Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures

2025-01-25

AI Summary

To address the challenge of predicting high-frequency, time-sensitive inter-service call relationships in microservice systems, this paper proposes a graph attention network (GAT)-based link prediction model tailored for dynamic call graphs. Methodologically, it introduces a novel integration of temporal graph segmentation and a customized dynamic negative sampling strategy—marking the first such combination—to alleviate the adaptability bottlenecks of conventional GNNs under strong dynamics and ultra-low-latency requirements. The model jointly leverages attention mechanisms and structural graph embeddings to enable adaptive monitoring and proactive performance issue mitigation. Evaluated on real-world microservice traces, it achieves an AUC exceeding 0.92 and an F1-score above 0.87, significantly outperforming baselines including GCN and Node2Vec. Empirical results validate its superiority in prediction accuracy, temporal robustness, and real-time responsiveness.

Technology Category

• Machine Learning: Graph-based Machine Learning
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Microservices
• Dynamic Interactions
• System Optimization

Innovation

• Graph Neural Networks
• Graph Attention Network
• Microservices Interaction Optimization