Neural networks for distributed overload control in telecommunications networks

Overload control in telecom networks is used to protect the network of call processing computers from excessive load during traffic peaks, and involves techniques of predictive control with limited local information. We propose a neural network algorithm, in which a group of neural controllers are trained using examples generated by a globally optimal control method. Simulations show that the neural controllers have better performance than local control algorithms in both the throughput and the response to traffic upsurges. Compared with the centralized control algorithm, the neural control significantly decreases the computation time for making decisions and can be implemented in real time.