Utilizing Graph Neural Networks for Effective Link Prediction in Microservice Architectures

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.