Abstract
Quality of Service (QoS) has been widely used to support dynamic Web Service (WS) selection and composition. Due to the volatile nature of QoS parameters, QoS prediction has been put forward to understand the trend of QoS data volatility and estimate QoS values in dynamic environments. In order to provide adaptive and effective QoS prediction, we propose a WS QoS prediction approach, named WS-QoSP, based on the technique of forecast combination. Different from the existing QoS prediction approaches that choose a most feasible forecasting model and predict relying on this “best” model, WS-QoSP selects multiple potential forecasting models, combines the results of the selected models to optimize the overall forecast accuracy. Results of real data experiments demonstrate the diversified forecast accuracy gains by using WS-QoSP.
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