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- Anomaly Detection in Microservices Using Execution Traces and Graph Neural NetworksPublication . Martins, Sara Maria da Silva; Freire, Mário MarquesTTraditional anomaly detection methods are often based on manual analysis of logs and are insufficient for the complex, multi-layered interactions of microservices. The existence of multiple calls to and from the same services, following different execution paths, is difficult to accommodate in traditional, usually linear, methods. Anomaly detection plays a key role in maintaining systems health and complying with Service-Level Agreements (SLAs) by identifying unusual behavior that may indicate system failures or security vulnerabilities. This shortcoming of traditional methods can mean that faults are not detected in microservice systems. Failure to properly monitor a system can lead to serious and potentially damaging failures, thus, to find ways of detecting new types of anomalies is imperative. This research work proposes a Graph Neural Network (GNN) model, with different convolutional layers and activation functions to optimize performance, using the AIOps Challenge 2020 dataset for evaluation. The results show that simpler models with fewer hidden layers achieve the best performance, often exceeding the accuracy of 99.9%. The research concludes that anomaly detection methods should be tailored to specific systems and that GNNs offers a promising approach to dealing with the complexities of microservice architectures.