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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.18240 |
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| _version_ | 1866916629805268992 |
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| author | Jha, Saurabh Rahane, Ameet Shwartz, Laura Palaci-Olgun, Marc Bagehorn, Frank Rios, Jesus Stingaciu, Dan Kattinakere, Ragu Banerjee, Debasish |
| author_facet | Jha, Saurabh Rahane, Ameet Shwartz, Laura Palaci-Olgun, Marc Bagehorn, Frank Rios, Jesus Stingaciu, Dan Kattinakere, Ragu Banerjee, Debasish |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_18240 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Causal AI-based Root Cause Identification: Research to Practice at Scale Jha, Saurabh Rahane, Ameet Shwartz, Laura Palaci-Olgun, Marc Bagehorn, Frank Rios, Jesus Stingaciu, Dan Kattinakere, Ragu Banerjee, Debasish Machine Learning Distributed, Parallel, and Cluster Computing Software Engineering 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. |
| title | Causal AI-based Root Cause Identification: Research to Practice at Scale |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing Software Engineering |
| url | https://arxiv.org/abs/2502.18240 |