  The rapid expansion of global cloud wide-area networks (WANs) has posed a
challenge for commercial optimization engines to efficiently solve network
traffic engineering (TE) problems at scale. Existing acceleration strategies
decompose TE optimization into concurrent subproblems but realize limited
parallelism due to an inherent tradeoff between run time and allocation
performance.
  We present Teal, a learning-based TE algorithm that leverages the parallel
processing power of GPUs to accelerate TE control. First, Teal designs a
flow-centric graph neural network (GNN) to capture WAN connectivity and network
flows, learning flow features as inputs to downstream allocation. Second, to
reduce the problem scale and make learning tractable, Teal employs a
multi-agent reinforcement learning (RL) algorithm to independently allocate
each traffic demand while optimizing a central TE objective. Finally, Teal
fine-tunes allocations with ADMM (Alternating Direction Method of Multipliers),
a highly parallelizable optimization algorithm for reducing constraint
violations such as overutilized links.
  We evaluate Teal using traffic matrices from Microsoft's WAN. On a large WAN
topology with >1,700 nodes, Teal generates near-optimal flow allocations while
running several orders of magnitude faster than the production optimization
engine. Compared with other TE acceleration schemes, Teal satisfies 6--32% more
traffic demand and yields 197--625x speedups.
