Technology Category
Reasoning under Uncertainty: CausalityMachine Learning: Causal LearningKnowledge Representation and Reasoning: Action, Change, and Causality
Causal AI-based Root Cause Identification: Research to Practice at Scale
🤖 AI Summary
Modern distributed systems suffer from frequent failures, and conventional APM tools—relying heavily on correlation analysis—exhibit high false-positive rates and poor interpretability, hindering near-real-time root-cause localization. To address this, we propose a novel root-cause identification paradigm grounded in causal AI. Our approach introduces the first end-to-end inference framework that jointly integrates structural causal models (SCMs), dynamic Bayesian networks, and streaming causal discovery to enable real-time causal reasoning over heterogeneous, multi-source monitoring data. The method has been integrated into IBM Instana and deployed at scale in enterprise production environments. Empirical evaluation demonstrates that it reduces mean root-cause localization time to the sub-second level and improves accuracy by 42%, significantly enhancing system reliability and SLO compliance assurance.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Identify root causes in distributed systems
Enhance system reliability using causal AI
Implement causal RCI algorithm at scale
Innovation
Methods, ideas, or system contributions that make the work stand out.
Causal AI-based RCI algorithm
Real-time failure diagnosis
Integration in IBM Instana
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