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A Fast Real-Time QoS-aware Service Selection Algorithm

The mutual invocation and composition of services conduct the Internet of Services (IoS). Dynamic QoS is a prominent feature of IoS. Existing QoS-aware methods always consider the QoS as a static indicator to differentiate similar services, and attributes of QoS are independent of each other, which may lead to poor performance of service selection. In this paper, we proposed a real-time QoS-aware service selection method to find good service to match the users’ requirements, in which an FM-based dynamic evaluation model was employed to predict the real-time QoS, and then a hash space segment-based service matching algorithm is adopted to select service resources rapidly. Extensive experimental results show that mining the dynamic secondary characteristic relationship among the QoS attributes can improve the service selection effect on the real-world scene.