cdcPIM: a proactive congestion control scheme for cross-datacenter RDMA networks

Abstract

Driven by the requirements of machine learning, cloud storage, and other network-intensive applications, remote direct memory access (RDMA) has been widely adopted in high-speed networks and is gradually being applied to geographically distributed datacenters. However, in cross-datacenter scenarios, long control loop latency and mixed traffic prevent existing RDMA congestion control schemes from perceiving and reacting to congestion in a timely and fair manner; this can lead to severe performance degradation and unfairness. To address these issues, we propose cdcPIM, a proactive congestion control scheme extended from datacenter parallel iterative matching (dcPIM) for cross-datacenter networks, which restructures the end-to-end control loop by introducing switch-coordinated control points, effectively transforming long-haul, RTT-bound feedback into localized control. Specifically, cdcPIM deploys a local control point by moving the token generation from the receiver to the sender side cross-datacenter switch, constraining the congestion control loop for inter-datacenter traffic within a single datacenter. Furthermore, cdcPIM introduces a remote control point to perform admission control for inter-datacenter traffic entering the receiver’s datacenter, thus avoiding intra-datacenter congestion caused by traffic bursts. Simulations demonstrate that when cdcPIM manages inter-datacenter traffic while cooperating with datacenter quantized congestion notification (DCQCN) for intra-datacenter traffic, long-haul congestion is effectively mitigated. Under mixed cross-datacenter workloads, DCQCN + cdcPIM reduces the overall average flow completion time (FCT) slowdown by up to 25.7% and the P99 FCT slowdown of intra-DC flows by up to 65.0% compared with the baseline scheme Themis.