PAQMAN: A Principled Approach to Active Queue Management

Abstract

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, denoted 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. Finally, we evaluate the performance of our approach using simulation compared to state-of-the-art AQM algorithms.

A Sketch-Based Fine-Grained Proportional Integral Queue Management Method

“…Pan C et al [14] reconfigure the packet drop policy model based on the average queue-length change rate, effectively mitigating the delay jitter problem generated by network traffic changes. Another AQM technique [15] uses a semi-Markov decision process to estimate the probability of dropping an arriving packet before it enters the buffer but requires manual setting of the target delay for various network types. Jakub Szyguła et al [16] proposed an adaptive mechanism, multiplicative increase/reduction (MID), which minimizes the queuing delay by eliminating queues generated by AQM that depend on fixed targets, thereby achieving lower latency and jitter.…”

Queue-Buffer Optimization Based on Aggressive Random Early Detection in Massive NB-IoT MANET for 5G Applications

“…It is, however, suitable for infrastructure-based devices with an ample amount of power, but due to the small queue buffer in IoT sensors in 5G, multiple threshold levels will not affect the performance as gaps between thresholds will be minimum. Another AQM technique [36] uses the Semi-Markov Decision Process to estimate the dropping probability of the arriving packets before they can be queued into the buffer. The method provides better results as compared to drop-tail and CoDel but needs to set the target delay manually for various network types.…”

Quadratic exponential random early detection: a new enhanced random early detection-oriented congestion control algorithm for routers

“…Zeng et al [17] described the active queue management (AQM) algorithm employed by routers as a more efficient way to relieve congestion. AQM algorithm detects congestion at an early stage and delivers a feedback signal (through dropping of packets) to traffic sources to slow down their transmission rate before the buffer is fully occupied [18]- [20]. AQM algorithms aimed at reducing packet loss rate, keeping average queue size small, increasing throughput and reducing delay [15].…”

“…Enhanced congestion control-RED (CoCo-RED) [40] is a refined version of CoCo-RED algorithm in which minTh and maxTh are adjusted depending on the congestion level. Kar et al [20] described the dropping probability as the central part of an AQM algorithm. Also, Koo et al [41] developing an AQM algorithm with a good drop probability was described as the hallmark of congestion control.…”