Root cause analysis (RCA) is a critical component in maintaining the reliability and performance of modern cloud applications. However, due to the inherent complexity of cloud environments, traditional RCA techniques become insufficient in supporting system administrators in daily incident response routines. This article presents an RCA solution specifically designed for cloud applications, capable of pinpointing failure root causes and recreating complete fault trajectories from the root cause to the effect. The novelty of our approach lies in approximating causal symptom dependencies by synergizing several symptom correlation methods that assess symptoms in terms of structural, semantic, and temporal aspects. The solution integrates statistical methods with system structure and behavior mining, offering a more comprehensive analysis than existing techniques. Based on these concepts, in this work, we provide definitions and construction algorithms for RCA model structures used in the inference, propose a symptom correlation framework encompassing essential elements of symptom data analysis, and provide a detailed description of the elaborated root cause identification process. Functional evaluation on a live microservice-based system demonstrates the effectiveness of our approach in identifying root causes of complex failures across multiple cloud layers.

中文翻译：

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云原生应用程序的根本原因分析

根本原因分析 (RCA) 是维持现代云应用程序可靠性和性能的关键组成部分。然而，由于云环境固有的复杂性，传统的 RCA 技术不足以支持系统管理员进行日常事件响应例程。本文介绍了一种专为云应用程序设计的 RCA 解决方案，能够查明故障根本原因并重新创建从根本原因到结果的完整故障轨迹。我们方法的新颖性在于通过协同几种症状相关方法来近似因果症状依赖性，这些方法从结构、语义和时间方面评估症状。该解决方案将统计方法与系统结构和行为挖掘相结合，提供比现有技术更全面的分析。基于这些概念，在这项工作中，我们提供了推理中使用的 RCA 模型结构的定义和构建算法，提出了包含症状数据分析基本要素的症状相关框架，并详细描述了详细的根本原因识别过程。对基于微服务的实时系统的功能评估证明了我们的方法在识别跨多个云层的复杂故障的根本原因方面的有效性。