Improving Availability and Scalability for RDMA Load Balancing with In-network Reordering
Jinbin Hu, Ruiqian Li, Shuying Rao, Jin Wang · 2024
Remote Direct Memory Access (RDMA) is widely deployed in datacenter networks (DCNs) due to its ultra-low latency, high throughput, and low CPU overhead. Since RDMA is sensitive to out-of-order packets, the previous load balancing schemes designed based on TCP do not work well in RDMA networks. Recently proposed load balancing schemes focus on solving packet reordering within the network. However, the existing solutions cannot be extended in practice because the required queues far exceed common switch capabilities. In this paper, we propose a scalable and efficient load balancing called SELB to improve availability and scalability. SELB employs a clustering algorithm to categorize equal-cost paths and then reroutes traffic to the same cluster parallel paths to reduce the degree of out-of-order and improve queue utilization. The NS-3 simulation results demonstrate that SELB reduces the average flow completion time (FCT) and the 99th percentile FCT by up to 33% and 21%, respectively, compared to the state-of-the-art load balancing schemes.