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Allocation of Wavelength Converters in All-Optical Networks

Chapter
Part of the Network Theory and Applications book series (NETA, volume 6)

Abstract

All-optical networks deliver information in the optical domain so that the electronic bottleneck can be avoided. They can support high data rate and provide large network capacity. In this chapter, we survey the state-of-the-art technologies for all-optical networks. In particular, we focus on the problem of allocating wavelength converters in all-optical networks. We explain why an all-optical network can use wavelength converters to improve its performance. To maximize the performance, it is necessary to allocate wavelength converters to the network nodes optimally. We describe three approaches to tackle this allocation problem:

  1. 1.

    Intuitive approach: This approach applies intuitive ideas to allocate wavelength converters.

     
  2. 2.

    Analytical approach: In this approach, the network performance is derived analytically, and then optimization algorithms are designed to allocate wavelength converters based on the analytical results. This approach is very popular in the literature and it is adopted by many existing allocation methods. However, various models and assumptions have to be adopted in deriving the network performance, and the resulting allocation methods are only applicable to these specific models and assumptions.

     
  3. 3.

    Simulation-based optimization approach: In this approach, utilization statistics of wavelength converters are collected from computer simulation, and then optimization algorithms are designed to allocate wavelength converters based on the utilization statistics. This approach is widely applicable and it is not restricted to any particular model or assumption.

     

Keywords

Traffic Load Optical Network Wavelength Division Multiplex Wavelength Conversion Wavelength Assignment 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 2001

Authors and Affiliations

  1. 1.Center for Advanced Telecommunications Systems and ServiceUniversity of Texas at DallasRichardsonUSA
  2. 2.Department of Computer ScienceHong Kong Baptist UniversityHong KongChina

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