Distributed expert systems for queueing network capacity planning

Queueing network capacity planning can become algorithmically intractable for moderately large networks. It is, therefore, a promising application area for expert systems. However, a survey of the published literature reveals a paucity of integrated systems combining design and optimization of network-based problems. The authors present a distributed expert system for network capacity planning, which uses Monte Carlo simulation-based optimization methodology for queueing networks. The present architecture admits parallel simulation of multiple configurations. A knowledge-based search drives the performance optimization of the network. The search process is a randomized combination of steepest descent and branch and bound algorithms, where the generating function of new states uses qualitative reasoning, and the gradient of the objective function is estimated using a heuristic score function method. The authors found a random search based on the relative order of the performance gradient components to be a powerful qualitative reasoning technique. The system is implemented as a loosely coupled expert system with components written in PROLOG, SIMSCRIPT and C. The authors demonstrate the efficacy of the present approach through an example from the domain of Jackson queueing networks.