Please use this identifier to cite or link to this item: https://doi.org/10.1109/JSAC.2006.258227
Title: Achieving multi-class service differentiation in WDM optical burst switching networks: A probabilistic preemptive burst segmentation scheme
Authors: Tan, C.W.
Mohan, G. 
Lui, J.C.-S.
Keywords: Optical burst switching (OBS)
Queueing theory
Wavelength-division multiplexing (WDM)
Issue Date: Dec-2006
Citation: Tan, C.W., Mohan, G., Lui, J.C.-S. (2006-12). Achieving multi-class service differentiation in WDM optical burst switching networks: A probabilistic preemptive burst segmentation scheme. IEEE Journal on Selected Areas in Communications 24 (12) : 106-119. ScholarBank@NUS Repository. https://doi.org/10.1109/JSAC.2006.258227
Abstract: We propose a Probabilistic Preemptive Burst Segmentation (PPBS) scheduling scheme to provide priority classes with different Quality-of-service (QoS) requirements in WDM Optical Burst Switching (OBS) networks. The PPBS scheme enables high priority bursts to preempt and segment low priority bursts in a probabilistic fashion. The PPBS scheme achieves 100% isolation among priority classes, and burst loss probabilities can be controlled by tunable parameters. Our PPBS queueing model also implicitly enables us to obtain several results related to the Erlang's loss function which may be useful in the role of this function arising in some areas of queueing theory, as well as characterizing a loss rate region that work conserving burst scheduling techniques can achieve. As an application to providing service differentiation, we show how the PPBS scheme can minimize the total sum of loss rates and achieve proportional loss differentiation. Finally, we also demonstrate the effectiveness of the PPBS scheme empirically using realistic Internet traffic model, e.g., long range dependent traffic model. © 2006 IEEE.
Source Title: IEEE Journal on Selected Areas in Communications
URI: http://scholarbank.nus.edu.sg/handle/10635/54873
ISSN: 07338716
DOI: 10.1109/JSAC.2006.258227
Appears in Collections:Staff Publications

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