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.

本文介绍了一种新的算法 Credence，它通过机器学习预测来提升数据中心交换机的性能，与传统方法相比，Credence 改善了吞吐量和流完成时间，具有多个未来研究的潜在机会。

通过 ML 预测增强数据中心交换机缓冲区共享

Credence: Augmenting Datacenter Switch Buffer Sharing with ML  Predictions

Designing online algorithms with machine learning predictions is a recent
technique beyond the worst-case paradigm for various practically relevant
online problems (scheduling, caching, clustering, ski rental, etc.). While most
previous learning-augmented algorithm approaches focus on integrating the
predictions of a single oracle, we study the design of online algorithms with
\emph{multiple} experts. To go beyond the popular benchmark of a static best
expert in hindsight, we propose a new \emph{dynamic} benchmark (linear
combinations of predictions that change over time). We present a competitive
algorithm in the new dynamic benchmark with a performance guarantee of $O(\log
K)$, where $K$ is the number of experts, for $0-1$ online optimization
problems. Furthermore, our multiple-expert approach provides a new perspective
on how to combine in an online manner several online algorithms - a
long-standing central subject in the online algorithm research community.

设计在线算法，利用机器学习预测，以超越最坏情况范例，解决各种实际相关在线问题（如调度、缓存、聚类、滑雪租赁等）。通过研究设计具备多个专家的在线算法，以超越静态最佳专家的贪心基准。在新的动态基准中提出了具有 O（log K）性能保证的竞争算法，其中 K 是专家数量，适用于 0-1 在线优化问题。此外，我们的多专家方法提供了一种在线组合多个在线算法的新视角，这是在线算法研究社区长期的核心主题。

多专家在线覆盖

Online Covering with Multiple Experts

Fairness for machine learning predictions is widely required in practice for
legal, ethical, and societal reasons. Existing work typically focuses on
settings without unobserved confounding, even though unobserved confounding can
lead to severe violations of causal fairness and, thus, unfair predictions. In
this work, we analyze the sensitivity of causal fairness to unobserved
confounding. Our contributions are three-fold. First, we derive bounds for
causal fairness metrics under different sources of unobserved confounding. This
enables practitioners to examine the sensitivity of their machine learning
models to unobserved confounding in fairness-critical applications. Second, we
propose a novel neural framework for learning fair predictions, which allows us
to offer worst-case guarantees of the extent to which causal fairness can be
violated due to unobserved confounding. Third, we demonstrate the effectiveness
of our framework in a series of experiments, including a real-world case study
about predicting prison sentences. To the best of our knowledge, ours is the
first work to study causal fairness under unobserved confounding. To this end,
our work is of direct practical value as a refutation strategy to ensure the
fairness of predictions in high-stakes applications.

现实中，由于法律、伦理和社会原因，对机器学习预测的公平性要求越来越高。尽管现有工作通常关注没有未观察到混淆的情况，但未观察到的混淆可能导致严重的因果公平性违规和不公平的预测。本研究分析了未观察到的混淆对因果公平性的敏感性，并提出了一个新的神经框架用于学习公平的预测，同时在一系列实验中验证了该框架的有效性。