An adaptively emerging mechanism for context-aware service selections regulated by feedback distributions

Background: In the cloud computing environments, numerous ambient services may be created speedily and provided to a variety of users. In such a situation, people may be annoyed by how to make a proper and optimal selection quickly and economically. Methods: In this study, we propose an Adaptively Emerging Mechanism (AEM) to reduce this selection burden with an interdisciplinary approach. AEM is applied and integrated into the Flowable Service Model (FSM), which has been proposed and developed in our previous study. We consider the user’s feedback information is a pivotal factor for AEM, which contains the user’s satisfaction degree after using the services. At the same time, we assume that these factors, such as the service cost, matching result precision, responding time, personal and social context information, etc., are essential parts of the optimizing process for the selection of ambient services. Results and Conclusion: By analyzing the result of AEM simulation, we reveal that AEM can (1) substantially improve the selection process for LOW feedback users; (2) bring no negative effect on the selection process for MEDIUM or HIGH feedback users; and (3) enhance the rationality for services selection.