years, microservices have gained widespread adoption in IT
operations due to their scalability, maintenance, and flexibility. However, it
becomes challenging for site reliability engineers (SREs) to pinpoint the root
cause due to the complex relationships in microservices when facing system
malfunctions. Previous research employed structured learning methods (e.g.,
PC-algorithm) to establish causal relationships and derive root causes from
causal graphs. Nevertheless, they ignored the temporal order of time series
data and failed to leverage the rich information inherent in the temporal
relationships. For instance, in cases where there is a sudden spike in CPU
utilization, it can lead to an increase in latency for other microservices.
However, in this scenario, the anomaly in CPU utilization occurs before the
latency increase, rather than simultaneously. As a result, the PC-algorithm
fails to capture such characteristics. To address these challenges, we propose
RUN, a novel approach for root cause analysis using neural Granger causal
discovery with contrastive learning. RUN enhances the backbone encoder by
integrating contextual information from time series, and leverages a time
series forecasting model to conduct neural Granger causal discovery. In
addition, RUN incorporates Pagerank with a personalization vector to
efficiently recommend the top-k root causes. Extensive experiments conducted on
the synthetic and real-world microservice-based datasets demonstrate that RUN
noticeably outperforms the state-of-the-art root cause analysis methods.
Moreover, we provide an analysis scenario for the sock-shop case to showcase
the practicality and efficacy of RUN in microservice-based applications. Our
code is publicly available at this https URL

借助神经 Granger 因果推理与对比学习，提出了一种名为 RUN 的用于根本原因分析的新方法，通过整合时间序列的上下文信息，利用时间序列预测模型进行神经 Granger 因果推理，同时结合个性化向量的 Pagerank，有效地推荐前 k 个根本原因。通过对合成和真实微服务基础数据集的广泛实验，证明 RUN 明显优于现有的根本原因分析方法。此外，我们提供了一个 sock-shop 案例的分析场景，展示了 RUN 在微服务应用中的实用性和效能。