Anomaly detection in microservice environments using distributed tracing data analysis and NLP

Abstract

In recent years DevOps and agile approaches like microservice architectures and Continuous Integration have become extremely popular given the increasing need for flexible and scalable solutions. However, several factors such as their distribution in the network, the use of different technologies, their short life, etc. make microservices prone to the occurrence of anomalous system behaviours. In addition, due to the high degree of complexity of small services, it is difficult to adequately monitor the security and behavior of microservice environments. In this work, we propose an NLP (natural language processing) based approach to detect performance anomalies in spans during a given trace, besides locating release-over-release regressions. Notably, the whole system needs no prior knowledge, which facilitates the collection of training data. Our proposed approach benefits from distributed tracing data to collect sequences of events that happened during spans. Extensive experiments on real datasets demonstrate that the proposed method achieved an F_score of 0.9759. The results also reveal that in addition to the ability to detect anomalies and release-over-release regressions, our proposed approach speeds up root cause analysis by means of implemented visualization tools in Trace Compass.