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.

Large Language Model (LLM) training increasingly relies on Remote Direct Memory Access (RDMA) to enable ultra-efficient networking. However, the unique traffic characteristics—sparse yet bandwidth-intensive—often lead to severe load imbalance under Equal-Cost Multi-Path (ECMP) routing. Packet Spraying (PS) offers a promising solution by distributing traffic across multiple paths, but its impact on congestion dynamics remains insufficiently studied. This paper presents a comprehensive study of PS in Artificial Intelligence (AI) clusters using NS-3 simulations, analyzing its effects on congestion distribution, packet reordering, and flow completion time. Our findings show that congestion patterns vary significantly with workload intensity and oversubscription ratios, and existing congestion control schemes are inadequate for general PS networks, where both routing paths and congestion hotspots frequently change. To address this gap, we propose APSCC, a congestion control algorithm that infers congestion locations from out-of-order packets and aggregates Explicit Congestion Notification (ECN) signals across paths for precise rate adaptation. Compared to state-of-the-art mechanisms, APSCC reduces Job Completion Time (JCT) by up to 30 %. The implementation is publicly available at https://github.com/tangjianback/APSCC.