A Survey on Active Queue Management Techniques

Sharma, A. K.

doi:10.18535/ijecs/v5i11.47

Cited by 4 publications

(4 citation statements)

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“…The classical techniques include the Fixed-Parameter Proportional Integral (PI) and the Random Early Detection (RED). The modern techniques are the Self-Tuning Regulator (STR) and the Model Predictive Control (MPC) [8]. Most of the work done on adaptive network congestion control techniques only provided the algorithm, but they did not consider the effect of varying network conditions on congestion control parameters and the subsequent effect on their respective controllers.…”

Section: The Problemmentioning

confidence: 99%

Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions

Okokpujie

Chukwu

Shobayo

et al. 2019

IJECE

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This paper demonstrates the robustness of active queue management techniques to varying load, link capacity and propagation delay in a wireless environment. The performances of four standard controllers used in Transmission Control Protocol/Active Queue Management (TCP/AQM) systems were compared. The active queue management controllers were the Fixed-Parameter Proportional Integral (PI), Random Early Detection (RED), Self-Tuning Regulator (STR) and the Model Predictive Control (MPC). The robustness of the congestion control algorithm of each technique was documented by simulating the varying conditions using MATLAB® and Simulink® software. From the results obtained, the MPC controller gives the best result in terms of response time and controllability in a wireless network with varying link capacity and propagation delay. Thus, the MPC controller is the best bet when adaptive algorithms are to be employed in a wireless network environment. The MPC controller can also be recommended for heterogeneous networks where the network load cannot be estimated.

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Section: The Problemmentioning

confidence: 99%

Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions

Okokpujie

Chukwu

Shobayo

et al. 2019

IJECE

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“…The algorithms used for AQM are complex and difficult to implement, however are efficient enough in case of heavy traffic and high computational overhead. [24] The algorithm believes in congestion avoidance rather than congestion control. The source node in this case plays a great role to control the flow of the data packets according to the predicted congestion.…”

Section: Active Queue Managementmentioning

confidence: 99%

Comparison between Different Queue Management Techniques in Mobile Ad-Hoc Network: A Survey

Sahu¹,

Mahapatro²,

Dash³

2018

IJET

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The world is running in a lightning speed with the rapid up gradation of technology. In fraction of second human civilization experiences a unique set of technologies and for this human sentiment started to demand better quality of service in almost all the areas which provides ease to it by minimizing human work efforts. Communication becomes an integral part of this rapidly changing technology. From the introduction of internet, the taste and preferences of virtual communication has been changed. A new technology called as Mobile Ad Hoc Network (MANet) is also getting its popularity due to its infrastructure less and mobility property. The rapid increase in demand for use of MANets tends it to further development to provide better quality of service. However Congestion in these networks creates a challenge to achieve desired Quality of Service. Different Queue Management techniques such as Passive and Active Queue management techniques are used to minimize the congestion in these networks which uses the concept of managing the buffers though algorithms. The Passive Queue Management technique Droptail, in the introduction stage, somehow manages to increase its efficiency. However in later stage, when data traffic increases, due to global synchronization problem many other algorithms including active queue management techniques such as RED and variants, REM have been developed to increase the efficiency by decreasing the congestion. The concepts even travel from congestion control to congestion avoidance. In this paper we have taken into account different passive and active queue management techniques and compared them based on our literature review to find out the best among them by considering different situations.

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“…Congestion mainly occurs when network resources are insufficient to meet the needs of service flows, or when the distribution of network service flows is unbalanced, causing an increase in the packet loss rate of the link, increase in the delay between nodes, increase network jitter, and reduce network throughput [2], Therefore, the use of effective methods to achieve congestion control has become a hot issue in current network research. At present, many algorithms have been proposed, such as RED, REM [3], Drop Tail Queue Management, DRED [4], PI controller [5], PID controller [6], and so on. But they all have some shortcomings.…”

Section: Introductionmentioning

confidence: 99%

Congestion Management in MPLS Network Based on Hybrid Particle Swarm Algorithm

Yousif

2021

J. Al-Qadisiyah Comp. Sci. Math.

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To minimize the network congestion problem, a multi-constraint optimization mathematical model is established and a hybrid particle swarm algorithm based on the Tabu search algorithm is proposed. The algorithm integrates Tabu search into particle swarm algorithm, which can prevent the algorithm from falling into the local optimal solution, and gives a better initial individual for Tabu search, so it can speed up the algorithm convergence and improve the quality of the solution. The simulation was done using Matlab and the results show that the optimization algorithm reduces the end-to-end delay and packet loss rate, thereby avoiding network congestion and optimizing the utilization of network resources.

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A Survey on Active Queue Management Techniques

Cited by 4 publications

References 0 publications

Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions

Comparative analysis of the performance of various active queue management techniques to varying wireless network conditions

Comparison between Different Queue Management Techniques in Mobile Ad-Hoc Network: A Survey

Congestion Management in MPLS Network Based on Hybrid Particle Swarm Algorithm

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