Dynamic Web Service Discovery Model Based on Artificial Neural Network with QoS Support

The Universal Description, Discovery and Integration (UDDI) registries do not have the ability to publish the QoS information, and the authenticity of the advertised QoS information available elsewhere may be questionable. We aim to refine the discovery process through designing a new framework that enhances retrieval algorithms by combining syntactic and semantic matching of services with QoS. We propose a model of Artif icial Neural Netw ork (ANN) w ith Quality of Services (QoS) based Web services discovery that combines an ANN based intelligent search and an augmented UDDI registry to publish the QoS information and a reputation manager to assign reputation scores to the services based on customer feedback of their performance. We develop a service matching, ranking and selection algorithm that f inds a set of services that match the consumer’s requirements, ranks these services using their QoS information and reputation scores, and f inally returns the web service consumer based on the consumer’s preferences in the service discovery request. Finally the web service discovery with QoS gives the most cost effective and suitable services as an output. The effectiveness of the system is improved by means of Artif icial Neural Netw ork w ith QoS.