Active Queue Management (AQM), a network-layer congestion control technique endorsed by the Internet Engineering Task Force (IETF), encourages routers to discard packets before the occurrence of buffer overflow. Traditional AQM techniques often employ heuristic approaches that require meticulous parameter adjustments, limiting their real-world applicability. In contrast, Machine Learning (ML) approaches offer highly adaptive, data-driven solutions custom to dynamic network conditions. Consequently, many researchers have adapted ML for AQM throughout the years, resulting in a wide variety of algorithms ranging from predicting congestion via supervised learning to discovering optimal packet-dropping policies with reinforcement learning. Despite these remarkable advancements, no previous work has compiled these methods in the form of a survey article. This paper presents the first thorough documentation and analysis of ML-based algorithms for AQM, in which the strengths and limitations of each proposed method are evaluated and compared. In addition, a novel taxonomy of ML approaches based on methodology is also established. The review is concluded by discussing unexplored research gaps and potential new directions for more robust ML-AQM methods.

翻译：主动队列管理（AQM）是一种由互联网工程任务组（IETF）推荐的网络层拥塞控制技术，其促使路由器在缓冲区溢出发生之前主动丢弃数据包。传统的AQM技术通常采用启发式方法，需要精细的参数调整，这限制了其在真实场景中的适用性。相比之下，机器学习（ML）方法提供了高度自适应、数据驱动的解决方案，能够适应动态的网络环境。因此，多年来许多研究者将ML应用于AQM，产生了从通过监督学习预测拥塞到利用强化学习发现最优丢包策略的多种算法。尽管取得了这些显著进展，此前尚无工作以综述文章的形式系统整理这些方法。本文首次对基于ML的AQM算法进行了全面的梳理与分析，评估并比较了每种提出方法的优势与局限。此外，还建立了一种基于方法论的新型ML方法分类体系。最后，通过讨论尚未探索的研究空白以及潜在的新方向，为开发更鲁棒的ML-AQM方法提供展望。