  Traffic Engineering (TE) is a basic building block of the Internet. In this
paper, we analyze whether modern Machine Learning (ML) methods are ready to be
used for TE optimization. We address this open question through a comparative
analysis between the state of the art in ML and the state of the art in TE. To
this end, we first present a novel distributed system for TE that leverages the
latest advancements in ML. Our system implements a novel architecture that
combines Multi-Agent Reinforcement Learning (MARL) and Graph Neural Networks
(GNN) to minimize network congestion. In our evaluation, we compare our
MARL+GNN system with DEFO, a network optimizer based on Constraint Programming
that represents the state of the art in TE. Our experimental results show that
the proposed MARL+GNN solution achieves equivalent performance to DEFO in a
wide variety of network scenarios including three real-world network
topologies. At the same time, we show that MARL+GNN can achieve significant
reductions in execution time (from the scale of minutes with DEFO to a few
seconds with our solution).
