CATE: A Cross-Attention-Transformer Based Method for Traffic Engineering

In this paper, we propose a Cross-AttentionTransformer Based Method for Traffic Engineering, CATE, which employs the cross attention mechanism to analyze traffic patterns and dynamically apply different strategies based on traffic characteristics. CATE effectively balances normal network performance with burst traffic resilience, ensuring the delivery of high-quality solutions across various network topologies. Preliminary evaluations on real-world wide-area network (WAN) topology datasets demonstrate that CATE outperforms existing state-of-the-art machine learning-based TE approaches as well as traditional linear programming methods, achieving a significant reduction in average maximum link utilization and improved computational efficiency.