Traffic Engineering in Large-scale Networks with Generalizable Graph Neural Networks

--Traffic engineering (TE) in large-scale computer networks has become a fundamental yet challenging problem, owing to the swift growth of global-scale cloud wide-area networks or backbone low-Earth-orbit satellite constellations. T o address the scalability issue of traditional TE algorithms, learning-based approaches have been proposed, showing potential of significant efficiency improvement over state-of-the-art methods. Nevertheless, the intrinsic limitations of existing learning-based methods hinder their practical application: they are not generalizable across diverse topologies and network conditions, incur excessive training overhead, and do not respect link capacities by default. This paper proposes TELGEN, a novel TE algorithm that learns to solve TE problems efficiently in large-scale networks, while achieving superior generalizability across diverse network conditions. TELGEN is based on the novel idea of transforming the problem of "predicting the optimal TE solution" into "predicting the optimal TE algorithm", which enables TELGEN to learn and efficiently approximate the end-to-end solving process of classical optimal TE algorithms. The learned algorithm is agnostic to the exact network topology or traffic patterns, and can efficiently solve TE problems given arbitrary inputs and generalize well to unseen topologies and demands. TELGEN achieved less than 3% optimality gap while ensuring feasibility in all cases, even when the test network had up to 20 more nodes than the largest in training. It also saved up to 84% solving time than classical optimal solver, and could reduce training time per epoch and solving time by 2 -4 orders of magnitude than latest learning algorithms on the largest networks. Traffic Engineering (TE) is becoming increasingly crucial amid the exponential growth in Internet traffic. Xue (xue@asu.edu) is with the School of Computing and Augmented Intelligence at the Arizona State University, Tempe, AZ, 85287, USA. The research of Zhou and Y u was supported in part by NSF grants 2045539 and 2433966. The research of Xue was sponsored in part by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-23-2-0225. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Army Research Laboratory or the U.S. Government. Personal use of this material is permitted. Usually, TE is implemented by a central controller that has a global view of the network and can make informed decisions about routing and traffic splitting to optimize traffic [26]. With the emergence of large-scale and dynamic networks, classical TE faces fundamental challenges in terms of scalability, responsiveness and performance.