Low Latency, Low Loss, and Scalable Throughput (L4S), as an emerging router-queue management technique, has seen steady deployment in the industry. An L4S-enabled router assigns each packet to the queue based on the packet header marking. Currently, L4S employs per-flow queue selection, i.e. all packets of a flow are marked the same way and thus use the same queues, even though each packet is marked separately. However, this may hurt tail latency and latency-sensitive applications because transient congestion and queue buildups may only affect a fraction of packets in a flow. We present SwiftQueue, a new L4S queue-selection strategy in which a sender uses a novel per-packet latency predictor to pinpoint which packets likely have latency spikes or drops. The insight is that many packet-level latency variations result from complex interactions among recent packets at shared router queues. Yet, these intricate packet-level latency patterns are hard to learn efficiently by traditional models. Instead, SwiftQueue uses a custom Transformer, which is well-studied for its expressiveness on sequential patterns, to predict the next packet's latency based on the latencies of recently received ACKs. Based on the predicted latency of each outgoing packet, SwiftQueue's sender dynamically marks the L4S packet header to assign packets to potentially different queues, even within the same flow. Using real network traces, we show that SwiftQueue is 45-65% more accurate in predicting latency and its variations than state-of-art methods. Based on its latency prediction, SwiftQueue reduces the tail latency for L4S-enabled flows by 36-45%, compared with the existing L4S queue-selection method.

翻译：低延迟、低损耗与可扩展吞吐量（L4S）作为一种新兴的路由器队列管理技术，已在工业界稳步部署。支持L4S的路由器根据数据包头标记将每个数据包分配到对应队列。目前，L4S采用基于流的队列选择策略，即同一流的所有数据包使用相同标记方式并因此进入相同队列，尽管每个数据包是独立标记的。然而，这种策略可能损害尾延迟和延迟敏感型应用，因为瞬态拥塞和队列堆积可能仅影响流中的部分数据包。我们提出SwiftQueue——一种新型L4S队列选择策略，其中发送端利用新颖的逐数据包延迟预测器，精确定位可能存在延迟尖峰或骤降的数据包。其核心洞察在于：许多数据包级延迟变化源于共享路由器队列中近期数据包间的复杂交互。然而，传统模型难以高效学习这些复杂的数据包级延迟模式。为此，SwiftQueue采用自定义Transformer（因其对序列模式的表达能力而被广泛研究），基于近期接收到的ACK延迟预测下一数据包的延迟。基于每个待发数据包的预测延迟，SwiftQueue的发送端动态标记L4S数据包头，使同一流内的数据包可被分配到不同队列。基于真实网络流量追踪的实验表明，SwiftQueue在延迟及其变化预测上的准确率比现有最佳方法高45-65%。凭借其延迟预测能力，相较于现有L4S队列选择方法，SwiftQueue可将启用L4S的流的尾延迟降低36-45%。