A Dynamic Model to enhance the Distributed Discovery of services in P2P Overlay Networks

Abstract

In Service Computing (SC), online Semantic Web services (SWs) is evolving over time and the increasing number of SWs with the same function on the Internet, a great amount of candidate services emerge. So, efficiency and effectiveness has become a stern challenge for distributed discovery to tackle uniformed behavior evolution of service and maintain high efficiency for large-scale computing. The distributed discovery of SWs according to their functionality increases the capability of an application to fulfill their own goals. In this paper, we describe an efficient and an effective approach for improving the performance and effectiveness of distributed discovery of SWs in P2P systems. As most Web services lack a rich semantic description, we extend the distributed discovery process by exploiting collaborative ranking to estimate the similarity of a SWs being used by existing hybrid matching technique of OWL-S (Ontology Web Language for Services) process models in order to reduce costs and execution time. We mapped our distributed discovery of OWL-S process models by developing a real application based on Gamma Distribution; a technique used to decrease the bandwidth consumption and to enhance the scalability of P2P systems. The particularity of the Gamma Distribution is then integrated for disseminating request about the P2P networks to perform quality based ranking so that the best SWs can be recommended first. The experimental result indicates that our approach is efficient and able to reduce considerably the execution time and the number of message overhead, while preserving high levels of the distributed discovery of SWs on large-size P2P networks.