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Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice Systems.

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Title: Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice Systems.
Authors: Zhang, Chenxi1 cxzhang20@fudan.edu.cn, Dong, Zhen1 zhendong@fudan.edu.cn, Peng, Xin1 pengxin@fudan.edu.cn, Zhang, Bicheng1 bczhang22@m.fudan.edu.cn, Chen, Miao1 22210240006@m.fudan.edu.cn
Source: ICSE: International Conference on Software Engineering. 2024, p1-12. 12p.
Subjects: Software localization, Computer software, Root cause analysis, Semantics, Robust statistics
Abstract: Modern microservice systems have become increasingly complicated due to the dynamic and complex interactions and runtime environment. It leads to the system vulnerable to performance issues caused by a variety of reasons, such as the runtime environments, communications, coordinations, or implementations of services. Traces record the detailed execution process of a request through the system and have been widely used in performance issues diagnosis in microservice systems. By identifying the execution processes and attribute value combinations that are common in anomalous traces but rare in normal traces, engineers may localize the root cause of a performance issue into a smaller scope. However, due to the complex structure of traces and the large number of attribute combinations, it is challenging to find the root cause from the huge search space. In this paper, we propose TraceContrast, a trace-based multi-dimensional root cause localization approach. TraceContrast uses a sequence representation to describe the complex structure of a trace with attributes of each span. Based on the representation, it combines contrast sequential pattern mining and spectrum analysis to localize multi-dimensional root causes efficiently. Experimental studies on a widely used microservice benchmark show that TraceContrast outperforms existing approaches in both multi-dimensional and instance-dimensional root cause localization with significant accuracy advantages. Moreover, Trace-Contrast is efficient and its efficiency can be further improved by parallel execution. [ABSTRACT FROM AUTHOR]
Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Trace-based Multi-Dimensional Root Cause Localization of Performance Issues in Microservice Systems.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Chenxi%22">Zhang, Chenxi</searchLink><relatesTo>1</relatesTo><i> cxzhang20@fudan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Dong%2C+Zhen%22">Dong, Zhen</searchLink><relatesTo>1</relatesTo><i> zhendong@fudan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Peng%2C+Xin%22">Peng, Xin</searchLink><relatesTo>1</relatesTo><i> pengxin@fudan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Bicheng%22">Zhang, Bicheng</searchLink><relatesTo>1</relatesTo><i> bczhang22@m.fudan.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Miao%22">Chen, Miao</searchLink><relatesTo>1</relatesTo><i> 22210240006@m.fudan.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22ICSE%3A+International+Conference+on+Software+Engineering%22">ICSE: International Conference on Software Engineering</searchLink>. 2024, p1-12. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Software+localization%22">Software localization</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink><br /><searchLink fieldCode="DE" term="%22Root+cause+analysis%22">Root cause analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Semantics%22">Semantics</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+statistics%22">Robust statistics</searchLink>
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  Data: Modern microservice systems have become increasingly complicated due to the dynamic and complex interactions and runtime environment. It leads to the system vulnerable to performance issues caused by a variety of reasons, such as the runtime environments, communications, coordinations, or implementations of services. Traces record the detailed execution process of a request through the system and have been widely used in performance issues diagnosis in microservice systems. By identifying the execution processes and attribute value combinations that are common in anomalous traces but rare in normal traces, engineers may localize the root cause of a performance issue into a smaller scope. However, due to the complex structure of traces and the large number of attribute combinations, it is challenging to find the root cause from the huge search space. In this paper, we propose TraceContrast, a trace-based multi-dimensional root cause localization approach. TraceContrast uses a sequence representation to describe the complex structure of a trace with attributes of each span. Based on the representation, it combines contrast sequential pattern mining and spectrum analysis to localize multi-dimensional root causes efficiently. Experimental studies on a widely used microservice benchmark show that TraceContrast outperforms existing approaches in both multi-dimensional and instance-dimensional root cause localization with significant accuracy advantages. Moreover, Trace-Contrast is efficient and its efficiency can be further improved by parallel execution. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of ICSE: International Conference on Software Engineering is the property of Association for Computing Machinery and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1145/3597503.3639088
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        Text: English
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      – SubjectFull: Root cause analysis
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      – SubjectFull: Semantics
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      – SubjectFull: Robust statistics
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            – D: 01
              M: 05
              Text: 2024
              Type: published
              Y: 2024
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