Relevant bibliographies by topics / Active queue management (AQM)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

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

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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

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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 (September 1, 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 (July 22, 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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Ryu, Seungwan, Christopher Rump, and Chunming Qiao. "Advances in Active Queue Management (AQM) Based TCP Congestion Control." Telecommunication Systems 25, no. 3/4 (March 2004): 317–51. http://dx.doi.org/10.1023/b:tels.0000014788.49773.70.

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Baklizi, Mahmoud. "Weight Queue Dynamic Active Queue Management Algorithm." Symmetry 12, no. 12 (December 14, 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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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 (June 1, 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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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 (December 1, 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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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 (April 26, 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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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 (June 24, 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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Dissertations / Theses on the topic "Active queue management (AQM)"

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Phirke, Vishal Vasudeo. "Traffic Sensitive Active Queue Management for Improved Quality of Service." Digital WPI, 2002. https://digitalcommons.wpi.edu/etd-theses/780.

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The Internet, traditionally FTP, e-mail and Web traffic, is increasingly supporting emerging applications such as IP telephony, video conferencing and online games. These new genres of applications have different requirements in terms of throughput and delay than traditional applications. For example, interactive multimedia applications, unlike traditional applications, have more stringent delay constraints and less stringent loss constraints. Unfortunately, the current Internet offers a monolithic best-effort service to all applications without considering their specific requirements. Adaptive RED (ARED) is an Active Queue Management (AQM) technique, which optimizes the router for throughput. Throughput optimization provides acceptable QoS for traditional throughput sensitive applications, but is unfair for these new delay sensitive applications. While previous work has used different classes of QoS at the router to accommodate applications with varying requirements, thus far all have provided just 2 or 3 classes of service for applications to choose from. We propose two AQM mechanisms to optimize router for better overall QoS. Our first mechanism, RED-Worcester, is a simple extension to ARED in order to tune ARED for better average QoS support. Our second mechanism, REDBoston, further extends RED-Worcester to improve the QoS for all flows. Unlike earlier approaches, we do not predefine classes of service, but instead provide a continuum from which applications can choose. We evaluate our approach using NS-2 and present results showing the amount of improvement in QoS achieved by our mechanisms over ARED.
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Mohamed, Mahmud H. Etbega. "Some Active Queue Management Methods for Controlling Packet Queueing Delay. Design and Performance Evaluation of Some New Versions of Active Queue Management Schemes for Controlling Packet Queueing Delay in a Buffer to Satisfy Quality of Service Requirements for Real-time Multimedia Applications." Thesis, University of Bradford, 2009. http://hdl.handle.net/10454/4258.

