A Novel Performance Prediction Framework for Web Service Workflow Applications

Web Service play an important role in the Service-oriented Architecture (SOA), which is a new paradigm to implementing dynamic e-business solution, as Web services can be composed in an orchestrated manner by using Business Process Execution Language (BPEL). In this context, the performance of a Web service workflow is a very important factor for Business Process Re-engineering (BPR). A framework for the performance prediction and analysis of service-based applications from users' perspectives was present in this paper. A historical time series for a specific performance is evaluated first in the framework. And then Particle Swarm Optimization based Back Propagation Neural Network (PSO-BPNN) model is constructed based on time series to predict the dynamic performance of workflow systems. When the predicted value is out of the preset range, we analyze the issues according to data of Quality of Service (QoS) which is detected at runtime, to find why cause service performance failure. Thus it suggests more suitable recovery strategies for service composition. To bring this approach to fruition we analyze a simple case study.