Congestion control is a key enabler for lossless Ethernet at scale. In this paper, we revisit this classic topic from a new perspective, i.e., understanding and exploiting the intrinsic properties of the underlying lossless network. We experimentally and analytically find that the intrinsic properties of lossless networks, such as packet conservation, can indeed provide valuable implications in estimating pipe capacity and the precise number of excessive packets. Besides, we derive principles on how to treat congested flows and victim flows individually to handle HoL blocking efficiently. Then, we propose ACK-driven congestion control (ACC) for lossless Ethernet, which simply resorts to the knowledge of ACK time series (supports ACK coalescing) to exert a temporary halt to exactly drain out excessive packets of congested flows and then match its rate to pipe capacity. Testbed and large-scale simulations demonstrate that ACC ameliorates fundamental issues in lossless Ethernet (e.g., congestion spreading, HoL blocking, and deadlock) and achieves excellent low latency and high throughput performance. For instance, compared with existing schemes, ACC improves the average and 99th percentile FCT performance of small flows by$1.3\sim 3.3\times $and$1.4\sim 11.5\times $, respectively.

The nature of datacenter traffic is a high volume of bursty tiny flows and standing long flows, which forms the coexistence of transient and persistent congestion. Traditional congestion control (CC) algorithms have inherent limitations in reconciling fast response and high efficiency towards transients with stability and fairness during persistence. In this paper, we provide an insight that re-architects CC with two control laws, tailored to transient and persistent concerns, respectively. Armed with this key insight, we propose bimodal congestion control (BCC), which is founded on two core ideas: (i) Quaternary network state detection, which further distinguishes transient and persistent states in switches, and (ii) Bimodal control law, which is manifested as the transient controller and persistent controller at sources. The transient controller employs a precise control paradigm that pauses flows to drain backlogged packets and ramps down/up flow rates to bottleneck bandwidth directly, striving for high efficiency. The persistent controller grounds itself in traditional CC algorithms, inheriting stability and fairness. We implement BCC in the Linux kernel and P4-programmable switch. In our evaluation, compared to DCQCN, HPCC, PowerTCP, and Swift, BCC reduces flow completion times by 14% ~ 99%.

Datacenter applications increasingly demand microsecond-scale latency and tight tail latency. Despite recent advances in datacenter transport protocols, we notice that the timeout caused by packet loss is the killer of microsecond-scale latency. Moreover, refining the RTO setting is impractical due to the significant fluctuations in RTT. In this paper, we propose explicit dropping notification (EDN) to avoid timeouts. EDN rekindles ICMP Source Quench, where the switch notifies the source of precise packet loss information. Then the source can rapidly pinpoint dropped packets for fast retransmission instead of waiting for timeouts. More importantly, fast retransmission does not mean immediate retransmission which is prone to aggravate congestion and deteriorate latency. In light of this, we suggest finessing the timing and sending rate of retransmission. Specifically, as a reward of the paradigm shift to explicit notification, the source can pause for the queue draining time piggybacked on EDN messages and estimate connection capacity to figure out a proper sending rate, thus avoiding congestion aggravation. We implement EDN on the P4-programmable switching ASIC and Linux kernel. Evaluations show that, compared with state-of-the-art loss recovery schemes, EDN reduces the latency by up to 4.1× on average and 3.6× at the 99th-percentile.