Modeling discrete-time analytical models based on random early detection: Exponential and linear

Abdel-Jaber, Hussein; Thabtah, Fadi; Woodward, M.E.

doi:10.1142/s1793962315500282

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Two discrete-time queue analytical models based on RED, called RED-Exponential and RED-Linear, were aimed at improving RED's issue in cases of heavy traffic were presented by [29]. ese two analytical models dealt with this issue by using instantaneous queue length instead of average queue length as a congestion measure.…”

Section: Journal Of Computer Network and Communicationsmentioning

confidence: 99%

An Exponential Active Queue Management Method Based on Random Early Detection

Abdel-Jaber

2020

Journal of Computer Networks and Communications

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Congestion is a key topic in computer networks that has been studied extensively by scholars due to its direct impact on a network’s performance. One of the extensively investigated congestion control techniques is random early detection (RED). To sustain RED’s performance to obtain the desired results, scholars usually tune the input parameters, especially the maximum packet dropping probability, into specific value(s). Unfortunately, setting up this parameter into these values leads to good, yet biased, performance results. In this paper, the RED-Exponential Technique (RED_E) is proposed to deal with this issue by dropping arriving packets in an exponential manner without utilizing the maximum packet dropping probability. Simulation tests aiming to contrast E_RED with other Active Queue Management (AQM) methods were conducted using different evaluation performance metrics including mean queue length (mql), throughput (T), average queuing delay (D), overflow packet loss probability (PL), and packet dropping probability (DP). The reported results showed that E_RED offered a marginally higher satisfactory performance with reference to mql and D than that found in common AQM methods in cases of heavy congestion. Moreover, RED_E compares well with the considered AQM methods with reference to the above evaluation performance measures using minimum threshold position (min threshold) at a router buffer.

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Section: Journal Of Computer Network and Communicationsmentioning

confidence: 99%

An Exponential Active Queue Management Method Based on Random Early Detection

Abdel-Jaber

2020

Journal of Computer Networks and Communications

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“…In this approach, the simulation period is divided into time units called slot. In each slot, multiple events could be occurred (Abdel-Jaber et al, 2015;Zhou and Wang, 2008;Guan et al, 2004c). The probability of packet arrival in BLUE-BP and GB-BP, is denoted by α0, while the packet departure probability in a slot is called by β.…”

Section: Simulationmentioning

confidence: 99%

Markov-Modulated Bernoulli-Based Performance Analysis for Gentle BLUE and BLUE Algorithms under Bursty and Correlated Traffic

Alsaaidah¹,

Zalisham²,

Fadzli³

et al. 2016

Journal of Computer Science

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This paper presents a performance study for Gentle BLUE (GB) under the bursty and correlated properties of aggregated network traffic. The Bernoulli Process (BP) fails to represent the properties of aggregated correlated and bursty traffic, so instead of that, MMBP has been used. MMBP is A 2D discrete-time Markov chain modeling for GB algorithm with two traffic classes, each with its own parameters. The proposed model is compared with the GB that uses the BP as a source model (GB-BP) and original BLUE that uses the BP (BLUE-BP) and MMBP (BLUE-MMBP-2) as source model. The evaluation is conducted in term of queuing waiting time, mean queue length, throughput, packet loss and dropping probability. When congestion (e.g., heavy congestion) occurs, the results show that GB-MMBP-2 provides the bestmean queue length, queuing time and packet loss among the compared methods.

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“…Using the approach of discrete-time queuing, the scholars in [31] proposed two analytical models i) RED-Exponential and ii) RED-linear. These two models are based on RED, however, the instantaneous queue length was employed for congestion measure.…”

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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Modeling discrete-time analytical models based on random early detection: Exponential and linear

Cited by 5 publications

References 12 publications

An Exponential Active Queue Management Method Based on Random Early Detection

An Exponential Active Queue Management Method Based on Random Early Detection

Markov-Modulated Bernoulli-Based Performance Analysis for Gentle BLUE and BLUE Algorithms under Bursty and Correlated Traffic

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

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