The increasing demand for latency-sensitive applications has necessitated the development of sophisticated algorithms that efficiently manage packets with end-to-end delay targets traversing the networked infrastructure. Network components must consider minimizing the packets' end-to-end delay violation probabilities (DVP) as a guiding principle throughout the transmission path to ensure timely deliveries. Active queue management (AQM) schemes are commonly used to mitigate congestion by dropping packets and controlling queuing delay. Today's established AQM schemes are threshold-driven, identifying congestion and trigger packet dropping using a predefined criteria which is unaware of packets' DVPs. In this work, we propose a novel framework, Delta, that combines end-to-end delay characterization with AQM for minimizing DVP. In a queuing theoretic environment, we show that such a policy is feasible by utilizing a data-driven approach to predict the queued packets' DVPs. That enables Delta AQM to effectively handle links with arbitrary stationary service time processes. The implementation is described in detail, and its performance is evaluated and compared with state of the art AQM algorithms. Our results show the Delta outperforms current AQM schemes substantially, in particular in scenarios where high reliability, i.e. high quantiles of the tail latency distribution, are of interest.

翻译：对延迟敏感应用的需求日益增长，迫使人们开发能够高效处理具有端到端时延目标的网络数据包的复杂算法。网络组件需以最小化数据包的端到端时延违反概率（DVP）作为传输路径上的指导原则，以确保及时交付。主动队列管理（AQM）方案通常用于通过丢包和控制排队时延来缓解拥塞。当前成熟的AQM方案基于阈值驱动，利用预设标准识别拥塞并触发丢包，但该标准未考虑数据包的DVP。本文提出了一种名为Delta的新型框架，将端到端时延特征分析与AQM相结合以最小化DVP。在排队论环境下，我们证明通过利用数据驱动方法预测排队数据包的DVP，此类策略是可行的。这使得Delta AQM能够有效处理具有任意平稳服务时间过程的链路。我们详细描述了其实现过程，并对其性能进行了评估及与现有先进AQM算法的比较。结果表明，Delta在性能上显著优于当前AQM方案，尤其在需要高可靠性（即尾延迟分布的高分位数）的场景中表现突出。