Collaborative Filtering (CF) is widely employed for making Web service recommendation. CF-based Web service recommendation aims to predict missing QoS (Quality-of-Service) values of Web services. Although several CF-based Web service QoS prediction methods have been proposed in recent years, the performance still needs significant improvement. Firstly, existing QoS prediction methods seldom consider personalized influence of users and services when measuring the similarity between users and between services. Secondly, Web service QoS factors, such as response time and throughput, usually depends on the locations of Web services and users. However, existing Web service QoS prediction methods seldom took this observation into consideration. In this paper, we propose a location-aware personalized CF method for Web service recommendation. The proposed method leverages both locations of users and Web services when selecting similar neighbors for the target user or service. The method also includes an enhanced similarity measurement for users and Web services, by taking into account the personalized influence of them. To evaluate the performance of our proposed method, we conduct a set of comprehensive experiments using a real-world Web service dataset. The experimental results indicate that our approach improves the QoS prediction accuracy and computational efficiency significantly, compared to previous CF-based methods.

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Cloud manufacturing yields insights of manufacturing services over cyberspace based on the integration of advanced manufacturing with cloud computing. However, the different communication standards between the different system levels are the main challenge for the integration without conflicting communication. Ethernet appears to be the best solution to support all levels for industrial manufacturing systems. Since the synchronous integration is fulfilled, the prerequisite for deciding the overall performance of systems is adaptable real‐time communication. This motivates us to study the problem of designing a new real‐time communication based on Ethernet standards to enable real‐time processing for factory networks, termed real‐time Ethernet (RTEthernet). In this paper, we introduce new architectures for master and controller stations. The objective is to have the maximum number of machines transmitting data in a data transmission window (coordinated cycle). The problem is called the maximum number of real‐time data transmission (MNRDT) problem. The MNRDT is shown to be NP‐hard. An approximation and three relevant solutions are proposed for addressing the reduction of MNRDT and the MNRDT problem. Simulation results are provided to show the performance of proposed solutions.