PACC: Proactive and Accurate Congestion Feedback for RDMA Congestion Control
Xiaolong Zhong, Jiao Zhang, Yali Zhang, Zixuan Guan, Zirui Wan
Abstract
The rapid upgrade of link speed and the prosperity of new applications in data center networks (DCNs) lead to a rigorous demand for ultra-low latency and high throughput. To mitigate the overhead of traditional software-based packet processing at end-hosts, RDMA (Remote Direct Memory Access) has been widely adopted in DCNs. Particularly, congestion control (CC) mechanisms designed for RDMA have attracted much attention to avoid performance deterioration when packets lose. However, through comprehensive analysis, we found that existing RDMA CC schemes have limitations of a sluggish response to congestion and unawareness of tiny microbursts due to the long end-to-end control loop. In this paper, we propose PACC, a switch-driven RDMA CC algorithm with easy deployability. PACC is driven by PI controller-based computation, threshold-based flow discrimination and weight-based allocation at the switch. It leverages real-time queue length to generate accurate congestion feedback proactively and piggybacks it to the corresponding source without modification to end-hosts. We theoretically analyze the stability and key parameter settings of PACC. Then, we conduct both micro-benchmark and large-scale simulations to evaluate the performance of PACC. The results show that PACC achieves fairness, fast reaction, high throughput, and 6~69% lower FCT (Flow Completion Time) than DCQCN, TIMELY and HPCC.