Dependability Prediction of WS-BPEL Service Compositions Using Petri Net and Time Series Models

Web services are emerging as a major technology for deploying automated interactions between distributed and heterogeneous applications. To predict dependability of composite service processes allows service users to decide whether service process meets quantitative trustworthiness requirement. Existing contributions for dependability prediction simply trust QoS information published in Service-Level-Agreement (SLA) or assume QoS of service activities to follow certain assumed distributions. These information and distributions are used as static model inputs into stochastic process models to obtain analytical results. Instead, we consider QoS of service activities to be fluctuating and introduce a dynamic framework to predict runtime dependability of service compositions built on WS-BPEL, employing the Autoregressive-Moving-Average Model (ARMA) time series model and general stochastic Petri net model. In the case study of a real-world service composition sample, a comparison between existing approaches and our one is presented and results suggest that our approach achieves higher prediction accuracy and a better curve-fitting.