Self-scaling cooperative discovery of service compositions in unstructured P2P networks

We propose an efficient technique for improving the performance of automatic and cooperative compositions in unstructured Peer-to-Peer networks during service discovery. The technique exploits a probabilistic forwarding algorithm that uses different sources of knowledge, such as network density and service grouping, to reduce the amount of messages exchanged in the network. The technique, analysed in several network configurations by using a simulator to observe resolution time, recall and message overhead, presents good performance especially in dense and large-scale service networks.To further improve performance and effectiveness of service discovery, we propose a bidirectional search strategy for distributed service composition. It enables concurrent searches over the Peer-to-Peer network exploring the service space in two search directions, hence reducing the response time when solutions are present; in case the requests for a service cannot be completely satisfied, discovered partial solutions may be analysed to identify service gaps that suggest future service implementations and consequently new opportunities for service providers. This technique further reduces the time for discovering compositions, highlighting only a limited increment, when compared with the unidirectional search, of the number of messages exchanged.