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Traditionally the Internet is used for the following applications: FTP, e-mail and Web traffic. However in the recent years the Internet is increasingly supporting emerging applications such as IP telephony, video conferencing and online games. These new applications have different requirements in terms of throughput and delay than traditional applications. For example, interactive multimedia applications, unlike traditional applications, have more strict delay constraints and less strict loss constraints. Unfortunately, the current Internet offers only a best-effort service to all applications without any consideration to the applications specific requirements. In this thesis three existing Active Queue Management (AQM) mechanisms are modified by incorporating into these a control function to condition routers for better Quality of Service (QoS). Specifically, delay is considered as the key QoS metric as it is the most important metric for real-time multimedia applications. The first modified mechanism is Drop Tail (DT), which is a simple mechanism in comparison with most AQM schemes. A dynamic threshold has been added to DT in order to maintain packet queueing delay at a specified value. The modified mechanism is referred to as Adaptive Drop Tail (ADT). The second mechanism considered is Early Random Drop (ERD) and, iii in a similar way to ADT, a dynamic threshold has been used to keep the delay at a required value, the main difference being that packets are now dropped probabilistically before the queue reaches full capacity. This mechanism is referred to as Adaptive Early Random Drop (AERD). The final mechanism considered is motivated by the well known Random Early Detection AQM mechanism and is effectively a multi-threshold version of AERD in which packets are dropped with a linear function between the two thresholds and the second threshold is moveable in order to change the slope of the dropping function. This mechanism is called Multi Threshold Adaptive Early Random Drop (MTAERD) and is used in a similar way to the other mechanisms to maintain delay around a specified level. The main focus with all the mechanisms is on queueing delay, which is a significant component of end-to-end delay, and also on reducing the jitter (delay variation) A control algorithm is developed using an analytical model that specifies the delay as a function of the queue threshold position and this function has been used in a simulation to adjust the threshold to an effective value to maintain the delay around a specified value as the packet arrival rate changes over time. iv A two state Markov Modulated Poisson Process is used as the arrival process to each of the three systems to introduce burstiness and correlation of the packet inter-arrival times and to present sudden changes in the arrival process as might be encountered when TCP is used as the transport protocol and step changes the size of its congestion window. In the investigations it is assumed the traffic source is a mixture of TCP and UDP traffic and that the mechanisms conserved apply to the TCP based data. It is also assumed that this consists of the majority proportion of the total traffic so that the control mechanisms have a significant effect on controlling the overall delay. The three mechanisms are evaluated using a Java framework and results are presented showing the amount of improvement in QoS that can be achieved by the mechanisms over their non-adaptive counterparts. The mechanisms are also compared with each other and conclusions drawn.
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Li, Zhi. "Fuzzy logic based robust control of queue management and optimal treatment of traffic over TCP/IP networks." University of Southern Queensland, Faculty of Sciences, 2005. http://eprints.usq.edu.au/archive/00001461/.

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Improving network performance in terms of efficiency, fairness in the bandwidth, and system stability has been a research issue for decades. Current Internet traffic control maintains sophistication in end TCPs but simplicity in routers. In each router, incoming packets queue up in a buffer for transmission until the buffer is full, and then the packets are dropped. This router queue management strategy is referred to as Drop Tail. End TCPs eventually detect packet losses and slow down their sending rates to ease congestion in the network. This way, the aggregate sending rate converges to the network capacity. In the past, Drop Tail has been adopted in most routers in the Internet due to its simplicity of implementation and practicability with light traffic loads. However Drop Tail, with heavy-loaded traffic, causes not only high loss rate and low network throughput, but also long packet delay and lengthy congestion conditions. To address these problems, active queue management (AQM) has been proposed with the idea of proactively and selectively dropping packets before an output buffer is full. The essence of AQM is to drop packets in such a way that the congestion avoidance strategy of TCP works most effectively. Significant efforts in developing AQM have been made since random early detection (RED), the first prominent AQM other than Drop Tail, was introduced in 1993. Although various AQMs also tend to improve fairness in bandwidth among flows, the vulnerability of short-lived flows persists due to the conservative nature of TCP. It has been revealed that short-lived flows take up traffic with a relatively small percentage of bytes but in a large number of flows. From the user’s point of view, there is an expectation of timely delivery of short-lived flows. Our approach is to apply artificial intelligence technologies, particularly fuzzy logic (FL), to address these two issues: an effective AQM scheme, and preferential treatment for short-lived flows. Inspired by the success of FL in the robust control of nonlinear complex systems, our hypothesis is that the Internet is one of the most complex systems and FL can be applied to it. First of all, state of the art AQM schemes outperform Drop Tail, but their performance is not consistent under different network scenarios. Research reveals that this inconsistency is due to the selection of congestion indicators. Most existing AQM schemes are reliant on queue length, input rate, and extreme events occurring in the routers, such as a full queue and an empty queue. This drawback might be overcome by introducing an indicator which takes account of not only input traffic but also queue occupancy for early congestion notification. The congestion indicator chosen in this research is traffic load factor. Traffic load factor is in fact dimensionless and thus independent of link capacity, and also it is easy to use in more complex networks where different traffic classes coexist. The traffic load indicator is a descriptive measure of the complex communication network, and is well suited for use in FL control theory. Based on the traffic load indicator, AQM using FL – or FLAQM – is explored and two FLAQM algorithms are proposed. Secondly, a mice and elephants (ME) strategy is proposed for addressing the problem of the vulnerability of short-lived flows. The idea behind ME is to treat short-lived flows preferably over bulk flows. ME’s operational location is chosen at user premise gateways, where surplus processing resources are available compared to other places. By giving absolute priority to short-lived flows, both short and long-lived flows can benefit. One problem with ME is starvation of elephants or long-lived flows. This issue is addressed by dynamically adjusting the threshold distinguishing between mice and elephants with the guarantee that minimum capacity is maintained for elephants. The method used to dynamically adjust the threshold is to apply FL. FLAQM is deployed to control the elephant queue with consideration of capacity usage of mice packets. In addition, flow states in a ME router are periodically updated to maintain the data storage. The application of the traffic load factor for early congestion notification and the ME strategy have been evaluated via extensive experimental simulations with a range of traffic load conditions. The results show that the proposed two FLAQM algorithms outperform some well-known AQM schemes in all the investigated network circumstances in terms of both user-centric measures and network-centric measures. The ME strategy, with the use of FLAQM to control long-lived flow queues, improves not only the performance of short-lived flows but also the overall performance of the network without disadvantaging long-lived flows.
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Al-Hammouri, Ahmad Tawfiq. "INTERNET CONGESTION CONTROL: COMPLETE STABILITY REGION FOR PI AQM AND BANDWIDTH ALLOCATION IN NETWORKED CONTROL." online version, 2008. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=case1189088621.

