Analytical Models Based Discrete-Time Queueing for the Congested Network

Al‐diabat, Mofleh; Abdel-Jaber, Hussein; Thabtah, Fadi; Abou-Rabia, Osman; Kishta, Mahmoud

doi:10.1142/s1793962311500048

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Only a single event allowed for the packet at any time (the packet may be arrive or departure). While in continuous approach, at contrary, multiple events could be occurred for the packet at a single time slot (the packet may arrive and/or departure) indiscrete time queue (Al-Diabat et al, 2012;Abdel-Jaber et al, 2007a).…”

Section: Related Workmentioning

confidence: 99%

“…Step one (initialize the parameters setting): GB-MMBP-2 required settings of many parameters as seen in Table 2. GB usesa threshold value as 60% of the buffer size as those in (Saaidah et al, 2014;Abdel-Jaber et al, 2007a;Al-Diabat et al, 2012). Initially, the queue length is empty, which means the initial value of queue length is zero and the packet will be arrived initially to state 1, thus the initial state is state 1.…”

Section: Fig 2 Gb Using Mmbp-2mentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Related Workmentioning

confidence: 99%

Section: Fig 2 Gb Using Mmbp-2mentioning

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

Self Cite

View full text Add to dashboard Cite

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“…Some methods aim to ease the parameter settings, especially fuzzy systems [7,[12][13][14][15]. Overall, the advanced AQM methods addressed the problems of reducing dropping [5,16,17], delay [18,19], loss [15,20], and easing the parameter settings [21,22], which are the main keys to achieve the desired performance of the AQM methods. Yet, one of the main significant issues is to keep the calculation at the router buffer as simple as possible to avoid high computational load and unnecessary delay at the router [23], which is not the case with complex AQM methods, especially the fuzzy-based methods [2,4,[24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Linear Random Early Detection for Congestion Control at the Router Buffer

Abu-Shareha¹,

Al-Kasasbeh²,

Shambour³

et al. 2022

IJCAI

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Active Queue Management (AQM) methods control the router's buffer to maintain high network performance and control congestion at the router buffer. Random Early Detection (RED) method is the most well-known and the most utilized AQM. RED suffers from a high dropping rate, which motivates the later AQM methods to use more complex processes, which reach the limits of using fuzzy systems as a processing technique. Yet, high computational cost affects the router's performance specifically and the network as a whole, with so-called processing delay. In this paper, a linear version of RED (LRED) is presented to reduce the computational cost of the original RED and maintain the network performance in terms of throughput, delay, dropping, and loss. LRED is built based on two distinctive features, simplifying the congestion indicator calculation and reducing the operations in calculating the dropping probability. The experimental results showed that the proposed method reduces the delay and the processing time while maintaining the throughput and loss of the RED method. Povzetek: Opisan je razvoj nove metode za upravljanje predpomnilnika na omrežju.

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“…Other congestion control techniques have been developed as analytical models to deal with QoS issues [1,2]. For example, in [15][16][17][18], the authors proposed discrete-time queue analytical models aimed at early control of congestion. Researchers [1,19,20] also developed analytical models to solve problems related to the delay in the computer network.…”

Section: Introductionmentioning

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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Analytical Models Based Discrete-Time Queueing for the Congested Network

Cited by 7 publications

References 19 publications

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

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

Linear Random Early Detection for Congestion Control at the Router Buffer

An Exponential Active Queue Management Method Based on Random Early Detection

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