Outlier-robust web service selection based on a probabilistic QoS model

In real world, a service may be affected by various factors making service selection process a difficult task. Therefore it is important to select an appropriate service efficiently from a large number of services based on the quality of a service QoS. However, existing approaches have limitations in processing QoS data with outliers that may occur in reality. When decomposing a global QoS constraint given by a service requester into local constraints, local thresholds are commonly computed with no consideration of outliers. Therefore, an appropriate service may not be selected due to false negative errors. To address this limitation, we propose an outlier-robust Web service selection approach based on a probabilistic QoS model. Specifically, the approach prunes services which do not satisfy a global constraint due to outliers. A service is then selected based on its probability of satisfying a global constraint. Experimental results show that the proposed approach outperforms an existing solution in terms of global QoS conformance.