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Wang, Lan. "Performance modeling of congestion control and resource allocation under heterogeneous network traffic : modeling and analysis of active queue management mechanism in the presence of poisson and bursty traffic arrival processes." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/4455.

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Along with playing an ever-increasing role in the integration of other communication networks and expanding in application diversities, the current Internet suffers from serious overuse and congestion bottlenecks. Efficient congestion control is fundamental to ensure the Internet reliability, satisfy the specified Quality-of-Service (QoS) constraints and achieve desirable performance in response to varying application scenarios. Active Queue Management (AQM) is a promising scheme to support end-to-end Transmission Control Protocol (TCP) congestion control because it enables the sender to react appropriately to the real network situation. Analytical performance models are powerful tools which can be adopted to investigate optimal setting of AQM parameters. Among the existing research efforts in this field, however, there is a current lack of analytical models that can be viewed as a cost-effective performance evaluation tool for AQM in the presence of heterogeneous traffic, generated by various network applications. This thesis aims to provide a generic and extensible analytical framework for analyzing AQM congestion control for various traffic types, such as non-bursty Poisson and bursty Markov-Modulated Poisson Process (MMPP) traffic. Specifically, the Markov analytical models are developed for AQM congestion control scheme coupled with queue thresholds and then are adopted to derive expressions for important QoS metrics. The main contributions of this thesis are listed as follows: • Study the queueing systems for modeling AQM scheme subject to single-class and multiple-classes Poisson traffic, respectively. Analyze the effects of the varying threshold, mean traffic arrival rate, service rate and buffer capacity on the key performance metrics. • Propose an analytical model for AQM scheme with single class bursty traffic and investigate how burstiness and correlations affect the performance metrics. The analytical results reveal that high burstiness and correlation can result in significant degradation of AQM performance, such as increased queueing delay and packet loss probability, and reduced throughput and utlization. • Develop an analytical model for a single server queueing system with AQM in the presence of heterogeneous traffic and evaluate the aggregate and marginal performance subject to different threshold values, burstiness degree and correlation. • Conduct stochastic analysis of a single-server system with single-queue and multiple-queues, respectively, for AQM scheme in the presence of multiple priority traffic classes scheduled by the Priority Resume (PR) policy. • Carry out the performance comparison of AQM with PR and First-In First-Out (FIFO) scheme and compare the performance of AQM with single PR priority queue and multiple priority queues, respectively.
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Fares, Rasha H. A. "Performance modelling and analysis of congestion control mechanisms for communication networks with quality of service constraints. An investigation into new methods of controlling congestion and mean delay in communication networks with both short range dependent and long range dependent traffic." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5435.

