In this paper, an approach to assign BPEL workflow steps to available resources is presented. The approach takes data dependencies between workflow steps and the utilization of resources at runtime into account. The developed scheduling algorithm simulates whether the make-span of workflows could be reduced by providing additional resources from a Cloud infrastructure. If yes, Cloud resources are automatically set up and used to increase throughput. The proposed approach does not require any changes to the BPEL standard. An implementation based on the Active-BPEL engine and Amazon's Elastic Compute Cloud is presented. Experimental results for a real-life workflow from a medical application indicate that workflow execution times can be reduced significantly.