Trace-based Intelligent Fault Diagnosis for Microservices with Deep Learning

Due to the scalability, fault tolerance, and high availability, distributed microservice-based applications gradually replace traditional monolithic applications as one of the main forms of Internet applications. However, current fault diagnosis methods for distributed applications have drawbacks in coarse-grained fault location and inaccurate root-cause analysis. To address the above issues, this paper proposes a trace-based intelligent fault diagnosis approach for microservices with deep learning. First, we build a request weighted directed graph and a request string to characterize the behaviors of microservices with collected historical traces. Then, we build a normal trace dataset in normal status and a faulty dataset by injecting faults, and then calculate the expected intervals of microservices' response time and the call sequences. After that, we train the fault diagnosis model based on the deep neural network with the trace datasets to diagnose faulty microservices. Finally, we have deployed a typical open-source microservice-based application TrainTicket to validate our approach by injecting various typical faults. The results show that our approach can effectively characterize the behavior of microservices when processing requests and effectively detect faults. For fault detection, our approach achieves 91.5% accuracy in detecting faults, and has the accuracy of 85.2% in locating root causes.