SparseRCA: Unsupervised Root Cause Analysis in Sparse Microservice Testing Traces

Microservice architecture has become a predominant paradigm in the software industry. This architecture necessitates robust end-to-end testing to ensure seamless integration of all components before deployment. Rapidly pinpointing issues when test cases fail is crucial for enhancing software development efficiency. However, in testing environments, the available trace is often sparse, and the system is continuously upgrading, which renders existing microservice-based root cause analysis (RCA) ineffective. To address these challenges, we propose SparseRCA. By assessing the abnormality of the exclusive latency, SparseRCA directly determines the probability of the root cause, solving the challenge of not being able to fully obtain the fault propagation information, such as call relationships in sparse trace scenarios. At the same time, by reconstructing the exclusive latency using the decoupled atomic span units, it solves the problem of latency prediction for new traces caused by frequent upgrades. We evaluate SparseRCA on real-world datasets from a large e-commerce system’s testing environment, where it demonstrates significant improvements over existing models. Our findings underscore the effectiveness of SparseRCA in addressing the challenges of RCA in microservice testing environments.

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Publication Details

Published
2024-10-28
DOI
https://doi.org/10.1109/issre62328.2024.00045
Citations
4
Primary Topic
Software System Performance and Reliability
Type
article
Field-Weighted Citation Impact
1.40

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article

SparseRCA: Unsupervised Root Cause Analysis in Sparse Microservice Testing Traces

Zhenhe Yao, Gaogang Xie, Guangpei Wang, Dan Pei et al.
4 citations
Software System Performance and Reliability
1.40
article

SparseRCA: Unsupervised Root Cause Analysis in Sparse Microservice Testing Traces

Zhenhe Yao, Gaogang Xie, Guangpei Wang, Dan Pei, Changhua Pei, Zeyan Li, Zhiwei Liu, Hongwei Chen, Jianhui Li, Guang Cheng, Haowei Ye, Hang Cui
article en
4 citations

Abstract

Microservice architecture has become a predominant paradigm in the software industry. This architecture necessitates robust end-to-end testing to ensure seamless integration of all components before deployment. Rapidly pinpointing issues when test cases fail is crucial for enhancing software development efficiency. However, in testing environments, the available trace is often sparse, and the system is continuously upgrading, which renders existing microservice-based root cause analysis (RCA) ineffective. To address these challenges, we propose SparseRCA. By assessing the abnormality of the exclusive latency, SparseRCA directly determines the probability of the root cause, solving the challenge of not being able to fully obtain the fault propagation information, such as call relationships in sparse trace scenarios. At the same time, by reconstructing the exclusive latency using the decoupled atomic span units, it solves the problem of latency prediction for new traces caused by frequent upgrades. We evaluate SparseRCA on real-world datasets from a large e-commerce system’s testing environment, where it demonstrates significant improvements over existing models. Our findings underscore the effectiveness of SparseRCA in addressing the challenges of RCA in microservice testing environments.

Chinese Academy of Sciences (CN), Computer Network Information Center (CN), Tsinghua University (CN)
National Natural Science Foundation of China
Zero hunger
Openalex Percentile: Top 16%
Software System Performance and Reliability
1.40
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