Distributed Graph Neural Networks in Programmable Data Planes

The ability to redefine the data plane behavior with programmable network devices provides a plethora of novel possibilities for in-network computing. One of these possibilities is embedding Artificial Intelligence (AI) and Machine Learning (ML) techniques directly in the data plane. Motivations include reducing decision latency and closing the control loop-i.e., performing measurements, learning, decisions, and actions directly in the data plane. However, running entire AI/ML algorithms in a single device might be infeasible due to memory and computing constraints. This work addresses the research challenges of running a Graph Neural Network (GNN) in a set of devices of a programmable data plane. Our hypothesis is that by distributing the GNN processing across the devices, the GNN uses instantaneous snapshots of the global network state and can act more quickly. As a proof of concept, we trained and evaluated a distributed GNN to perform explicit congestion notifications based on Data Center Transmission Control Protocol (DCTCP). We verified the feasibility of GNN classification in the data plane through simulations and both software and hardware switch experiments with bmv2 and Intel Tofino.