End‐to‐Network Load Balancing for <scp>AI</scp> Data Center Networks: A Convergence‐Based Approach to Enhance Training Efficiency

ABSTRACT In large‐scale language model training, network performance is a crucial determinant of training efficiency. Traditional load balancing methods, such as equal‐cost multipath (ECMP), often suffer from hash polarization, leading to suboptimal traffic distribution—particularly in scenarios with limited flow counts and a dominance of elephant flows. To mitigate this challenge, this paper introduces end‐to‐network load balancing (ENLB), a novel and readily deployable scheme that optimizes uplink utilization through coordinated server‐switch traffic scheduling. Leveraging end‐to‐network convergence principles, ENLB enhances bandwidth efficiency while minimizing flow completion times. Simulation and experimental evaluations demonstrate that ENLB improves network bandwidth utilization by up to 38% and reduces model training task durations by over 3% compared to conventional ECMP‐based approaches. These findings underscore ENLB's potential as a scalable solution for modern AI Data Center (AIDC) networks.