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The First International Conference on Smart Systems, Devices and Technologies

SMART 2012

May 27 - June 1, 2012 - Stuttgart, Germany


Passive Optical Network Configurations - Performance Analysis
co-authored by John Vardakas and Michael Logothetis, University of Patras, Greece

The Passive Optical Network (PON) is a fiber-based access network that provides huge bandwidth in a cost-effective manner. The basic building blocks of a PON are a centralized Optical Line Terminal (OLT), located in the central office, and a number of Optical Network Units (ONUs) located at the users’ premises. A Passive Optical Splitter/Combiner (PO-SC) broadcasts traffic from the OLT to the ONUs (downstream direction) and transmits traffic from the ONUs to the OLT (upstream direction). PONs come in different flavors, depending on the multiple access scheme they deploy in both directions, such as Time Division Multiple Access (TDMA), Wavelength Division Multiplexing (WDM) and Optical Code Division Multiple Access (OCDMA).

PONs are now deployed as the primary solution for the provision of Fiber-To-The-Home (FTTH) services, by utilizing their ability to share the fiber bandwidth among customers in cost-gainful way. However, the ever-increasing bandwidth demands, driven by the emergence of bandwidth-hungry applications, enhance the need for PON configurations that will fully utilize the advantages of the optical fiber. Therefore, it is important for  the service providers to discover the capabilities of the PON and predict its performance under different subscriber demands. The performance analysis of  PONs through the development of efficient mathematical models can provide numerous advantages. Firstly, contrary to the method of simulation, the mathematical models provide a concrete way for the determination of crucial performance metrics, such as blocking probabilities, delay, jitter and utilization of the network’s resources. The calculation of these metrics can be performed in relatively very short time, in comparison to the time-consuming simulations, which are typically performed by using troublesome and expensive simulation tools. Furthermore, mathematical models are a resourceful tool that could be used by service providers in order to answer questions involving trade-offs between the amount of resources allocated for a specific service-class and the QoS that will be experienced by the subscribers, and to predict network performance under extreme traffic conditions. In addition, the development of analytical models for the performance evaluation of networks is the first step for the derivation of network optimization models that aims at minimizing the network operational cost, while maintaining the QoS experienced by the subscribers above the Service Level Agreements (SLA) levels. As a result, the development of mathematical models for the performance analysis of PONs can provide the best possible network resources with the highest QoS to the network subscribers.



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