Context. Microservice-based systems have gained significant attention over the past years. A critical factor for understanding and analyzing the behavior of these systems is the collection of monitoring data such as logs, metrics, and traces. These data modalities can be used for anomaly detection and root cause analysis of failures. In particular, multi-modal methods utilizing several types of this data at once have gained traction in the research community since these three modalities capture different dimensions of system behavior.

Aim. We provide a dataset that supports research on anomaly detection and architectural degradation in microservice systems. We generate a comprehensive dataset of logs, metrics, and traces from a production microservice system to enable the exploration of multi-modal fusion methods that integrate multiple data modalities. % to improve anomaly detection capabilities.

Method. We dynamically tested the various APIs of the MS-based system, implementing the OAuth2.0 protocol using the Locust tool. For each execution of the prepared test suite, we collect logs and performance metrics for correct and erroneous calls with data labeled according to the error triggered during the call.

Contributions. Logs and metrics were collected from a production-ready open-source Microservice-based system. We share the data set and the framework to replicate the data collection according to FAIR principles. We provide initial insights into analyzing logs, identifying key metrics through Principal Component Analysis, and addressing challenges in collecting traces for this system. Moreover, we highlight the possibilities for making a more fine-grained version of the data set. This work lays the foundation for advancing anomaly detection methods in microservice systems using complementary data sources.