SaTE: Low-Latency Traffic Engineering for Satellite Networks

This paper explores traffic engineering (TE) for large-scale Low-Earth-Orbit satellite constellations. While there is rich prior work on TE algorithms for global cloud wide-area networks (WANs), they are designed for static network topologies and often require significant computation time for largescale networks. Such limitations make existing WAN TE algorithms unsuitable for large-scale satellite networks which rapidly change topology and require computing optimal traffic allocation under stringent latency constraints.

We present SaTE, a low-latency TE algorithm for largescale satellite networks, computing traffic allocation at millisecond latency. SaTE formulates a heterogeneous graph to model the TE problem, adapting to dynamic satellite topologies. By removing redundant graph relations, SaTE reduces computational latency, allowing the graph to be efficiently learned by a graph neural network that leverages GPUs to rapidly infer traffic allocations. SaTE also exploits the similarity of satellite network topologies and the geospatial distribution of traffic demands to facilitate model training. We evaluate SaTE through extensive data-driven simulation on today's largest satellite constellation, Starlink with 4236 satellites. Our results show over a 23.5% improvement in satisfied demand with an average TE runtime of 17 𝑚𝑠, achieving a 2738× speedup compared to commercial solvers.