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Despite the rapid advances in mobile technology,many constraints still prevent mobile devices from running resource-demanding applications in mobile environments.Cloud computing with flexibility,stability and scalability enables access to unlimited resources for mobile devices,so more studies have focused on cloud computing-based mobile services.Due to the stability of wireless networks,changes of Quality of Service(QoS) level and user’ real-time preferences,it is becoming challenging to determine how to adaptively choose the“appropriate”service in mobile cloud computing environments.In this paper,we present an adaptive service selection method.This method first extracts user preferences from a service’s evaluation and calculates the similarity of the service with the weighted Euclidean distance.Then,they are combined with user context data and the most suitable service is recommended to the user.In addition,we apply the fuzzy cognitive maps-based model to the adaptive policy,which improves the efficiency and performance of the algorithm.Finally,the experiment and simulation demonstrate that our approach is effective.
Despite the rapid advances in mobile technology, many constraints still prevent mobile devices from running resource-demanding applications in mobile environments .Cloud computing with flexibility, stability and scalability allows access to unlimited resources for mobile devices, so more studies have focused on cloud computing- based mobile services. D to the stability of wireless networks, changes of Quality of Service (QoS) level and user ’real-time preferences, it is becoming challenging to determine how to adaptively choose the “appropriate ” service in mobile cloud computing environments.In this paper, we present an adaptive service selection method. This method first extracts user preferences from a service’s evaluation and calculates the similarity of the service with the weighted Euclidean distance.Then, they are combined with user context data and the most suitable service is recommended to the user. In addition, we apply the fuzzy cognitive maps-based model to the adaptive policy, which i mproves the efficiency and performance of the algorithm. Finaally, the experiment and simulation demonstrate that our approach is effective.