Context-aware Root Cause Localization in Distributed Traces Using Social Network Analysis (work in Progress Paper)

The complexity of microservices and their distributed nature necessitates constant monitoring and tracing of their execution to identify performance problems and underlying root causes. However, the large volume of collected data and the complexity of distributed communications pose challenges in identifying and locating abnormal services. In this paper, we propose a novel approach that takes into consideration the importance of execution contexts in propagating and localizing performance root causes. We achieve this by integrating social network analysis techniques with spectrum analysis. To evaluate our proposed approach, we conducted an experiment using a real-world benchmark, and we observed promising preliminary results, with a success rate of 91.3% in correctly identifying the primary root cause (top-1), and a perfect 100% success rate in finding the root cause within the top three candidates (top-3).