Graph Neural Networks for Metrics Prediction in Microservice Architecture

The article examines the issues of monitoring performance in microservice architectures. We explore the problem of forecasting performance indicators as well as fault propagation in such systems, which are distributed and have independent service deployments. The paper addresses these issues by proposing a novel approach that uses multimetric time series data to establish causal relationships between microservices and build graph neural networks based on revealed system dependencies. The method’s goal is to proactively forecast performance indicators and fault propagation in order to assure the resilience and reliability of microservices. Various graph neural network architectures are discussed. The best one DCRNN uses a diffusion convolutional recurrent neural network in a basis and is able to predict well both on data with and without anomalies.