Incast-Free MoE Rate-Based Scheduling

Abstract:Mixture of Experts (MoE) architectures have become key to large language models; however, their typical round-robin (RR) scheduling introduces significant bottlenecks. In this paper, we demonstrate that RR causes a previously-undiscovered exponential incast phenomenon with MoE traffic. We propose an alternative proactive fair scheduling framework tailored for MoE workloads, which effectively prevents fabric oversubscription. We also outline how it can be implemented in NICs. Finally, through extensive simulations with real and synthetic workloads, we demonstrate that this framework consistently eliminates incast, maintains a near-100% link utilization, and reduces Collective Completion Time (CCT).

Networking and Internet Architecture (cs.NI); Distributed, Parallel, and Cluster Computing (cs.DC); Machine Learning (cs.LG)

arXiv:2607.26340 [cs.NI]

(or arXiv:2607.26340v1 [cs.NI] for this version)

https://doi.org/10.48550/arXiv.2607.26340 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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