Run-time Revenue Maximization for Composite Web Services with Response Time Commitments
We investigate dynamic decision mechanisms for composite web services maximizing the expected revenue for the providers of composite services. A composite web service is represented by a (sequential) workflow, and for each task within this workflow, a number of service alternatives may be available. These alternatives offer the same functionality at different price-quality levels. After executing a sub-service, it is decided which alternative of the next sub-service in the workflow is invoked. The decisions optimizing expected revenue are based on observed response times, costs and response-time characteristics of the alternatives as well as end-to-end response-time objectives and corresponding rewards and penalties. We propose an approach, based on dynamic programming, to determine the optimal, dynamic selection policy. Extensive numerical examples show significant potential gain in expected revenues using the dynamic approach compared to other, non-dynamic approaches.