Packet buffers in datacenter switches are shared across all the switch ports in order to improve the overall throughput. The trend of shrinking buffer sizes in datacenter switches makes buffer sharing extremely challenging and a critical performance issue. Literature suggests that push-out buffer sharing algorithms have significantly better performance guarantees compared to drop-tail algorithms. Unfortunately, switches are unable to benefit from these algorithms due to lack of support for push-out operations in hardware. Our key observation is that drop-tail buffers can emulate push-out buffers if the future packet arrivals are known ahead of time. This suggests that augmenting drop-tail algorithms with predictions about the future arrivals has the potential to significantly improve performance. This paper is the first research attempt in this direction. We propose Credence, a drop-tail buffer sharing algorithm augmented with machine-learned predictions. Credence can unlock the performance only attainable by push-out algorithms so far. Its performance hinges on the accuracy of predictions. Specifically, Credence achieves near-optimal performance of the best known push-out algorithm LQD (Longest Queue Drop) with perfect predictions, but gracefully degrades to the performance of the simplest drop-tail algorithm Complete Sharing when the prediction error gets arbitrarily worse. Our evaluations show that Credence improves throughput by $1.5$x compared to traditional approaches. In terms of flow completion times, we show that Credence improves upon the state-of-the-art approaches by up to $95\%$ using off-the-shelf machine learning techniques that are also practical in today's hardware. We believe this work opens several interesting future work opportunities both in systems and theory that we discuss at the end of this paper.

翻译：数据中心交换机的数据包缓冲区在所有端口间共享以提升整体吞吐量。随着交换机缓冲区容量持续缩减，缓冲区共享成为极具挑战性的关键性能问题。文献表明，推挤式缓冲区共享算法相比丢弃尾算法具有显著更优的性能保障。然而，由于硬件缺乏对推挤操作的支持，交换机无法从这些算法中受益。我们的关键发现是：若能提前预知未来数据包到达情况，丢弃尾缓冲区可以模拟推挤缓冲区。这表明，将未来到达预测信息融入丢弃尾算法有望大幅提升性能。本文是该方向的首项研究工作。我们提出Credence——一种融合机器学习预测的丢弃尾缓冲区共享算法。Credence能够解锁此前仅有推挤算法可实现的性能，其表现取决于预测精度。具体而言，当预测完美时，Credence达到已知最优推挤算法LQD（最长队列丢弃）的近乎最优性能；当预测误差任意恶化时，性能则会优雅退化为最简丢弃尾算法Complete Sharing。评估表明，与传统方法相比，Credence的吞吐量提升1.5倍。在流完成时间方面，我们证明，利用现有硬件可实现的现成机器学习技术，Credence相比最新方法最多可改进95%。我们相信，这项工作为系统与理论领域开辟了诸多值得探索的未来方向，论文末尾将对此展开讨论。