Customized Cloud Service Trustworthiness Evaluation and Comparison Using Fuzzy Neural Networks

Abstract

Cloud services have been adopted by more and more consumers and different types of businesses. An intrinsically difficult problem faced by service consumers and businesses is that no standard way to evaluate and compare cloud services' trustworthiness, while trust has already become a critical factor for service and service provider selection. In this paper, we propose a trustworthiness evaluation and comparison framework to help make customized selection decisions based on trustworthiness. This framework utilizes fuzzy processing and neural network to handle subjectivity and inaccuracy, and customized recommendation. A feedback learning mechanism is also incorporated to make the entire framework be adaptive to users' preferences. The framework is implemented as prototype system to evaluate and recommend different types of cloud services. Experiments on different types of services and different types of trustworthiness prove the effectiveness and efficiency of the proposed framework.