Grammar-Based Anomaly Detection of Microservice Systems Execution Traces
Andrea D’Angelo, Giordano d’Aloisio · 2024
Microservice architectures are a widely adopted architectural pattern for large-scale applications. Given the large adoption of these systems, several works have been proposed to detect performance anomalies starting from analysing the execution traces. However, most of the proposed approaches rely on machine learning (ML) algorithms to detect anomalies. While ML methods may be effective in detecting anomalies, the training and deployment of these systems as been shown to be less efficient in terms of time, computational resources, and energy required.