The internet currently hosts a large number of web services with highly volatile quality of service (QoS), which makes it difficult for users to quickly access highly reliable online services. Hence, the selection of the optimal service composition based on fast and reliable QoS has emerged as a challenging and popular problem in the field of service computing. In this paper, we propose a service selection approach based on QoS prediction. We consider historical QoS information as time series and predict QoS values using the autoregressive integrated moving average model, which can provide more accurate QoS attribute values. We then calculate the uncertainty in the prediction results using an improved coefficient of variation to prune redundant services. In order to downsize the search space, we employ Skyline computing to prune redundant services and perform Skyline service selection using 0-1 mixed-integer programming. Experimental results based on real-world dataset showed that our approach yields satisfactory performance in terms of reliability and efficiency.
- Autoregressive integrated moving average model
- QoS prediction
- Service selection
- Skyline service
ASJC Scopus subject areas
- Computer Networks and Communications