Performance Prediction of WS-CDL Based Service Composition

In this paper, we propose a translation-based approach for performance prediction of composite service built on WS-CDL. To translate a composite service into a state-transition model for quantitative analysis, we first give a set of translation rules to map WS-CDL elements into general-stochastic-petri-nets (GSPN). Based on the GSPN representation, we introduce the prediction algorithm to calculate the expected-process-normal-completion-time of WS-CDL processes. We also validate the accuracy of the approach in the experimental study by showing 95% confidence intervals obtained from experimental performance results cover corresponding theoretical prediction values.