  Traffic Engineering (TE) is critical for improving network performance and
reliability. A key challenge in TE is the management of sudden traffic bursts.
Existing TE schemes often struggle to accurately determine the extent of focus
required for these surges, thereby facing difficulties in achieving a balance
between performance under normal and peak traffic conditions. To address this
issue, we introduce FIGRET, a Fine-Grained Robustness-Enhanced TE Scheme.
FIGRET offers a novel approach to TE by providing varying levels of robustness
enhancements, customized according to the distinct traffic characteristics of
various source-destination pairs. By leveraging a sophisticated loss function
and advanced deep learning techniques, FIGRET is capable of generating
high-quality TE solutions efficiently. Our evaluations of real-world production
networks, including Wide Area Networks and data centers, demonstrate that
FIGRET significantly outperforms existing TE schemes. Compared to the TE scheme
currently deployed in the Jupiter network of Google, FIGRET achieves a 9\%-34\%
reduction in average Maximum Link Utilization and improves solution speed by
$35\times$-$1800 \times$. Against DOTE, a state-of-the-art deep learning-based
TE method, FIGRET substantially lowers the occurrence of significant congestion
events triggered by traffic bursts by 41\%-53.9\% in topologies characterized
by high traffic dynamics.
