Congestion Control for Cross-Datacenter Networks

• Published in: IEEE/ACM transactions on networking Vol. 30; no. 5; pp. 2074 - 2089
• Main Authors: Zeng, Gaoxiong, Bai, Wei, Chen, Ge, Chen, Kai, Han, Dongsu, Zhu, Yibo, Cui, Lei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.10.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Bandwidth; Buffers; Congestion; congestion control; Data centers; DCN; Delay; Delays; Geographical distribution; Low latency communication; Network latency; Packet loss; Reduction; Throughput; transport; Transport protocols; WAN; Wide area networks
• ISSN: 1063-6692; 1558-2566
• DOI: 10.1109/TNET.2022.3161580

Geographically distributed applications hosted on cloud are becoming prevalent. They run on cross-datacenter network that consists of multiple data center networks (DCNs) connected by a wide area network (WAN). Such a cross-DC network poses significant challenges in transport design because the DCN and WAN segments have vastly distinct characteristics ( e.g. , buffer depths, RTTs). In this paper, we find that existing DCN or WAN transport reacting to ECN or delay alone do not (and cannot be extended to) work well for such an environment. The key reason is that neither of the signals, by itself only, can simultaneously capture the location and degree of congestion, mainly due to the discrepancies between DCN and WAN. Motivated by this, we present the design and implementation of GEMINI that strategically integrates both ECN and delay signals for cross-DC congestion control. To achieve low latency, GEMINI bounds the inter-DC latency with delay signal and prevents the intra-DC packet loss with ECN. To maintain high throughput, GEMINI modulates the window dynamics and maintains low buffer occupancy utilizing both congestion signals. GEMINI is implemented in Linux kernel and evaluated by extensive testbed experiments. Results show that GEMINI achieves up to 53%, 31%, 76% and 2% reduction of small flow average completion times, and up to 34%, 39%, 9% and 58% reduction of large flow average completion times compared to TCP Cubic, DCTCP, BBR and TCP Vegas.