In recent years, the widespread adoption of distributed microservice
architectures within the industry has significantly increased the demand for
enhanced system availability and robustness. Due to the complex service
invocation paths and dependencies at enterprise-level microservice systems, it
is challenging to locate the anomalies promptly during service invocations,
thus causing intractable issues for normal system operations and maintenance.
In this paper, we propose a Causal Heterogeneous grAph baSed framEwork for root
cause analysis, namely CHASE, for microservice systems with multimodal data,
including traces, logs, and system monitoring metrics. Specifically, related
information is encoded into representative embeddings and further modeled by a
multimodal invocation graph. Following that, anomaly detection is performed on
each instance node with attentive heterogeneous message passing from its
adjacent metric and log nodes. Finally, CHASE learns from the constructed
hypergraph with hyperedges representing the flow of causality and performs root
cause localization. We evaluate the proposed framework on two public
microservice datasets with distinct attributes and compare with the
state-of-the-art methods. The results show that CHASE achieves the average
performance gain up to 36.2%(A@1) and 29.4%(Percentage@1), respectively to its
best counterpart.

该论文提出了基于因果异构图的微服务系统的根本原因分析框架 CHASE，通过编码相关信息和建模多模态调用图来实现对微服务系统中的异常检测和根本原因定位，实验结果表明该框架在两个公开数据集上取得了平均性能提升 36.2%(A@1) 和 29.4%(Percentage@1)。

CHASE: 一个基于因果异构图的多模态微服务系统根本原因分析框架

CHASE: A Causal Heterogeneous Graph based Framework for Root Cause  Analysis in Multimodal Microservice Systems

Availability issues of industrial microservice systems (e.g., drop of
successfully placed orders and processed transactions) directly affect the
running of the business. These issues are usually caused by various types of
service anomalies which propagate along service dependencies. Accurate and
high-efficient root cause localization is thus a critical challenge for
large-scale industrial microservice systems. Existing approaches use service
dependency graph based analysis techniques to automatically locate root causes.
However, these approaches are limited due to their inaccurate detection of
service anomalies and inefficient traversing of service dependency graph. In
this paper, we propose a high-efficient root cause localization approach for
availability issues of microservice systems, called MicroHECL. Based on a
dynamically constructed service call graph, MicroHECL analyzes possible anomaly
propagation chains, and ranks candidate root causes based on correlation
analysis. We combine machine learning and statistical methods and design
customized models for the detection of different types of service anomalies
(i.e., performance, reliability, traffic). To improve the efficiency, we adopt
a pruning strategy to eliminate irrelevant service calls in anomaly propagation
chain analysis. Experimental studies show that MicroHECL significantly
outperforms two state-of-the-art baseline approaches in terms of both accuracy
and efficiency. MicroHECL has been used in Alibaba and achieves a top-3 hit
ratio of 68% with root cause localization time reduced from 30 minutes to 5
minutes.

这篇论文介绍了一种基于微服务系统的高效根本原因定位方法，称为 MicroHECL，该方法通过动态构建服务调用图，分析可能的异常传播链，并基于相关性分析对候选根本原因进行排序，实验结果表明，MicroHECL 在精度和效率方面显著优于其他现有方法。