Derivation of Three Queue Nodes Discrete-Time Analytical Model Based on DRED Algorithm

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

Woodward

2015

Self Cite

Congestion control is among primary topics in computer network in which random early detection (RED) method is one of its common techniques. Nevertheless, RED suffers from drawbacks in particular when its "average queue length" is set below the buffer's "minimum threshold" position which makes the router buffer quickly overflow. To deal with this issue, this paper proposes two discrete-time queue analytical models that aim to utilize an instant queue length parameter as a congestion measure. This assigns mean queue length (mql) and average queueing delay smaller values than those for RED and eventually reduces buffers overflow. A comparison between RED and the proposed analytical models was conducted to identify the model that offers better performance. The proposed models outperform the classic RED in regards to mql and average queueing delay measures when congestion exists. This work also compares one of the proposed models (RED-Linear) with another analytical model named threshold-based linear reduction of arrival rate (TLRAR). The results of the mql, average queueing delay and the probability of packet loss for TLRAR are deteriorated when heavy congestion occurs, whereas, the results of our RED-Linear were not impacted and this shows superiority of our model.

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Section: Related Workmentioning

confidence: 99%

“…Another way of controlling congestion is to analyze and model queueing network systems based on AQM techniques [11][12][13][14][15][16] such as using discrete-time queues. 17 A discrete-time queue uses a time unit called a slot.…”

Section: Introductionmentioning

confidence: 99%

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

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

Woodward

2015

Self Cite

show abstract

“…Discrete-time queueing approach can be employed to analyze and model different queueing network systems 19,20,[24][25][26][27][28] based on the AQM methods. 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.).…”

Section: Related Workmentioning

confidence: 99%

“…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

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

et al. 2012

Self 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.

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Performance Analysis of the Proposed Adaptive Gentle Random Early Detection Method under NonCongestion and Congestion Situations

Ababneh

Communications in Computer and Information Science

et al. 2011