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Active Queue Management (AQM) schemes are used for ensuring the Quality of Service (QoS) in telecommunication networks. However, they are sensitive to parameter settings and have weaknesses in detecting and controlling congestion under dynamically changing network situations. Another drawback for the AQM algorithms is that they have been applied only on the Markovian models which are considered as Short Range Dependent (SRD) traffic models. However, traffic measurements from communication networks have shown that network traffic can exhibit self-similar as well as Long Range Dependent (LRD) properties. Therefore, it is important to design new algorithms not only to control congestion but also to have the ability to predict the onset of congestion within a network. An aim of this research is to devise some new congestion control methods for communication networks that make use of various traffic characteristics, such as LRD, which has not previously been employed in congestion control methods currently used in the Internet. A queueing model with a number of ON/OFF sources has been used and this incorporates a novel congestion prediction algorithm for AQM. The simulation results have shown that applying the algorithm can provide better performance than an equivalent system without the prediction. Modifying the algorithm by the inclusion of a sliding window mechanism has been shown to further improve the performance in terms of controlling the total number of packets within the system and improving the throughput. Also considered is the important problem of maintaining QoS constraints, such as mean delay, which is crucially important in providing satisfactory transmission of real-time services over multi-service networks like the Internet and which were not originally designed for this purpose. An algorithm has been developed to provide a control strategy that operates on a buffer which incorporates a moveable threshold. The algorithm has been developed to control the mean delay by dynamically adjusting the threshold, which, in turn, controls the effective arrival rate by randomly dropping packets. This work has been carried out using a mixture of computer simulation and analytical modelling. The performance of the new methods that have
Ministry of Higher Education in Egypt and the Egyptian Cultural Centre and Educational Bureau in London
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Fares, Rasha Hamed Abdel Moaty. "Performance modelling and analysis of congestion control mechanisms for communication networks with quality of service constraints : an investigation into new methods of controlling congestion and mean delay in communication networks with both short range dependent and long range dependent traffic." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5435.

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Active Queue Management (AQM) schemes are used for ensuring the Quality of Service (QoS) in telecommunication networks. However, they are sensitive to parameter settings and have weaknesses in detecting and controlling congestion under dynamically changing network situations. Another drawback for the AQM algorithms is that they have been applied only on the Markovian models which are considered as Short Range Dependent (SRD) traffic models. However, traffic measurements from communication networks have shown that network traffic can exhibit self-similar as well as Long Range Dependent (LRD) properties. Therefore, it is important to design new algorithms not only to control congestion but also to have the ability to predict the onset of congestion within a network. An aim of this research is to devise some new congestion control methods for communication networks that make use of various traffic characteristics, such as LRD, which has not previously been employed in congestion control methods currently used in the Internet. A queueing model with a number of ON/OFF sources has been used and this incorporates a novel congestion prediction algorithm for AQM. The simulation results have shown that applying the algorithm can provide better performance than an equivalent system without the prediction. Modifying the algorithm by the inclusion of a sliding window mechanism has been shown to further improve the performance in terms of controlling the total number of packets within the system and improving the throughput. Also considered is the important problem of maintaining QoS constraints, such as mean delay, which is crucially important in providing satisfactory transmission of real-time services over multi-service networks like the Internet and which were not originally designed for this purpose. An algorithm has been developed to provide a control strategy that operates on a buffer which incorporates a moveable threshold. The algorithm has been developed to control the mean delay by dynamically adjusting the threshold, which, in turn, controls the effective arrival rate by randomly dropping packets. This work has been carried out using a mixture of computer simulation and analytical modelling. The performance of the new methods that have.
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Chung, Jae Won. "Congestion control for streaming media." Link to electronic thesis, 2005. http://www.wpi.edu/Pubs/ETD/Available/etd-081805-084831/.

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Dissertation (Ph.D.) -- Worcester Polytechnic Institute.
Keywords: streaming media; streaming transport protocol; active queue management (AQM); Internet congestion control. Includes bibliographical references (p. 236-248).
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Nikaeen, Ramin. "Combined Queue-Rate Active Queue Management for Internet congestion control." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63024.pdf.

