Abstract
The transition from monolithic to microservice architectures has significantly increased the complexity of system observability. Distributed tracing has emerged as a vital tool for monitoring inter-service interactions, yet traditional anomaly detection methods often fail to capture the intricate structural relationships inherent in these systems. This article presents a comprehensive comparative analysis of sequence-based machine learning approaches, specifically Long Short-Term Memory (LSTM) networks, with Graph-based Deep Learning methods such as Graph Neural Networks (GNNs) for anomaly detection in microservice traces. We evaluate these paradigms across three critical dimensions: structural awareness, detection accuracy, and computational efficiency. Our analysis, drawn from recent frameworks including DeepTraLog and TraceVAE, demonstrates that graph-based models provide superior structural fidelity and achieve higher precision-recall metrics while maintaining scalability for high-dimensional telemetry data. The findings indicate that GNN-based approaches achieve F1-scores up to 0.954, significantly outperforming sequence-based alternatives which typically achieve F1-scores around 0.741. These results have important implications for the design of next-generation observability platforms for cloud-native applications.