Efficient utilization of cell switched networks supporting diverse applications will require service disciplines that are well designed for the particular quality of service constraints and traffic mix, a difficult task in view of the paucity of information about the expected traffic. We demonstrate the use of on-line dynamic programming in an adaptive cell scheduling mechanism that can easily be engineered to meet arbitrary quality of service constraints. When the objective is to minimize the total cell loss rate, our algorithm, urgency scheduling, compares favorably with the optimal earliest deadline first algorithm. For more complex quality of service constraints where optimal scheduling algorithms are unavailable, the simulations show urgency scheduling can provide significant increases in the usable bandwidth of a link. The learning techniques we develop are quite general and should be readily applicable to other network control problems.

Exhaustive exploration of an ensemble of networks is used to model learning and generalization in layered neural networks. A simple Boolean learning problem involving networks with binary weights is numerically solved to obtain the entropy Sm and the average generalization ability Gm as a function of the size m of the training set. Learning curves Gm vs m are shown to depend solely on the distribution of generalization abilities over the ensemble of networks. Such distribution is determined prior to learning, and provides a novel theoretical tool for the prediction of network performance on a specific task.