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Dahlberg, Love. "A Data Plane native PPV PIE Active Queue Mangement Scheme using P4 on a Programmable Switching ASIC." Thesis, Karlstads universitet, Institutionen för matematik och datavetenskap (from 2013), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-84552.

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New internet services require low and stable latency, which is difficult to provide with traditional routers and queuing mechanisms. Current routers aim to provide high throughput using large buffers causing considerable network latency under load. Recently, Active Queue Management (AQM) algorithms have been proposed to reduce such problem by actively controlling queue lengths to maintain target latencies. However, AQMs are difficult to implement in switching Application-Specific Integrated Circuits (ASIC) due to inherent architectural constraints. On the other hand, resource sharing is another important goal aiming to differentiate traffic and allocating more resources to different traffic types.  The objective of this thesis is to implement the AQM algorithm Proportional Integral Controller Enhanced (PIE) with a packet marking based resource sharing concept Per Packet Value (PPV) on a programmable switching ASIC using the novel network programmability concept P4. Our solution is designed to maintain low and controllable latency and to utilize the bottleneck link efficiently, while observing the bandwidth sharing properties of the marking scheme. Our goal is to show that Data Plane native implementations of PPV PIE using the Tofino is possible without severely limiting performance or accuracy. The solution places the computation of PIE's drop probability estimation on a timer in the Data Plane utilizing a state machine, packet mirroring, packet recirculation and approximative arithmetics implemented by lookup tables. Additionally, a small control loop is required in order to update lookup tables based on packet statistics from the Control Plane.  In our evaluation using a Tofino based testbed, we evaluate the impact of different parameters on both Control Plane latency, Data Plane throughput and delay for both static and dynamic traffic scenarios. Our results demonstrate commendable performance in terms of controlling queuing delay, effective throughput and bandwidth share when taking operator policy in regard.
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Books on the topic "Active queue management (AQM)"

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Nikaeen, Ramin. Combined queue-rate active queue management for internet congestion control. Ottawa: National Library of Canada, 2001.

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Active Queue Management Mechanisms for Real-Time Traffic in MANETs. Storming Media, 2001.

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Book chapters on the topic "Active queue management (AQM)"

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Menth, Michael, and Sebastian Veith. "Active Queue Management Based on Congestion Policing (CP-AQM)." In Lecture Notes in Computer Science, 173–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74947-1_12.

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Chatranon, Gwyn, Miguel A. Labrador, and Sujata Banerjee. "A Credit-Based Active Queue Management (AQM) Mechanism to Achieve Fairness in the Internet." In NETWORKING 2005. Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communications Systems, 930–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11422778_75.

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George, Jean, and R. Santhosh. "Congestion Control Mechanism for Unresponsive Flows in Internet Through Active Queue Management System (AQM)." In Mobile Computing and Sustainable Informatics, 765–77. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-1866-6_58.

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Jalili-Kharaajoo, M. "Sliding Mode Queue Management in TCP/AQM Networks." In Telecommunications and Networking - ICT 2004, 638–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-27824-5_86.

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Jalili-Kharaajoo, Mahdi. "Adaptive Fuzzy Queue Management and Congestion Avoidance in TCP/AQM Networks." In Lecture Notes in Computer Science, 196–208. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30233-9_15.

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Che, Lichang, and Bin Qiu. "Fuzzy Predictive Preferential Dropping for Active Queue Management." In Lecture Notes in Computer Science, 336–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552451_44.

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Kind, Andreas, and Bernard Metzler. "Rate-Based Active Queue Management with Token Buckets." In Lecture Notes in Computer Science, 176–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45076-4_18.

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Li, Zhi, and Zhongwei Zhang. "A Coupled Fuzzy Logic Control for Routers’ Queue Management over TCP/AQM Networks." In Lecture Notes in Computer Science, 357–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11552451_47.

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Phirke, Vishal, Mark Claypool, and Robert Kinicki. "Traffic Sensitive Active Queue Management for Improved Multimedia Streaming." In Quality of Service in Multiservice IP Networks, 551–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-36480-3_40.

