As network simulations scale to hundreds of thousands of nodes, parallel discrete event simulation (PDES) has become indispensable for sustaining performance—yet its efficacy hinges on frequent time synchronization steps. Existing synchronization algorithms suffer from the inter-machine communication overhead in distributed environments, eroding the benefits of parallelism. We observe that time synchronization in PDES often involves frequent allreduce operations, and that in-network computing has the potential to substantially accelerate such collectives. In this work, we present SINA (Synchronization using In-Network Allreduce)—the first integration of in-network computing into time synchronization to address the demands of large-scale simulations. We implemented SINA in a testbed with Mellanox SHArP-enabled switches and ConnectX-5 network cards, offloading allreduce to the network hardware while preserving software‑level correctness. Our evaluation shows that SINA achieves up to 88.6% acceleration compared to state‑of‑the‑art methods and achieves up to 67.5% optimization in topologies of tens of thousands of nodes, demonstrating its suitability for high‑performance, large‑scale parallel simulations.

The limitations of reactive congestion control protocols in scheduling buffer become more apparent due to its long feedback delay. As a result, the switch buffer management (BM) policy respond directly to complex traffic within the network is critical to optimizing network performance because it determines the buffer occupancy pattern. Nevertheless, the existing BMs exhibit an inadequate consideration of the unique traffic character of contention scenarios, thereby overlooking the potential problem of threshold truncation. After analysis, we find that the root cause of this problem is that existing BMs are sensitive to contention. Therefore, we blunt BM's perceptibility to contention changes (BPC) to avoid the possible threshold truncation. Experiments show that BPC can avoid packet loss and retransmission triggered by the threshold truncation.

Delay-based congestion control (CC) protocols have been widely used in data centers, where senders monitor the real-time delay and adjust the sending rate or congestion window accordingly. We propose Rately, a CC algorithm that utilizes accurate one-way delay (OWD) measurements and a compatible rate regulation mechanism. Experiments show that Rately greatly improves performance under realistic workload.

Applications such as cloud computing, big data processing, and artificial intelligence, demand high bandwidth and low latency in datacenter networks. Existing congestion control and flow control schemes at switches have limitations in granularity, fairness, and signal transmission delay. This paper proposes a fast notified per-hop per-flow backpressure congestion management, called BCN. BCN dynamically allocates queues for each flow at each switch hop, precisely pauses upstream queues based on queuing conditions, and enables non-paused queues to continue transmission. The switch sends network status notifications, collaborating with sender-side speed up and deceleration strategies to achieve rapid traffic rate adjustment. To the best of our knowledge, this is the first work focusing on fast rate adjustment of per-hop per-flow traffic. We evaluate BCN in real traffic scenarios and observe significant reductions in flow completion time of 53% and 60% compared to BFC and DCQCN, respectively. Under high workload conditions, BCN also improves network throughput. In high incast scenarios, BCN outperforms BFC and DCQCN, reducing flow completion time by 67.7% and 74.2%, respectively, while maintaining throughput comparable to BFC. Additionally, BCN minimizes transmission delay, ensuring nearly lossless transmission and optimizing overall throughput.