Aether: Toward Generalized Traffic Engineering with Elastic Multi-agent Graph Transformers

Recent algorithms show deep learning's potential to efficiently allocate traffic flows in wide-area networks (WANs). However, current learning-based methods ignore the importance of edges correlations and traffic differentiation, and have limitations in generalizing to multiple networks, let alone unseen ones, making them impractical. In this paper, we propose a novel traffic engineering algorithm, Aether, which excels in generalizing across different networks and different amount of demands. We propose an elastic multi-agent graph transformer where each agent process a demand, and agents are sequentially modeled by graph transformer to enhance both representation capability and generalization ability. To improve effectiveness, we propose hierarchical graph neural networks which model inter- and intra-relations between edges and paths. And a differentiated traffic strategy is proposed which handles small flows with rules, letting the model focuses on larger flows for better learning. Experiments on real-world data show that our model outperforms state-of-the-art learning-based approaches, achieving 12.1 % to 31.7% better MLU without network-specific training, while demonstrating scalability for large networks. It is worth highlighting that Aether's performance is only 3.9% lower on MLU compared to its network-specific trained version.