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Ryu, Seungwan, and Christopher Rump. "PAQM: Pro-active Queue Management for Internet Congestion Control." In Operations Research/Computer Science Interfaces Series, 245–72. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4757-3762-2_13.

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Conference papers on the topic "Active queue management (AQM)"

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Shah, K., S. Bohacek, and E. Jonckheere. "On the performance limitation of active queue management (AQM)." In 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). IEEE, 2004. http://dx.doi.org/10.1109/cdc.2004.1428819.

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Xue, Y., H. V. Nguyen, and K. Nahrstedt. "CA-AQM: Channel-Aware Active Queue Management for Wireless Networks." In 2007 IEEE International Conference on Communications. IEEE, 2007. http://dx.doi.org/10.1109/icc.2007.788.

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Xunhe Yin, Qingquan Cui, Xianglong Meng, Yongkang Xiao, and Y. D. Song. "An active queue management (AQM) scheme based on modified Smith Predictor." In 2009 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2009. http://dx.doi.org/10.1109/ivs.2009.5164382.

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Havey, Daniel M., and Kevin C. Almeroth. "Active Sense Queue Management (ASQM)." In 2015 IFIP Networking Conference (IFIP Networking). IEEE, 2015. http://dx.doi.org/10.1109/ifipnetworking.2015.7145297.

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Tsung-Yen Chen, Chang-Kuo Chen, Jun-Juh Yan, and Teh-Lu Liao. "Robust active queue management controller design for a stochastic TCP/AQM system." In 2009 International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2009. http://dx.doi.org/10.1109/icnsc.2009.4919335.

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Hassan, Suhaidi, Atheer F. Hassan, and Suki Arif. "HF-AQM: An efficient active queue management scheme for wireless local area network environment." In TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE, 2017. http://dx.doi.org/10.1109/tencon.2017.8228402.

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Pibiri, Gianluigi, Ciarán Mc Goldrick, and Meriel Huggard. "Using active queue management to enhance performance in IEEE802.11." In the 4th ACM workshop. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1641913.1641923.

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Martin, James, Gongbing Hong, and James Westall. "Managing fairness and application performance with active queue management in docsis-based cable networks." In SIGCOMM'14: ACM SIGCOMM 2014 Conference. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2630088.2630092.

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Ardelean, Dan, Ethan Blanton, and Maxim Martynov. "Remote active queue management." In the 18th International Workshop. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1496046.1496052.

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Claypool, M., R. Kinicki, and A. Kumar. "Traffic Sensitive Active Queue Management." In Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications. IEEE, 2006. http://dx.doi.org/10.1109/infocom.2006.336.

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Reports on the topic "Active queue management (AQM)"

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Ros, D. Characterization Guidelines for Active Queue Management (AQM). Edited by N. Kuhn, P. Natarajan, and N. Khademi. RFC Editor, July 2016. http://dx.doi.org/10.17487/rfc7928.

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White, G., and R. Pan. Active Queue Management (AQM) Based on Proportional Integral Controller Enhanced PIE) for Data-Over-Cable Service Interface Specifications (DOCSIS) Cable Modems. RFC Editor, February 2017. http://dx.doi.org/10.17487/rfc8034.

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Nichols, K., and V. Jacobson. Controlled Delay Active Queue Management. Edited by A. McGregor and J. Iyengar. RFC Editor, January 2018. http://dx.doi.org/10.17487/rfc8289.

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Jou, Jia-Shiang, and John S. Baras. A Parallel Virtual Queue Structure for Active Queue Management. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada439709.

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Baker, F., and G. Fairhurst, eds. IETF Recommendations Regarding Active Queue Management. RFC Editor, July 2015. http://dx.doi.org/10.17487/rfc7567.

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Hoeiland-Joergensen, T., P. McKenney, D. Taht, J. Gettys, and E. Dumazet. The Flow Queue CoDel Packet Scheduler and Active Queue Management Algorithm. RFC Editor, January 2018. http://dx.doi.org/10.17487/rfc8290.

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