TADL: Fault Localization with Transformer-based Anomaly Detection for Dynamic Microservice Systems
Yuewei Li, Yan Lu, Jingyu Wang, Qi Qi, Jing Wang, Yingying Wang, Jianxin Liao
Abstract
Due to the complexity of microservice architecture, it is difficult to accomplish efficient microservice anomaly detection and localization tasks and achieve the target of high system reliability. For rapid failure recovery and user satisfaction, it is significant to detect and locate anomalies fast and accurately in microservice systems. In this paper, we propose an anomaly detection and localization model based on Transformer, named TADL (Transformer-based Anomaly Detector and Locator), which models the temporal features and dynamically captures container relationships using Transformer with sandwich structure. TADL uses readily available container performance metrics, making it easy to implement in already-running container clusters. Evaluations are conducted on a sock-shop dataset collected from a real microservice system and a publicly available dataset SMD. Empirical studies on the above two datasets demonstrate that TADL can outperform baseline methods in the performance of anomaly detection, the latency of anomaly detection, and the effect of anomalous container localization, which indicates that TADL is useful in maintaining complex and dynamic microservice systems in the real world.