engleski

Real-time adaptive QoS prediction using approximate matrix multiplication

We introduce a novel QoS prediction model as a real-time support for the selection of atomic service candidates based on their QoS properties while constructing composite applications. The proposed approach satisfies the following requirements: (i) fast and accurate prediction of QoS values and (ii) adaptability with respect to environment changes. The model precomputes the similarities between users and services using approximate matrix multiplication to reduce the time complexity. When calculating a prediction for a user–service pair, the model considers similar users and services, but enhances the prediction accuracy by incorporating the number of observed records. Time complexity is further reduced by storing the lists of similar users and services which are updated in real-time. The model adapts to the changing environment: newer records are set to have greater influence on the predictions. The experiments conducted on relevant service-oriented datasets show advantages of the proposed model in accuracy and time performance.

approximate matrix multiplication ; QoS prediction ; quality of service ; real-time adaptability ; service recommendation ; web services

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