Computational intelligence in management of ATM networks: a survey of the current state of research

Y. Ahmet Sekercioglu; Andreas Pitsillides; Athanasios V. Vasilakos

doi:10.1117/12.367704

1 November 1999 Computational intelligence in management of ATM networks: a survey of the current state of research

Y. Ahmet Sekercioglu, Andreas Pitsillides, Athanasios V. Vasilakos

Proceedings Volume 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II; (1999) https://doi.org/10.1117/12.367704
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1999, Denver, CO, United States

Abstract

Designing effective control strategies for Asynchronous Transfer Mode (ATM) networks is known to be difficult because of the complexity of the structure of networks, nature of the services supported, and variety of dynamic parameters involved. Additionally, the uncertainties involved identification of the network parameters cause analytical modeling of ATM networks to be almost impossible. This renders the application of classical control system design methods (which rely on the availability of these models) to the problem even harder. Consequently, a number of researchers are looking at alternative non-analytical control system design and modeling techniques that have the ability to cope with these difficulties to devise effective, robust ATM network management schemes. Those schemes employ artificial neural networks, fuzzy systems and design methods based on evolutionary computation. In this survey, the current state of ATM network management research employing these techniques as reported in the technical literature is summarized. The salient features of the methods employed are reviewed.

Citation Download Citation

Y. Ahmet Sekercioglu, Andreas Pitsillides, and Athanasios V. Vasilakos "Computational intelligence in management of ATM networks: a survey of the current state of research", Proc. SPIE 3812, Applications and Science of Neural Networks, Fuzzy Systems, and Evolutionary Computation II, (1 November 1999); https://doi.org/10.1117/12.367704

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