T-Rank:A Lightweight Spectrum based Fault Localization Approach for Microservice Systems


The cloud-native system is shifting from traditional monolithic architecture to microservice architecture because of loosely coupling, better maintainability and availability, faster deployment, and richer ecology brought by it. Except for these advantages, it still has an inevitable weakness-the communication over RPC (Remote Procedure Call) between services makes the system performance more unpredictable. Moreover, the complex interactions amongst services make it hard to reveal the root cause of performance issues. To address this challenge, we propose a lightweight spectrum-based performance diagnosis tool, named T-Rank. T-Rank provides the ranked suspicious score in a list of microservices to localize root causes with very few resources. We demonstrate the high accuracy and the low cost of T-Rank by conducting experiments with the data collected from a real-world production microservice system. Moreover, comparison results show that T-Rank outperforms other state-of-the-art approaches.

In Proceedings of 2021 IEEE/ACM 21st International Symposium on Cluster, Cloud and Internet Computing (CCF C, CORE A )
Guangba Yu
Guangba Yu
Ph.D. Candidate Focus on Cloud Native

My research interests include cloud computing, microservices, Serverless, AIOps