A Trace-Log-Clusterings-Based Fault Localization Approach to Microservice Systems
Chang‐ai Sun, Tao Zeng, Wanqing Zuo, Huai Liu · 2023
Microservice architecture has been widely used for the development of large-scale distributed applications. Microservice systems normally have high complexity and loose coupling nature, which make it challenging to localize faults in them. Automated fault localization is particularly difficult for microservice systems, due to their unique features, such as frequent updates, complex dependencies, and multiple microservice instances. In this paper, we propose a fault localization approach for microservice systems based on trace log clusterings, called TLCluster. TLCluster first derives trace logs by collecting and combining communication messages and logs of microservice systems, then clusters trace logs for different business process categories, calculates similarities between normal and abnormal trace logs, and finally evaluates and ranks the suspiciousness scores of microservice instances. We conducted a series of experiments to evaluate the effectiveness of TLCluster using a large-scale microservice system. Experimental results show that our approach is able to effectively localize faults of microservice systems and demonstrates a better fault localization accuracy and precision compared with state-of-the-art baseline techniques.