Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures
This addresses the challenge of managing complex and dynamic microservice interactions for system administrators and developers, though it is incremental as it applies an existing GNN method to a new domain.
The study tackled the problem of predicting future interactions in microservice architectures to enable proactive maintenance, using a Graph Neural Network (GAT) approach that achieved high accuracy and robustness in predicting microservice interactions, as demonstrated by performance metrics like AUC, Precision, Recall, and F1 Score on real-world data.
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.