The rapid evolution of cloud computing, big data, and Artificial Intelligence (AI) technologies has created an urgent demand for ultra-high bandwidth and ultra-low latency in modern data centers. Although Remote Direct Memory Access (RDMA) technology has been widely adopted, traditional congestion control algorithms such as Priority-based Flow Control (PFC) and Data Center Quantized Congestion Notification (DCQCN) face significant challenges in scalability, deployment flexibility, and tail latency management in complex network environments. To address these limitations, we propose SFCC, a sender-driven rate control solution based on Round-Trip Time (RTT) that requires no switch configuration. SFCC incorporates an adaptive Additive Increase Multiplicative Decrease (AIMD) control loop for enhanced scalability and latency reduction, an RTT re-estimation mechanism to correct deviations, dynamic RTT calculation for optimized queue management, and Negative Acknowledgment (NAK) signal utilization for fast convergence during packet loss. Implemented on commercial RDMA Network Interface Cards (NICs) using a Programmable Congestion Control (PCC) platform, SFCC has been evaluated through small-scale testbeds, storage network testbeds, and NS-3 simulations of ultra-large-scale incast scenarios. Experimental results demonstrate that SFCC significantly improves throughput while reducing switch queue lengths, decreases the 99th percentile tail latency by 55.2 %, maintains high reliability under packet loss conditions, and achieves up to$\mathbf{7 5. 4 \%}$reduction in flow completion time for small flows compared to DCQCN, along with faster convergence speed.

Congestion control plays a crucial role in ensuring the performance of data center networks. However, mainstream RDMA congestion control algorithms still face challenges such as slow congestion response and poor deployability. In this paper, we propose a novel fast-convergence congestion control algorithm, FCC, to address these shortcomings. FCC leverages Explicit Congestion Notification (ECN) and Round-Trip Time (RTT) signals, utilizing the gradient of RTT to enhance response speed and employing a Sigmoid curve for rate increment. Through experiments, we demonstrate that compared to existing state-of-the-art algorithms, FCC achieves superior performance in terms of convergence speed, fairness, and small flow latency metrics.

The rapid upgrade of link speed and the prosperity of new applications in data center networks (DCNs) lead to a rigorous demand for ultra-low latency and high throughput. To mitigate the overhead of traditional software-based packet processing at end-hosts, RDMA (Remote Direct Memory Access) has been widely adopted in DCNs. Particularly, congestion control (CC) mechanisms designed for RDMA have attracted much attention to avoid performance deterioration when packets lose. However, through comprehensive analysis, we found that existing RDMA CC schemes have limitations of a sluggish response to congestion and unawareness of tiny microbursts due to the long end-to-end control loop. In this paper, we propose PACC, a proactive and accurate switch-driven RDMA CC algorithm with easy deployability. PACC is driven by PI controller-based computation, threshold-based flow discrimination and weight-based allocation at the switch. It leverages real-time queue length to generate accurate congestion feedback proactively and piggybacks it to the corresponding source without modification to end-hosts. We theoretically analyze the stability, convergence and key parameter settings of PACC. Then, we implement PACC in a testbed consisting of DPDK-based end-hosts and Tofino P4 switches. In our evaluation, PACC achieves better fairness, fast reaction, high throughput, and 6$\sim$69% lower FCT (Flow Completion Time) than DCQCN, TIMELY, HPCC and RoCC.