Learning to detect and avoid run-time feature interactions in intelligent networks

The Intelligent Network (IN) allows rapid changes in the services provisioned and their functionality. Services may be supplied by different service providers, making it unlikely that all service specifications will be available for examination by any single agency. Approaches to handle feature interaction problems must be able to operate within these constraints. Work by the authors has produced a generic run time feature interaction manager (FIM) concept to manage feature interactions in a live network. It monitors features as black boxes, learns their “correct” behavior and uses this to determine when feature interactions have occurred. The paper describes and compares experiences using three different techniques to realize the proposed approach. These are: state sequence monitoring, artificial neural networks (ANN), and rule based monitoring which also includes integrated generic resolution approaches. The paper explores the design alternatives with the various techniques, and reports on the results obtained from experimentation