Reconfigurable Nonlinear GRED Algorithm

Al-Allaf, Ahmad F.; Jabbar, A. I. A.

doi:10.12785/ijcds/090521

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…However, the default active queue management (AQM) 1 algorithm is used as congestion control in Congestion Avoidance phase to reduce packet dropping and maintain the stability of data flow in the router's queue. Many queue management algorithms have been developed to handle congestion that focused on managing congestion in Congestion Avoidance phase, such as the Random Early Detection (RED) algorithm 2 , Adaptive Random Early Detection (ARED) algorithm 3 , Gentle Random Early Detection (GRED) 4 , NonLinear Random Early Detection (NLRED) 5 , and Reconfigurable Non-Linear Gentle Random Early Detection (RNLGRED) 6 .…”

mentioning

confidence: 99%

Improving RED algorithm congestion control by using the Markov decision process

Mahawish

Hassan

2022

Sci Rep

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Congestion control plays an essential role on the internet to manage overload, which affects data transmission performance. The random early detection (RED) algorithm belongs to active queue management (AQM), which is used to manage internet traffic. The RED is used to eliminate weakness in default control of the Transport Control Protocol (TCP) drop-tail mechanism. The drawback of RED is parameter tuning, while adaptive RED (ARED) automatically adjusts these parameters. In this study, the suggested algorithm, the Markov decision process RED (MDPRED) uses the Markov decision process (MDP) to suitably adapt values for queue weight in the RED algorithm based on average queue length to enhance the performance of the traditional RED during TCP Slow Startup phase. This study is conducted based on fluctuations among the rate of service, queuing weight, and the mean queue length by using open-source network simulator NS3. The study shows efficient results by fluctuating end-to-end packet throughput and fast response to the inception of congestion in the network. The modified algorithm achieves a low level of drop packets by evaluating the results with other five algorithms, which is done by increasing the algorithm’s response when the average queue size becomes close to the maximum queue length threshold.

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confidence: 99%

Improving RED algorithm congestion control by using the Markov decision process

Mahawish

Hassan

2022

Sci Rep

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“…The proposed methods can be used on telecommunication network nodes. According to AL-Allaf and Jabbar [22], the intelligent fish swarm inspired protocol (IFSIP) routing protocol and the queuing model (M/G/1) on network nodes are proposed to minimize the global packet delay. According to Lemeshko et al [23] a reconfigurable nonlinear gentle random early dropping (RNLGRED) packet queuing scheme was proposed, designed to stabilize the queue length around a certain target value.…”

Section: Analysis Of Existing Queuing Modelsmentioning

confidence: 99%

“…In flow models, the following problem of traffic distribution is considered in [22]- [24]. There are 𝑀 classes of traffic distinguished by the type of priority 𝑁 of serviced queues.…”

Section: Analysis Of Existing Queuing Modelsmentioning

confidence: 99%

Model of optimal distribution of network resources with constraints on quality of service indicators

Ulugbek

Azamat

2023

Bulletin EEI

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Existing algorithms and mathematical models of queuing at the nodes of a telecommunications network are considered in this paper. The necessity of coordinated solutions to problems of distribution of channel and buffer resources of the network is shown. A model for the optimal distribution of channel and buffer resources on network nodes has been developed. The optimization (minimization) criterion is the total average packet delay with constraints on the quality of service (QoS) indicators of heterogeneous flows. The optimization problem is presented as a constrained nonlinear programming problem and solved using the “fmincon” program of the optimization toolbox MATLAB package.

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“…The problem with the GRED was the increased number of thresholds that needed to be selected carefully for a good result. Reconfigurable Nonlinear GRED (RNLGRED) [13] was similar to the GRED by duplicating the max th along with applying nonlinear change to either the max P or max th , depending on the avg in the router queue. The researcher in [14] incremented the queue weight when increasing the avg.…”

Section: Related Workmentioning

confidence: 99%

Improving RED algorithm congestion control by using the Markov Decision Process

Mahawish

Hassan

2022

Preprint

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Congestion control plays an essential role on the internet to manage overload, which affects data transmission performance. The random early detection (RED) algorithm belongs to active queue management (AQM), which is used to manage internet traffic. The RED is used to eliminate weakness in default control of the transport control protocol (TCP) drop-tail mechanism. The drawback of RED is parameter tuning, while adaptive RED (ARED) automatically adjusts these parameters. In this study, the suggested algorithm, the Markov decision process RED (MDPRED) uses the Markov decision process (MDP) to suitably adapt values for queue weight in the RED algorithm based on average queue length to enhance the performance of the traditional RED. This study is conducted based on fluctuations among the rate of service, the queuing weight, and the mean queue length by using open-source network simulator NS3. The study shows efficient results by fluctuating end-to-end packet throughput and fast response to the inception of congestion in the network. The modified algorithm achieves a low level of loss packets by evaluating the results with the default RED algorithm and ARED, which is done by increasing the algorithm’s response when the average queue size becomes close to the maximum queue length threshold.

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Reconfigurable Nonlinear GRED Algorithm

Cited by 3 publications

References 6 publications

Improving RED algorithm congestion control by using the Markov decision process

Improving RED algorithm congestion control by using the Markov decision process

Model of optimal distribution of network resources with constraints on quality of service indicators

Improving RED algorithm congestion control by using the Markov Decision Process

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