Load Balancing for AI Training Workloads
arXiv:2507.21372v14 citationsh-index: 63
Originality Synthesis-oriented
AI Analysis
This work addresses load balancing for AI training workloads, which is an incremental improvement in optimizing infrastructure efficiency.
The paper investigated load balancing algorithms for large-scale AI training workloads on dedicated infrastructure, finding that performance depends on congestion control and loss recovery algorithms, with evaluations providing insights into optimal design choices.
We investigate the performance of various load balancing algorithms for large-scale AI training workloads that are running on dedicated infrastructure. The performance of load balancing depends on both the congestion control and loss recovery algorithms, so our evaluation also sheds light on the appropriate choices for those designs as well.