FastCredit: Expediting Credit-based Proactive Transports in Datacenters

• Published in: 2020 IEEE 26th International Conference on Parallel and Distributed Systems (ICPADS) pp. 528 - 535
• Main Authors: Dong, Dezun, Huang, Shan, Zhou, Zejia, Yang, Wenxiang, Shi, Hanyi
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-12-2020
• Subjects: Acceleration; credit-based transports; data center networks; Data centers; flow scheduling; Proposals; Receivers; Schedules; Throughput; Transport protocols
• ISSN: 2690-5965
• DOI: 10.1109/ICPADS51040.2020.00074

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.