Automated determination of sensitivity classes of web services

We presents a method to calculate the sensitivity, which is a benchmark for web services performance. We introduce metrics which characterize sensitivity, describe the routine for their evaluation, and suggest the method of class definition for an arbitrary web service. The method uses several machine learning algorithms such as clustering via $k$-means and classification via support vector machines. At the end we present the results of a numerical experiment, which illustrates our method.