Robust Anomaly Diagnosis in Heterogeneous Microservices Systems under Variable Invocations
Jingjing Yang, Yuchun Guo, Yishuai Chen, Yongxiang Zhao, Zhongda Lu, Yuqiang Liang · GLOBECOM 2022 - 2022 IEEE Global Communications Conference · 2022
Microservice architecture has been widely adopted for large-scale applications because of its benefits of scalability, flexibility, and reliability. However, due to the heterogeneity of system architecture and variable invocations between services, it is difficult to accurately diagnose the root causes of performance degradation in time. This paper proposes WinG, a system to pinpoint root causes. Firstly, since the characteristic of a system element is difficult to capture due to variable invocations between elements, WinG characterizes an element's status by a feature vector that includes all its invocation relationships. Secondly, WinG adopts a warping procedure to assess an element's anomaly severity based on its status deviation, to mitigate the interference of variable invocations. Thirdly, WinG groups heterogeneous elements with similar invocation characteristics to avoid the interference of diverse elements types. Finally, false alarms are filtered by the anomaly duration and frequency. Experimental evaluation results on the public dataset show that, with the above four methods, WinG can locate root causes with 87% precision, outperforming baseline methods. On average of 78 test cases, it achieves 34% precision improvement over the champion method of the competition.