Congestion control is crucial for the overall performance of data center networks and still faces considerable challenges. Recently, credit-driven congestion control has been emerging to enable precise flow control for current high-speed and highly dynamic data centers. However, existing credit-driven methods essentially separate credit and data packets, i.e., credits can fully regulate data packets, but they receive little feedback from the data packets. Accordingly, these approaches inevitably struggle with lossy credits and impaired throughput. To address the issue, we present data-credit-coupling congestion control named DC4. For a better understanding of the relationship between data and credit, we revisit the principle of credit-based congestion control and make the first attempt to explore the art of presenting the data-credit plane architecture. Based on the proposed data-credit framework, DC4 transforms the interaction between credit and data packets from one-way control to two-way coordination to achieve mutual benefits and dynamic balances between the credit and data packets. We conduct extensive experiments to evaluate the performance of our design and compare it with state-of-the-art protocols, including HPCC, ExpressPass, and Aeolus. Experimental results show that DC4 outperforms data-credit-separated approaches in terms of the flow completion time, throughput, and credit waste.

Proactive transports nowadays have drawn much attention because of fast convergence, near-zero queueing and low latency. Proactive protocols, however, need an extra RTT to allocate ideal sending rate for new flows. To solve this, some studies, such as pHost, Homa, send unscheduled packets with line rate in the first RTT, which will causes severe network congestion. To avoid the queue buildup, Aeolus directly drops unscheduled packets when congestion occurs. Nevertheless, based on our experiment, a considerable part of small flows (0-100 KB) will be completed in the first RTT under 100 Gbps network, so that dropping unscheduled packets will severely affect performance of the small flows. In this paper we propose SSP, a new scheme aimed to eliminate the extra RTT delay and improve the flow completion time (FCT) of small flows under the proactive mechanism. Like pHost and Homa, SSP sends unscheduled packets at line rate when new flow arrives. Different from Aeolus, SSP selectively drops scheduled packets once queue buildup happens in the switch, thus protecting unscheduled packets which are more likely belong to small flows. Besides, based on the short-job-first (SJF) principle, we give relative higher priorities for small flows at the sender. Our simulation results with realistic workloads show that SSP can improve the FCT of small flows significantly. Specifically, under Web Search workload, SSP facilitates nearly 63% of 0-100 KB flows to complete one RTT faster. Also, SSP reduces the tail FCT by 56.8% at the 99th percentile compared with Expresspass and 29.2% compared with Aeolus while not leads to large queue buildup.

Recent proposals have leveraged emerging credit-based proactive transports to achieve high throughput low latency datacenter network transports. Particularly, those transports that employ hop-by-hop credits have the merits of fast convergence, low buffer occupancy, and strong congestion avoidability. However, they fairly transmit long flows and latency-sensitive short flows, which will cause the transmission latency of short flows and the average flow completion time increased. Although flow scheduling mechanisms have studied extensively to accelerate short flow transmission, they are hard to be directly applied in credit-based transports. The root cause is that most traditional flow scheduling mechanisms mainly work in the long queue containing flows in various sizes, while credit-based proactive transports maintain the extremely short bounded queue, near zero. Based on this observation, this paper makes the first attempt to accelerate short-flow scheduling in credit-based proactive transport, and proposed FastCredit. FastCredit can be used as a general building block to expedite short flows in credit-based proactive transports. In FastCredit, we schedule credit transmission at both receivers and switches to indirectly perform flow scheduling, and develop a mechanism to mitigate credit waste and improve network goodput. Compared to the state-of-the-art credit-based transport protocol, FastCredit reduces average flow completion time to 0.78x and greatly improves the short flow transmission latency to 0.51x in realistic workloads. Especially, FastCredit reduces average flow completion time to 0.76x under incast circumstances and 0.62x in many-to-one traffic mode. Furthermore, FastCredit still maintains the advantages of short queue and high throughput.