Computational intelligence in management of ATM networks

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 in 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.