Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures

TL;DR

This study tackles the challenge of predicting future inter-service calls in microservice architectures by employing a Graph Attention Network (GAT) on temporally segmented call graphs. By constructing time-windowed graphs, applying advanced negative sampling, and leveraging multi-head attention, the method effectively captures the dynamic and dense interactions inherent in microservices. Experimental results on Alibaba's 2022 Cluster Trace show strong predictive performance across AUC, Precision, Recall, and F1, with robust interpretability via attention heatmaps and standard evaluation curves. The findings highlight the potential of GNN-based link prediction to enable proactive monitoring, adaptive resource management, and performance optimization in distributed systems. The approach is adaptable to different datasets and can be extended with richer features and scalable training to broaden its practical impact.

Abstract

Managing microservice architectures in distributed systems is complex and resource intensive due to the high frequency and dynamic nature of inter service interactions. Accurate prediction of these future interactions can enhance adaptive monitoring, enabling proactive maintenance and resolution of potential performance issues before they escalate. This study introduces a Graph Neural Network GNN based approach, specifically using a Graph Attention Network GAT, for link prediction in microservice Call Graphs. Unlike social networks, where interactions tend to occur sporadically and are often less frequent, microservice Call Graphs involve highly frequent and time sensitive interactions that are essential to operational performance. Our approach leverages temporal segmentation, advanced negative sampling, and GATs attention mechanisms to model these complex interactions accurately. Using real world data, we evaluate our model across performance metrics such as AUC, Precision, Recall, and F1 Score, demonstrating its high accuracy and robustness in predicting microservice interactions. Our findings support the potential of GNNs for proactive monitoring in distributed systems, paving the way for applications in adaptive resource management and performance optimization.

Figures (10)

• Figure 1: Methodology Diagram
• Figure 2: Epoch 0
• Figure 3: Epoch 49
• Figure 4: Epoch 99
• Figure 5: Epoch 149