A service trust evaluation model using clustering fuzzy inference for guiding network service selection

The evolution of distributed and virtualized network services makes service selection difficult, as service providers and their links are becoming open and random. The trust degree of service providers is considered as an effective guidance, but it is unmethodical to establish and maintain a clear and stable trust relationship between them. Traditional solutions of service trust evaluation are not comprehensive and accurate enough, because they generally do not take randomness and fuzziness into account. In this context, a model of service trust evaluation based on clustering fuzzy inference for guiding network service selection is proposed in this paper. Four clustering evaluation indexes are determined, and an evaluation mechanism is established based on the fuzzy membership function. The valuation process is time-aware, and the fuzzy knowledge base can be iteratively updated to keep the trust degree fresh. Simulation experiments illustrate the feasibility of the proposed model and indicate the superiority of greater performance compared with other similar solutions.