Towards Network Model Generalization using Strategic Data Collection

Essential networking applications, such as video streaming, require accurate network models to estimate current and future network states (e.g., is the network congested?). Due to the complexity of today's networks and the subsequent difficulty of this modeling task, Machine Learning (ML)-based approaches have emerged as an alternative to first-principle modeling methods. However, proposed ML algorithms suffer from a generalization crisis: they often fail to perform in deployments outside of their training environment. Moreover, simple solutions such as naively training on more data do not guarantee improved generalization performance.