Simplifying Prioritization and Scheduling with P2CS
TL;DR
Evaluation on representative workloads demonstrates that P2CS achieves performance comparable to in-network mechanisms while significantly reducing complexity and cost, and requires minimal software changes making it readily deployable in today's datacenter infrastructure.
Abstract
Modern datacenter networks host diverse services and workloads with varying quality-of-service (QoS) requirements, yet are constrained by hardware limitations. Most notably, the small number of physical priority queues available in commodity switches. Existing scheduling mechanisms, whether end-host or in-network based, struggle to scale under these constraints due to their reliance on global priority information or complex queue management. This paper presents P2CS (Priority-based Probabilistic Congestion Signaling), a lightweight and scalable approach that enables fine-grained traffic prioritization using only a single FIFO queue. P2CS combines priority-aware probabilistic congestion signaling, priority-aware packet dropping, and simple switch-side arbitration to enforce prioritization across flows. P2CS supports a range of scheduling objectives, and requires minimal software changes making it readily deployable in today's datacenter infrastructure. Evaluation on representative workloads, including multi-tenant ML training, HPC, and mixed spray/ECMP traffic, demonstrates that P2CS achieves performance comparable to in-network mechanisms while significantly reducing complexity and cost.