Controlling Delay at the Router Buffer using Modified Random Early Detection

Abu-Shareha, Ahmad Adel

doi:10.5121/ijcnc.2019.11604

Cited by 16 publications

(13 citation statements)

References 24 publications

(53 reference statements)

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“…The values are enumerated to create different traffic load on the simulated network [34]. The arrival rates are chosen to be of the values [0.3, 0.5 and 0.95] and the departure of the value 0.5, which were used in recent AQM evaluations [7,8,11,35]. Three scenarios are created with such arrival and departure probabilities, light traffic, moderate and heavy traffic.…”

Section: Simulation Environmentmentioning

confidence: 99%

“…Random Early Detection (RED) has been the first method in the AQM technique and has been developed based on queue monitoring through the Average Queue Length (AQL) [5]. RED has ben then extended using various performance indicators and through variation of the dropping policies [7,8]. The goal of the proposed methods was to improve the calculation of the Dp to improve network performance.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fuzzy-Logic Based Active Queue Management Using Performance Metrics Mapping into Multi-Congestion Indicators

Abualhaj

Al-Tahrawi

Hussein

et al. 2021

Cybernetics and Information Technologies

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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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Section: Simulation Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fuzzy-Logic Based Active Queue Management Using Performance Metrics Mapping into Multi-Congestion Indicators

Abualhaj

Al-Tahrawi

Hussein

et al. 2021

Cybernetics and Information Technologies

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show abstract

“…Delay-controller random early detection "DcRED" [41] is an extension of the RED algorithm by using the delay feature while preserving the original character such as min th and max th . The modification in DcRED using a delay of a packet in the queue to calculate the drop probability.…”

Section: Aqm Algorithms Based On Queuing Length and Delaymentioning

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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“…This study proposes a new algorithm called weight queue dynamic active queue management algorithm (WQDAQM) to address the limitations of the existing ones and improve network performance. The proposed algorithm is integrated with the TCP protocol to maintain the established conversation for data exchange in high performance [22]. The specific objective of this study is to decrease the mean queue length (mql), P L , and D compared with other algorithms.…”

Section: Related Workmentioning

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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Controlling Delay at the Router Buffer using Modified Random Early Detection

Cited by 16 publications

References 24 publications

Fuzzy-Logic Based Active Queue Management Using Performance Metrics Mapping into Multi-Congestion Indicators

Fuzzy-Logic Based Active Queue Management Using Performance Metrics Mapping into Multi-Congestion Indicators

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

Weight Queue Dynamic Active Queue Management Algorithm

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