  In the context of an efficient network traffic engineering process where the
network continuously measures a new traffic matrix and updates the set of paths
in the network, an automated process is required to quickly and efficiently
identify when and what set of paths should be used. Unfortunately, the burden
of finding the optimal solution for the network updating process in each given
time interval is high since the computation complexity of optimization
approaches using linear programming increases significantly as the size of the
network increases. In this paper, we use deep reinforcement learning to derive
a data-driven algorithm that does the path selection in the network considering
the overhead of route computation and path updates. Our proposed scheme
leverages information about past network behavior to identify a set of robust
paths to be used for multiple future time intervals to avoid the overhead of
updating the forwarding behavior of routers frequently. We compare the results
of our approach to other traffic engineering solutions through extensive
simulations across real network topologies. Our results demonstrate that our
scheme fares well by a factor of 40% with respect to reducing link utilization
compared to traditional TE schemes such as ECMP. Our scheme provides a slightly
higher link utilization (around 25%) compared to schemes that only minimize
link utilization and do not care about path updating overhead.
