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

dc.contributor.authorAbdel-Jaber, Hussein
dc.contributor.authorThabtah, Fadi
dc.contributor.authorWoodward, Mike
dc.date.accessioned2020-02-13T09:11:18Z
dc.date.available2020-02-13T09:11:18Z
dc.date.copyright2015en_US
dc.date.issued2015
dc.descriptionThis article is not available at CUD collection. The version of scholarly record of this Article is published in International Journal of Modeling, Simulation, and Scientific Computing (2015), available online at: https://doi.org/10.1142/S1793962315500282en_US
dc.description.abstractCongestion 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. © 2015 World Scientific Publishing Company.en_US
dc.identifier.citationAbdel-Jaber, H., Thabtah, F., & Woodward, M. (2015). Modeling discrete-time analytical models based on random early detection: Exponential and linear. International Journal of Modeling, Simulation, and Scientific Computing, 6(3). https://doi.org/10.1142/S1793962315500282en_US
dc.identifier.issn17939623
dc.identifier.urihttp://dx.doi.org/10.1142/S1793962315500282
dc.identifier.urihttp://hdl.handle.net/20.500.12519/138
dc.language.isoenen_US
dc.publisherWorld Scientific Publishing Co. Pte Ltden_US
dc.relationAuthors Affiliations: Abdel-Jaber, H., Department of Information Technology and Computing, Faculty of Computer Studies, Arab Open University, Saudi Arabia; Thabtah, F., Ebusiness Department, Canadian University of Dubai, United Arab Emirates; Woodward, M., Department of Computing, University of Bradford, Bradford, BD7 1DP, United Kingdom
dc.relation.ispartofseriesInternational Journal of Modeling, Simulation, and Scientific Computing;Vol. 6, no. 3
dc.rightsPermission to reuse abstract has been secured from World Scientific Publishing Co. Pte Ltd.
dc.rights.holderCopyright : 2015 World Scientific Publishing Company
dc.subjectCongestion control (communication)en_US
dc.subjectQueueing networksen_US
dc.subjectQueueing theoryen_US
dc.subjectAverage queue lengthsen_US
dc.subjectBetter performanceen_US
dc.subjectCongestion measureen_US
dc.subjectDiscrete time queuesen_US
dc.subjectLinear reductionen_US
dc.subjectMean queue lengthsen_US
dc.subjectQueueing delaysen_US
dc.subjectRandom Early Detectionsen_US
dc.subjectAnalytical modelsen_US
dc.titleModeling discrete-time analytical models based on random early detection : exponential and linearen_US
dc.typeArticleen_US

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