QCI-WSC: Estimation and prediction of QoS confidence interval for web service composition based on Bootstrap

Abstract In web service composition, the Quality of Service (QoS) prediction applications based on the statistical point estimation method in accuracy consist of many challenges. Aiming at allowing users to select the web service composition based on their requirements, this study proposed a method based on Bootstrap to estimate and predict the QoS confidence interval for web service composition (QCI-WSC). The QCI-WSC first indicates the structure of the web service composition and simplifies the structure model. Apart from that, the QoS estimation interval can be calculated by the historical QoS data, which are invoked by users. Meanwhile, the user similarity is calculated, and the QoS of web service invoked by the similar users is used to predict QCI-WSC. Finally, the results of user-invoked web service composition QoS are verified by the average interval coverage rate, compared to the actual QoS values and prediction values of the other methods, such as adaptive QoS prediction method based on collaborative filtering (QACF) and QoS-Aware web service recommendation (WSRec). Additionally, in this work, dataset1 in WSDream is adopted to estimate and predict the QCI-WSC. Experiments show that the QoS confidence interval estimation results conform to the exponential distribution, and the validity of the QCI-WSC is proved. Furthermore, the average interval probability of the prediction algorithm was more than 75%. The QCI-WSC can accurately cover the actual QoS values of the web service composition and most of the accurate QoS values predicted by QACF and WSRec. It effectively improves the selectivity of service, which provides web service composition featuring better quality for users.