Proactive Queue Management Research Articles

A queue length estimation and prediction model for long freeway off-ramps

Real-time queue length estimation and prediction provides useful information for proactively managing transportation networks. Queue spillback from off-ramps onto main lanes of freeways is one of the traffic issues caused by vehicular queues that can be efficiently managed using dynamic queue information. In this paper, a case-based reasoning algorithm combined with a Kalman filter is developed to provide real-time queue length estimations and predictions on freeway off-ramps. The estimations are based on occupancy readings from three loop detectors installed on a ramp. The proposed method is examined using a micro-simulation model of an off-ramp with a length of 650 meters and a traffic signal downstream of the ramp. The simulation results show an accuracy of ±3.15 vehicles in the queue in 60-second time intervals. In addition, a rigorous sensitivity analysis is conducted to examine the performance of the algorithm under various demand loading scenarios, time intervals, number of detectors used, and errors in prior estimations. The results show that the model performs well in terms of estimating and predicting the length of long queues on freeway off-ramps at various congestion levels. The outcomes of this study can be utilized to activate dynamic, responsive, and proactive queue management and traffic control measures.

A Fuzzy Logic Based Network Congestion Control Using Active Queue Management Techniques

Conventional IP routers are passive devices that accept packets and perform the routing function on any input. Usually the tail-drop (TD) strategy is used where the input which exceeds the buffer capacity are simply dropped. In active queue management (AQM) methods routers manage their buffers by dropping packets selectively. We study one of the AQM methods called as random exponential marking (REM). We propose an intelligent approach to AQM based on fuzzy logic controller (FLC) to drop packets dynamically, keep the buffer size around desired level and also prevent buffer overflow. Our proposed approach is based on REM algorithm, which drops the packets by drop probability function. In our proposal we replace the drop probability function by a FLC to drop the packets, stabilize the buffer around the desired size and reduce delay. Simulation results show a better regulation of the buffer. Keywords: Random exponential marking; Active queue management; Fuzzy logic controller; Pro-active queue management. © 2010 JSR Publications. ISSN: 2070-0237 (Print); 2070-0245 (Online). All rights reserved. DOI: 10.3329/jsr.v2i2.2786 J. Sci. Res. 2 (2), 273-284 (2010)

Lightweight proactive queue management

The quest for better resource control has been the driving force behind Active Queue Management (AQM) research. Random Early Detection (RED), the defacto standard and its variants have been proposed as simple solutions to the AQM problem. These approaches, however, are known to suffer from problems like parameter sensitivity and inability to capture input traffic load fluctuations accurately, thereby resulting in instability. This paper presents a proactive queue management algorithm called PAQMAN that captures input traffic load fluctuations accurately and regulates the queue size around the desirable level. PAQMAN draws from the predictability in the underlying traffic by employing the Recursive Least Squares (RLS) algorithm to forecast the average queue size over the next prediction interval using the average queue size information of the past intervals. The packet drop probability is then computed as a function of this predicted average queue size. The performance of PAQMAN has been evaluated and compared against existing AQM schemes through ns-2 simulations that encompass varying network conditions for networks comprising of single as well as multiple bottleneck links. Simulation results demonstrate that PAQMAN maintains a relatively low queue size, while at the same time achieving high link utilization and low packet loss. Moreover, the computational overhead of PAQMAN is negligible (lightweight) which further justifies its use.

PAQM: an adaptive and proactive queue management for end‐to‐end TCP congestion control

AbstractTwo functions, the congestion indicator (i.e. how to detect congestion) and the congestion control function (i.e. how to avoid and control congestion), are used at a router to support end‐to‐end congestion control in the Internet. Random early detection (RED) (IEEE/ACM Trans. Networking 1993; 1(4):397–413) enhanced the two functions by introducing queue length averaging and probabilistic early packet dropping. In particular, RED uses an exponentially weighted moving average (EWMA) queue length not only to detect incipient congestion but also to smooth the bursty incoming traffic and its resulting transient congestion. Following RED, many active queue management (AQM)‐based extensions have been proposed. However, many AQM proposals have shown severe problems with detection and control of the incipient congestion adaptively to the dynamically changing network situations. In this paper, we introduce and analyse a feedback control model of TCP/AQM dynamics. Then, we propose the Pro‐active Queue Management (PAQM) mechanism, which is able to provide proactive congestion avoidance and control using an adaptive congestion indicator and a control function under a wide range of traffic environments. The PAQM stabilizes the queue length around the desired level while giving smooth and low packet loss rates and high network resource utilization. Copyright © 2004 John Wiley & Sons, Ltd.

Advances in Active Queue Management (AQM) Based TCP Congestion Control

Current end-to-end Internet congestion control under tail-drop (TD) queue management experiences performance degradations such as multiple packet losses, high queueing delay and low link utilization. In this paper, we review recently proposed active queue management (AQM) algorithms for supporting end-to-end transmission control protocol (TCP) congestion control. We focus recently developed control theoretic design and analysis method for the AQM based TCP congestion control dynamics. In this context, we analyze the problems of existing AQM proposals in which congestion is detected and controlled reactively based on current and/or past congestion. Then we argue that AQM based TCP congestion control should be adaptive to the dynamically changing traffic situation in order to detect, control and avoid the current and the incipient congestion proactively. Finally, we survey two adaptive and proactive AQM algorithms, PID-controller and Pro-Active Queue Management (PAQM), designed using classical proportional-integral---derivative (PID) feedback control to overcome the reactive congestion control dynamics of existing AQM algorithms. A comparative study of these AQM algorithms with existing AQM algorithms is given. A simulation study under a wide range of realistic traffic conditions suggests that PID-controller and PAQM outperform other AQM algorithms such as random early detection (RED) [Floyd and Jacobson, 18] and proportional-integral (PI) controller [Hollot et al., 24].