  Robust network design, which aims to guarantee network availability under
various failure scenarios while optimizing performance/cost objectives, has
received significant attention. Existing approaches often rely on model-based
mixed-integer optimization that is hard to scale or employ deep learning to
solve specific engineering problems yet with limited generalizability. In this
paper, we show that failure evaluation provides a common kernel to improve the
tractability and scalability of existing solutions. By providing a neural
network function approximation of this common kernel using graph attention
networks, we develop a unified learning-based framework, FERN, for scalable
Failure Evaluation and Robust Network design. FERN represents rich problem
inputs as a graph and captures both local and global views by attentively
performing feature extraction from the graph. It enables a broad range of
robust network design problems, including robust network validation, network
upgrade optimization, and fault-tolerant traffic engineering that are discussed
in this paper, to be recasted with respect to the common kernel and thus
computed efficiently using neural networks and over a small set of critical
failure scenarios. Extensive experiments on real-world network topologies show
that FERN can efficiently and accurately identify key failure scenarios for
both OSPF and optimal routing scheme, and generalizes well to different
topologies and input traffic patterns. It can speed up multiple robust network
design problems by more than 80x, 200x, 10x, respectively with negligible
performance gap.
