A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909277336109056 |
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| author | Wang, Tingting Qi, Guilin |
| author_facet | Wang, Tingting Qi, Guilin |
| contents | The complex dependencies and propagative faults inherent in microservices, characterized by a dense network of interconnected services, pose significant challenges in identifying the underlying causes of issues. Prompt identification and resolution of disruptive problems are crucial to ensure rapid recovery and maintain system stability. Numerous methodologies have emerged to address this challenge, primarily focusing on diagnosing failures through symptomatic data. This survey aims to provide a comprehensive, structured review of root cause analysis (RCA) techniques within microservices, exploring methodologies that include metrics, traces, logs, and multi-model data. It delves deeper into the methodologies, challenges, and future trends within microservices architectures. Positioned at the forefront of AI and automation advancements, it offers guidance for future research directions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00803 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends Wang, Tingting Qi, Guilin Software Engineering Artificial Intelligence Computational Engineering, Finance, and Science The complex dependencies and propagative faults inherent in microservices, characterized by a dense network of interconnected services, pose significant challenges in identifying the underlying causes of issues. Prompt identification and resolution of disruptive problems are crucial to ensure rapid recovery and maintain system stability. Numerous methodologies have emerged to address this challenge, primarily focusing on diagnosing failures through symptomatic data. This survey aims to provide a comprehensive, structured review of root cause analysis (RCA) techniques within microservices, exploring methodologies that include metrics, traces, logs, and multi-model data. It delves deeper into the methodologies, challenges, and future trends within microservices architectures. Positioned at the forefront of AI and automation advancements, it offers guidance for future research directions. |
| title | A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends |
| topic | Software Engineering Artificial Intelligence Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2408.00803 |