Congestion control

Network congestion control remains a critical issue and a high priority, especially given the growing size, demand, and speed (bandwidth) of the increasingly integrated services networks. Designing effective congestion control strategies for these networks is known to be difficult because of the complexity of the structure of the networks, nature of the services supported, and the variety of the dynamic parameters involved. In addition to these, the uncertainties involved in identification of the network parameters lead to the difficulty of obtaining realistic, cost effective, analytical models of these networks. This renders the application of classical, control system design methods (which rely on availability of these models) very hard, and possibly not cost effective. Consequently, a number of researchers are looking at alternative nonanalytical control system design and modeling schemes that have the ability to cope with these difficulties in order to devise effective, robust congestion control techniques as an alternative (or supplement) to traditional control approaches. These schemes employ artificial neural networks, fuzzy systems, and design methods based on evolutionary computation (collectively known as Computational Intelligence). In this chapter we first discuss the difficulty of the congestion-control problem and review control approaches currently in use, before we motivate the utility of Computational Intelligence based control. Then, through a number of examples, we illustrate congestion control methods based on fuzzy control, artificial neural networks, and evolutionary computation. Finally, some concluding remarks and suggestions are given for further work.