  Next-gen networks require significant evolution of management to enable
automation and adaptively adjust network configuration based on traffic
dynamics. The advent of software-defined networking (SDN) and programmable
switches enables flexibility and programmability. However, traditional
techniques that decide traffic policies are usually based on hand-crafted
programming optimization and heuristic algorithms. These techniques make
non-realistic assumptions, e.g., considering static network load and topology,
to obtain tractable solutions, which are inadequate for next-gen networks. In
this paper, we design and develop a deep reinforcement learning (DRL) approach
for adaptive traffic routing. We design a deep graph convolutional neural
network (DGCNN) integrated into the DRL framework to learn the traffic behavior
from not only the network topology but also link and node attributes. We adopt
the Deep Q-Learning technique to train the DGCNN model in the DRL framework
without the need for a labeled training dataset, enabling the framework to
quickly adapt to traffic dynamics. The model leverages q-value estimates to
select the routing path for every traffic flow request, balancing exploration
and exploitation. We perform extensive experiments with various traffic
patterns and compare the performance of the proposed approach with the Open
Shortest Path First (OSPF) protocol. The experimental results show the
effectiveness and adaptiveness of the proposed framework by increasing the
network throughput by up to 7.8% and reducing the traffic delay by up to 16.1%
compared to OSPF.
