Stabilizing Average Queue Length in Active Queue Management Method

Baklizi, Mahmoud

doi:10.14569/ijacsa.2019.0100310

Cited by 6 publications

(7 citation statements)

References 10 publications

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“…The DGRED depends on the three-state markov modulated bernoulli arrival process (MMBP-3) to support three types of the traffic flow by performing correlation among these network traffics. An extension of DGRED is stabilized dynamic GRED "SDGRED" [21], [22] to provide dynamically stabilizing the avg between min th and max th . This dynamic algorithm uses dynamic increase or decrease of max th and doublemax th value by stabilizing the avg value around min th to adjust the calculation of dropping probability.…”

Section: Figure 3 Red Algorithmmentioning

confidence: 99%

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

Mahawish

Hassan

2021

IJEECS

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The congestion on the internet is the main issue that affects the performance of transition data over the network. An algorithm for congestion control is required to keep any network efficient and reliable for transfer traffic data of the users. Many Algorithms had been suggested over the years to improve the control of congestion that occurs in the network such as drop tail packets. Recently there are many algorithms have been developed to overcome the drawback of the drop tail procedure. One of the important algorithms developed is active queue management (AQM) that provides efficient congestion control by reducing drop packets, this technique considered as a base for many other congestion control algorithms schema. It works at the network core (router) for controlling the drop and marking of packets in the router's buffer before the congestion inception. In this study, a comprehensive survey is done on the AQM Algorithm schemas that proposed and modification these algorithms to achieve the best performance, the classification of AQM algorithms based on queue length, queue delay, or both. The advantages and limitations of each algorithm have been discussed. Also, debate the intelligent techniques procedure with AQM algorithm to achieve optimization in performance of algorithm operation. Finally, the comparison has been discussed among algorithms to find the weakness and powerful of each one based on different metrics.

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Section: Figure 3 Red Algorithmmentioning

confidence: 99%

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

Mahawish

Hassan

2021

IJEECS

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“…Table 1 compares the existing AQM algorithms and highlights the gaps that will be covered in this paper. Unlike in DGRED, all thresholds in the SDGRED algorithm change following the status of aql at the router buffer [16]. Moreover, SDGRED eliminates Taql to decrease the parameterization in the router buffer.…”

Section: Related Workmentioning

confidence: 99%

“…To face such a challenge, many active queue management (AQM) algorithms were developed and improved to detect congestion at an early stage and improve network performance. Several examples include the enhanced adaptive gentle random early detection (GRED) [15], stabilized dynamic GRED (SDGRED) [16], Markov-modulated queuing systems [17], FL Intelligent Traffic Management [18], fuzzy logic approach for congestion control [19], and dynamic GRED (DGRED) [20], Markov-modulated [17], FLACC [19], and dynamic stochastic early discovery [20]. These algorithms, although they have great influence in easing the congestion, suffer from some limitations.…”

Section: Introductionmentioning

confidence: 99%

Weight Queue Dynamic Active Queue Management Algorithm

Baklizi

2020

Symmetry

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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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“…The algorithm aimed at stabilizing ave at a computed specified value between minTh and maxTh while dynamically adjusting the queue thresholds: maxTh and two times maxTh in order to provide an improved performance in terms of packet loss rate. Baklizi et al [29] suggested the stabilized DGRED (SDGRED) algorithm, a modified version of DGRED whereby the maxTh and two times maxTh queue thresholds were dynamically adjusted and aimed at stabilizing ave at a computed specified value between minTh and two times maxTh queue thresholds in order to obtain a reduced packet loss rate and queuing delay. Weight queue dynamic AQM (WQDAQM) algorithm [30] is an improvement over SDGRED algorithm.…”

Section: Introductionmentioning

confidence: 99%

Quadratic exponential random early detection: a new enhanced random early detection-oriented congestion control algorithm for routers

Hassan

Rufai

Nwaocha

et al. 2023

IJECE

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<span>Network congestion is still a problem on the internet. The random early detection (RED) algorithm being the most notable and widely implemented congestion algorithm in routers faces the problems of queue instability and large delay arising from the presence of an ineffectual singular linear packet dropping function. This research article presents a refinement to RED, named quadratic exponential random early detection (QERED) algorithm, which exploits the advantages of two drop functions, namely quadratic and exponential in order to enhance the performance of RED algorithm. ns-3 simulation studies using various traffic load conditions to assess and benchmark the effectiveness of QERED with two improved variants of RED affirmed that QERED offers a better performance in terms of average queue size and delay metrics at various network scenarios. Fortunately, to replace/upgrade the implementation for RED algorithm with QERED’s in routers will require minimal effort due to the fact that nothing more besides the packet dropping probability profile got to be adjusted.</span>

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Stabilizing Average Queue Length in Active Queue Management Method

Cited by 6 publications

References 10 publications

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

Survey on: A variety of AQM algorithm schemas and intelligent techniques developed for congestion control

Weight Queue Dynamic Active Queue Management Algorithm

Quadratic exponential random early detection: a new enhanced random early detection-oriented congestion control algorithm for routers

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