An RDMA Congestion Control Enhancement Framework for Edge Datacenters

Le résumé fourni par la source

With the rapid expansion of Internet of Things (IoT) devices and the growing demand for real-time data processing, Remote Direct Memory Access (RDMA) has become increasingly vital in edge datacenters due to its high performance and low CPU utilization. To fully harness RDMA’s potential, a lossless underlying network is essential, typically achieved through hop-by-hop Priority Flow Control (PFC). However, existing RDMA congestion control mechanisms in edge datacenters struggle to achieve rapid rate convergence and may exacerbate PFC side effects such as head-of-line blocking, unfairness, and even deadlocks. In this paper, we propose a reinforcement learning-based RDMA congestion control enhancement framework called RDI. By leveraging receiver-side information and network congestion levels, RDI provides precise rate guidance for congested flows, alleviating congestion and mitigating PFC issues. Furthermore, RDI combines both online and offline learning to assist receiver-side information, achieving finer rate adjustments to dynamically adapt to changing network workloads and optimize congestion control performance. RDI is transparent and compatible with existing RDMA network architectures, requiring no modifications to network devices. Extensive simulations under realistic traffic patterns show that the congestion control scheme enhanced by RDI significantly outperforms the original mechanisms in terms of throughput and flow completion time (FCT), while also reducing PFC side effects. RDI-enhanced congestion control reduces the 99th-percentile tail FCT by up to 92% and the average FCT by up to 60%.

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