Inferring new design rules by machine learning: a case study of topological optimization

This paper presents a machine learning approach to the topological optimization of computer networks. Traditionally formulated as an integer program, this problem is well known to be a very difficult one, only solvable by means of heuristic methods. This paper addresses the specific problem of inferring new design rules that can reduce the cost of the network, or reduce the message delay below some acceptable threshold. More specifically, it extends a recent approach using a rule-based system in order to prevent the risk of combinatorial explosion and to reduce the search space of feasible network topologies. This extension essentially implements an efficient inductive learning algorithm leading to the refinement of existing rules and to the discovery of new rules from examples, defined as network topologies satisfying a given reliability constraint. The contribution of this paper is the integration of learning capabilities into topological optimization of computer networks. Computational results confirm the efficiency of the discovered rules.