Relevant bibliographies by topics / Active queue management (AQM)

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Academic literature on the topic 'Active queue management (AQM)'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 June 2025

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Journal articles on the topic "Active queue management (AQM)"

1

Chydziñski, Andrzej, and Łukasz Chróst. "Analysis of AQM queues with queue size based packet dropping." International Journal of Applied Mathematics and Computer Science 21, no. 3 (2011): 567–77. http://dx.doi.org/10.2478/v10006-011-0045-7.

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Analysis of AQM queues with queue size based packet dropping Queueing systems in which an arriving job is blocked and lost with a probability that depends on the queue size are studied. The study is motivated by the popularity of Active Queue Management (AQM) algorithms proposed for packet queueing in Internet routers. AQM algorithms often exploit the idea of queue-size based packet dropping. The main results include analytical solutions for queue size distribution, loss ratio and throughput. The analytical results are illustrated via numerical examples that include some commonly used blocking probabilities (dropping functions).
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Szyguła, Jakub, Adam Domański, Joanna Domańska, Dariusz Marek, Katarzyna Filus, and Szymon Mendla. "Supervised Learning of Neural Networks for Active Queue Management in the Internet." Sensors 21, no. 15 (2021): 4979. http://dx.doi.org/10.3390/s21154979.

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The paper examines the AQM mechanism based on neural networks. The active queue management allows packets to be dropped from the router’s queue before the buffer is full. The aim of the work is to use machine learning to create a model that copies the behavior of the AQM PIα mechanism. We create training samples taking into account the self-similarity of network traffic. The model uses fractional Gaussian noise as a source. The quantitative analysis is based on simulation. During the tests, we analyzed the length of the queue, the number of rejected packets and waiting times in the queues. The proposed mechanism shows the usefulness of the Active Queue Management mechanism based on Neural Networks.
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3

Ryu, Seungwan, Christopher Rump, and Chunming Qiao. "Advances in Active Queue Management (AQM) Based TCP Congestion Control." Telecommunication Systems 25, no. 3/4 (2004): 317–51. http://dx.doi.org/10.1023/b:tels.0000014788.49773.70.

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4

Baklizi, Mahmoud. "Weight Queue Dynamic Active Queue Management Algorithm." Symmetry 12, no. 12 (2020): 2077. http://dx.doi.org/10.3390/sym12122077.

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The current problem of packets generation and transformation around the world is router congestion, which then leads to a decline in the network performance in term of queuing delay (D) and packet loss (PL). The existing active queue management (AQM) algorithms do not optimize the network performance because these algorithms use static techniques for detecting and reacting to congestion at the router buffer. In this paper, a weight queue active queue management (WQDAQM) based on dynamic monitoring and reacting is proposed. Queue weight and the thresholds are dynamically adjusted based on the traffic load. WQDAQM controls the queue within the router buffer by stabilizing the queue weight between two thresholds dynamically. The WQDAQM algorithm is simulated and compared with the existing active queue management algorithms. The results reveal that the proposed method demonstrates better performance in terms mean queue length, D, PL, and dropping probability, compared to gentle random early detection (GRED), dynamic GRED, and stabilized dynamic GRED in both heavy or no-congestion cases. In detail, in a heavy congestion status, the proposed algorithm overperformed dynamic GRED (DGRED) by 13.3%, GRED by 19.2%, stabilized dynamic GRED (SDGRED) by 6.7% in term of mean queue length (mql). In terms of D in a heavy congestion status, the proposed algorithm overperformed DGRED by 13.3%, GRED by 19.3%, SDGRED by 6.3%. As for PL, the proposed algorithm overperformed DGRED by 15.5%, SDGRED by 19.8%, GRED by 86.3% in term of PL.
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Bisoy, Sukant Kishoro, and Prasant Kumar Pattnaik. "RQ-AQM: a rate and queue-based active queue management using feedback control theory." International Journal of Communication Networks and Distributed Systems 21, no. 2 (2018): 266. http://dx.doi.org/10.1504/ijcnds.2018.094204.

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Bisoy, Sukant Kishoro, and Prasant Kumar Pattnaik. "RQ-AQM: a rate and queue-based active queue management using feedback control theory." International Journal of Communication Networks and Distributed Systems 21, no. 2 (2018): 266. http://dx.doi.org/10.1504/ijcnds.2018.10014494.

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7

Abualhaj, Mosleh M., Mayy M. Al-Tahrawi, Abdelrahman H. Hussein, and Sumaya N. Al-Khatib. "Fuzzy-Logic Based Active Queue Management Using Performance Metrics Mapping into Multi-Congestion Indicators." Cybernetics and Information Technologies 21, no. 2 (2021): 29–44. http://dx.doi.org/10.2478/cait-2021-0017.

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Abstract The congestion problem at the router buffer leads to serious consequences on network performance. Active Queue Management (AQM) has been developed to react to any possible congestion at the router buffer at an early stage. The limitation of the existing fuzzy-based AQM is the utilization of indicators that do not address all the performance criteria and quality of services required. In this paper, a new method for active queue management is proposed based on using the fuzzy logic and multiple performance indicators that are extracted from the network performance metrics. These indicators are queue length, delta queue and expected loss. The simulation of the proposed method show that in high traffic load, the proposed method preserves packet loss, drop packet only when it is necessary and produce a satisfactory delay that outperformed the state-of-the-art AQM methods.
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8

Domański, Adam, Joanna Domańska, Tadeusz Czachórski, and Jerzy Klamka. "The use of a non-integer order PI controller with an active queue management mechanism." International Journal of Applied Mathematics and Computer Science 26, no. 4 (2016): 777–89. http://dx.doi.org/10.1515/amcs-2016-0055.

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AbstractIn this paper the performance of a fractional order PI controller is compared with that of RED, a well-known active queue management (AQM) mechanism. The article uses fluid flow approximation and discrete-event simulation to investigate the influence of the AQM policy on the packet loss probability, the queue length and its variability. The impact of self-similar traffic is also considered.
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9

Tabash, I. K., M. A. Mamun, and A. Negi. "A Fuzzy Logic Based Network Congestion Control Using Active Queue Management Techniques." Journal of Scientific Research 2, no. 2 (2010): 273–84. http://dx.doi.org/10.3329/jsr.v2i2.2786.

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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)
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10

Amigó, José M., Guillem Duran, Ángel Giménez, José Valero, and Oscar Martinez Bonastre. "Modeling a New AQM Model for Internet Chaotic Behavior Using Petri Nets." Applied Sciences 11, no. 13 (2021): 5877. http://dx.doi.org/10.3390/app11135877.

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Formal modeling is considered one of the fundamental phases in the design of network algorithms, including Active Queue Management (AQM) schemes. This article focuses on modeling with Petri nets (PNs) a new scheme of AQM. This innovative AQM is based on a discrete dynamical model of random early detection (RED) for controlling bifurcations and chaos in Internet congestion control. It incorporates new parameters (α,β) that make possible better stability control over oscillations of an average queue length (AQL) at the router. The PN is validated through the matrix equation approach, reachability tree, and invariant analysis. The correctness is validated through the key properties of reachability, boundedness, reversibility, deadlock, and liveness.
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