Skyline Service Selection Based on QoS Prediction
Yan Guo, Shangguang Wang · 2016
In this paper, we propose a Skyline service selection approach based on QoS prediction. We first consider the QoS history records as time series and predict the QoS values by using Autoregressive Integrated Moving Average (ARIMA) model to provide more accurate QoS attributes values. And then we calculate the uncertainty of the prediction result by adopting an improved Coefficient of Variation. In order to downsize the search space, we employ Skyline computing to prune redundant services and then perform Skyline service selection by using Mixed Integer Programming. Extensive experimental results show that our approach has a better performance than other approaches.