A Discrete-time Queue Analytical Model based on Dynamic Random Early Drop

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

doi:10.1109/itng.2007.3

Cited by 10 publications

(15 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…3,11,12,14 The results from the analysis and modeling of queueing network systems are analytical models including their derived performance measures (mean queue length, throughput, average queueing delay, packet loss probability, etc.). 2,[9][10][11]16 These analytical models can be used as congestion control methods such as DRED models, 24,25,27,29 BLUE models, 20,26 and others. 23,30,31 For instance, the authors in Ref.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Analytical Models Based Discrete-Time Queueing for the Congested Network

Al‐diabat

Abdel-Jaber

Thabtah

et al. 2012

Int. J. Model. Simul. Sci. Comput.

Self Cite

View full text Add to dashboard Cite

Congestion is one of the well-studied problems in computer networks, which occurs when the request for network resources exceeds the buffer capacity. Many active queue management techniques such as BLUE and RED have been proposed in the literature to control congestions in early stages. In this paper, we propose two discrete-time queueing network analytical models to drop the arrival packets in preliminary stages when the network becomes congested. The first model is based on Lambda Decreasing and it drops packets from a probability value to another higher value according to the buffer length. Whereas the second proposed model drops packets linearly based on the current queue length. We compare the performance of both our models with the original BLUE in order to decide which of these methods offers better quality of service. The comparison is done in terms of packet dropping probability, average queue length, throughput ratio, average queueing delay, and packet loss rate.

show abstract

Section: Related Workmentioning

confidence: 99%

“…28 Moreover, several discrete-time queue analytical models based on different AQM methods were developed. 19,20,[24][25][26][27]29 The authors of Ref. 29 proposed an analytical model based on DRED method 11 in order to identify the congestion incipiently at networks router buffer.…”

Section: Related Workmentioning

confidence: 99%

Analytical Models Based Discrete-Time Queueing for the Congested Network

Al‐diabat

Abdel-Jaber

Thabtah

et al. 2012

Int. J. Model. Simul. Sci. Comput.

Self Cite

View full text Add to dashboard Cite

show abstract

“…With QoS requirements in mind, a number of Active Queue Management (AQM) schemes have been proposed, such as Random Early Detection (RED) [11], Gentle Random Early Detection (GRED) [6], Dynamic Random Early Detection (DRED) [1], Random Early Marking (REM) [3,19], and Loss and Queuing Delay Controller (LQD) [20]. Most of the schemes provide queue management using different mechanisms for enqueuing and dropping packets.…”

Section: Introductionmentioning

confidence: 99%

“…Apart from the algorithmic approaches described above, mathematical models have also been used widely to solve and analyze related QoS problems [1,10,4,12,15,9,13,22,21]. For instance, a Markov chain modeling process is popularly used, to solve network congestion in [1], delay in [10,4,12] and packet loss in [15,9].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

QoS enhancements and performance analysis for delay sensitive applications

Wang

Guan

Wang

et al. 2011

Journal of Computer and System Sciences

View full text Add to dashboard Cite

This paper presents a comprehensive system modeling and analysis approach for both predicting queuing delay and controlling average queuing delay of a single buffer to a required value in a multiple traffic source network environment. This approach could effectively enhance the QoS performance of delay sensitive applications. A discrete-time analytical model that approximates the multi-source arrival process with a binomial distribution has been developed to analyze the relationship between the queuing threshold and average queuing delay. A control strategy with dynamic queue thresholds based on the analytical result is then used to control the average queuing delay to a required value within the buffer. Packet dropping is treated as implicit congestion feedback to the arrival process for rate adjustment. The feasibility of the system has been validated by comparing theoretical analysis with a diverse set of simulation results. Following from the simulation results, a set of statistical analyses has been performed to evaluate the efficiency and accuracy of the proposed scheme. In addition, a user-friendly graphical user interface has been developed to allow user-configuration of the simulation process and display simulation results.

show abstract