New RED-type TCP-AQM algorithms based on beta distribution drop functions

Giménez, Ángel; Murcia, Miguel A.; Amigó, José M.; Bonastre, Oscar Martínez; Valero, José

doi:10.48550/arxiv.2201.01105

Cited by 6 publications

(5 citation statements)

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“…Weighted Queue Dynamic Active Queue Management (WQDAQM) [43] extends RED, introducing a queue weight to calculate the average queue length and dynamically adjusting the thresholds based on the traffic load. Beta Distribution Drop Functions (BetaRED) [44] extend RED using a novel queue averaging calculation. The recently proposed AC-RED (Average Queue Length Change Rate-RED) method [45] extends RED by incorporating the dynamically calculated average queue length change rate.…”

Section: The Related Workmentioning

confidence: 99%

“…The PHAQM [19] method solely utilizes delay as an indicator in nonadaptive mechanism. On the other hand, the adaptive methods, including ARED [30], DRED [31], BLUE [32], GREEN [33], SRED [34], ATRED [39], CRED [40], AAQMRD [41], LTRED [42], WQDAQM [43], BetaRED [44] AC-RED [45], and AgRED [47] do not consider queue and traffic statuses explicitly, instead, focusing on adaptively calculating or estimating certain parameters to stabilize the performance while mitigating high packet dropping in high congested networks.…”

Section: Theoretical Comparisonmentioning

confidence: 99%

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A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

Alshahrani,

Abu-Shareha,

Shambour

et al. 2023

CMC

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Active queue management (AQM) methods manage the queued packets at the router buffer, prevent buffer congestion, and stabilize the network performance. The bursty nature of the traffic passing by the network routers and the slake behavior of the existing AQM methods leads to unnecessary packet dropping. This paper proposes a fully adaptive active queue management (AAQM) method to maintain stable network performance, avoid congestion and packet loss, and eliminate unnecessary packet dropping. The proposed AAQM method is based on load and queue length indicators and uses an adaptive mechanism to adjust the dropping probability based on the buffer status. The proposed AAQM method adapts to single and multiclass traffic models. Extensive simulation results over two types of traffic showed that the proposed method achieved the best results compared to the existing methods, including Random Early Detection (RED), BLUE, Effective RED (ERED), Fuzzy RED (FRED), Fuzzy Gentle RED (FGRED), and Fuzzy BLUE (FBLUE). The proposed and compared methods achieved similar results with low or moderate traffic load. However, under high traffic load, the proposed AAQM method achieved the best rate of zero loss, similar to BLUE, compared to 0.01 for RED, 0.27 for ERED, 0.04 for FRED, 0.12 for FGRED, and 0.44 for FBLUE. For throughput, the proposed AAQM method achieved the highest rate of 0.54, surpassing the BLUE method's throughput of 0.43. For delay, the proposed AAQM method achieved the second-best delay of 28.51, while the BLUE method achieved the best delay of 13.18; however, the BLUE results are insufficient because of the low throughput. Consequently, the proposed AAQM method outperformed the compared methods with its superior throughput and acceptable delay.

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Section: The Related Workmentioning

confidence: 99%

Section: Theoretical Comparisonmentioning

confidence: 99%

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

Alshahrani,

Abu-Shareha,

Shambour

et al. 2023

CMC

View full text Add to dashboard Cite

show abstract

“…where maxQ indicates the buffer size. To tackle the instability issue of RED algorithm, Gimenez et al [37] developed the Beta RED algorithm which utilizes a (nonlinear) Beta distribution packet drop function in lieu of the linear packet drop function utilized by RED. Suwannapong and Khunboa [38] deploys two dropping functions which are linear (between minTh and maxTh) and exponential (between maxTh and K: in which K is the size of buffer).…”

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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“…in which: count indicates the number of arrived packets since the last dropped packet and pb indicates the initial packet dropping probability function. RED remains the most studied AQM [11]- [14], even till present [14]. A lot of AQM algorithms which leverages its simple model continues to evolve in literature [10].…”

Section: Introductionmentioning

confidence: 99%

“…The modified algorithm was called BetaRED. It is interesting to note that AQM algorithms that utilizes nonlinear drop functions has been reported in literature to exhibit acceptable performance especially in terms of providing better stability of the queue size [14], [29].…”

Section: Introductionmentioning

confidence: 99%

Random early detection-quadratic linear: an enhanced active queue management algorithm

Hassan

Nwaocha²,

Rufai³

et al. 2022

Bulletin EEI

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This paper identifies the lone linear drop function for computing the dropping probability between certain queue threshold values as a major weakness for the random early detection (RED) algorithm as it leads to large delay and queue instability. To address this concern, we propose an enhanced RED-based algorithm called random early detection-quadratic linear (referred to as “RED-QL”) active queue management (AQM) which leveraged the benefit of a quadratic packet drop function for a light-to moderate traffic load conditions together with a linear packet drop function for a heavy traffic load condition respectively. Results from ns-3 network simulator using different experimental scenarios clearly reveals that the proposed RED-QL algorithm yields a substantial reduction in delay performance and indeed a reduced average queue size than other three representative AQM algorithms. RED-QL is robust, easy to implement and deploy in routers (both in software and hardware) as no more than the packet drop probability profile of the classic RED’s algorithm implementation needs modest alteration.

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New RED-type TCP-AQM algorithms based on beta distribution drop functions

Cited by 6 publications

References 0 publications

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

A Fully Adaptive Active Queue Management Method for Congestion Prevention at the Router Buffer

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

Random early detection-quadratic linear: an enhanced active queue management algorithm

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