Abstract
With the expansion of application scale, the dependence of microservices becomes more and more complex, which makes it difficult to accurately locate the root cause of failure. At present, many microservices fault diagnosis methods have the problems of high cost of fine-grained location and difficult to predict the fault propagation path, which leads to more false positives and low diagnosis efficiency. Therefore, this paper proposes a microservice fault diagnosis method based on correlation analysis. Firstly, the historical exception and fault data of microservices is collected and processed, and the exception event is defined. Secondly, the machine learning algorithm is used to mine the correlation between microservices and abnormal events, and the hierarchical correlation graph is constructed. Finally, a microservice fault diagnosis mechanism based on a hierarchical correlation graph is designed to infer the root cause of the fault and provide effective diagnosis information for administrators. Experimental results show that the mechanism can quickly locate the fault location and infer the root cause of microservice failure in a more fine-grained way.
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Acknowledgements
This work is supported by the Natural Science Foundation of China (No. 61762008), the National Key Research and Development Project of China (No. 2018YFB1404404), the Major special project of science and technology of Guangxi (No. AA18118047-7), and the Guangxi Natural Science Foundation Project (No. 2017GXNSFAA198141).
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Tang, Y., Chen, N., Zhang, Y., Yao, X., Yu, S. (2022). Fine-Grained Diagnosis Method for Microservice Faults Based on Hierarchical Correlation Analysis. In: Sun, Y., et al. Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2021. Communications in Computer and Information Science, vol 1491. Springer, Singapore. https://doi.org/10.1007/978-981-19-4546-5_2
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DOI: https://doi.org/10.1007/978-981-19-4546-5_2
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