主动队列管理中的最优决策
Optimal Decision Making in Active Queue Management
AI总结：
本文针对 AQM 启发式策略难以刻画流特征与拥塞影响的问题，构建 SMDP 得到最优丢包策略 PAQMAN，并提出 TCP 拥塞控制参数推理算法，在保持可比吞吐量的同时显著降低延迟。
摘要：
主动队列管理（AQM）旨在防止路由器和交换机 FIFO 数据包缓冲区中的缓冲膨胀与连续丢包，这些缓冲区通常采用弃尾队列机制。AQM 描述了通过选择性丢包或标记，向 TCP 流源发送主动反馈以调节其速率的方法。传统上，AQM 策略依赖启发式方法来近似提供服务质量（QoS），例如给定流的目标延迟。这些启发式方法通常基于简单的网络和 TCP 控制模型以及受监测的缓冲区填充情况。这些启发式方法的一个主要缺点是，它们将流特征纳入反馈机制的方式，以及其对拥塞状态产生的相应影响，尚未得到充分理解。在本研究中，我们表明，通过对流速率和出队模式采用概率模型，可以构建半马尔可夫决策过程（SMDP），从而获得最优的丢包策略。这种基于策略的 AQM 名为 PAQMAN，它考虑了 TCP 的稳态模型以及流的目标延迟。此外，我们提出一种建立在 TCP 拥塞控制之上的推理算法，用于校准支配底层网络状况的模型参数。通过仿真，我们表明所给出的 AQM 能够实现与最先进 AQM 算法相当的吞吐量，同时显著降低延迟。
英文摘要：
Active Queue Management (AQM) aims to prevent bufferbloat and serial drops in router and switch FIFO packet buffers that usually employ drop-tail queueing. AQM describes methods to send proactive feedback to TCP flow sources to regulate their rate using selective packet drops or markings. Traditionally, AQM policies relied on heuristics to approximately provide Quality of Service (QoS) such as a target delay for a given flow. These heuristics are usually based on simple network and TCP control models together with the monitored buffer filling. A primary drawback of these heuristics is that their way of accounting flow characteristics into the feedback mechanism and the corresponding effect on the state of congestion are not well understood. In this work, we show that taking a probabilistic model for the flow rates and the dequeueing pattern, a Semi-Markov Decision Process (SMDP) can be formulated to obtain an optimal packet-dropping policy. This policy-based AQM, named PAQMAN, takes into account a steady-state model of TCP and a target delay for the flows. Additionally, we present an inference algorithm that builds on TCP congestion control in order to calibrate the model parameters governing underlying network conditions. Using simulation, we show that the prescribed AQM yields comparable throughput to state-of-the-art AQM algorithms while reducing delays significantly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
