用因果AI精准定位系统故障根源，提升运维效率。
Causal AI-based Root Cause Identification: Research to Practice at Scale
- 基于因果关系而非相关性分析故障根源
- 已在企业级系统中实时部署并验证效果
- 适合需要高可靠性的分布式系统运维团队
现代应用由大量模块、团队和数据中心构成，尽管工程与恢复策略完善，故障和性能问题仍不可避免，可能引发重大中断。快速准确地识别故障根源对保障系统可靠性与关键服务指标至关重要。本文提出一种基于因果推理的根因识别（RCI）算法，强调因果关系而非相关性，并已集成至IBM Instana，实现从研究到大规模生产的落地。通过运用“因果AI”，Instana区别于传统应用性能管理（APM）工具，可在近实时内定位问题。本文详述了RCI算法的理论基础与实际部署方案，并通过真实案例展示其在复杂系统环境中的可靠性与性能提升能力。
原文摘要 · Abstract (English)
Modern applications are built as large, distributed systems spanning numerous modules, teams, and data centers. Despite robust engineering and recovery strategies, failures and performance issues remain inevitable, risking significant disruptions and affecting end users. Rapid and accurate root cause identification is therefore vital to ensure system reliability and maintain key service metrics. We have developed a novel causality-based Root Cause Identification (RCI) algorithm that emphasizes causation over correlation. This algorithm has been integrated into IBM Instana-bridging research to practice at scale-and is now in production use by enterprise customers. By leveraging "causal AI," Instana stands apart from typical Application Performance Management (APM) tools, pinpointing issues in near real-time. This paper highlights Instana's advanced failure diagnosis capabilities, discussing both the theoretical underpinnings and practical implementations of the RCI algorithm. Real-world examples illustrate how our causality-based approach enhances reliability and performance in today's complex system landscapes.
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