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A service trust evaluation model using clustering fuzzy inference for guiding network service selection
Author(s) -
Li Zhaozheng,
Lei Weimin
Publication year - 2018
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/dac.3790
Subject(s) - computer science , cluster analysis , fuzzy logic , data mining , service (business) , service provider , selection (genetic algorithm) , randomness , fuzzy clustering , inference , machine learning , artificial intelligence , statistics , economy , mathematics , economics
Summary 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.