Multi-objective Scheduling of BPEL Workflows in Geographically Distributed Clouds

In this paper, a novel scheduling algorithm for Cloud-based workflow applications is presented. If the constituent workflow tasks are geographically distributed - hosted by different Cloud providers or data centers of the same provider - data transmission can be the main bottleneck. The algorithm therefore takes data dependencies between workflow steps into account and assigns them to Cloud resources based on the two conflicting objectives of cost and execution time according to the preferences of the user. Our implementation is based on BPEL, an industry standard for workflow modeling, and does not require any changes to the standard. It is based on, but not limited to, the Active BPEL engine and Amazon´s Elastic Compute Cloud. To automatically adapt the scheduling decisions to network-related changes, the data transmission speed between the available resources is monitored continuously. Experimental results for a real-life workflow from a medical domain indicate that both the workflow execution times and the corresponding costs can be reduced significantly.