Federated cloud enables a workflow to be deployed in multiple private and public clouds. By facilitating external cloud-based services to execute sub-tasks of the workflow, service workflow owners can reduce the cost of executing the workflow, while meeting a performance requirement, since those cloud-based services can be more cost efficient and have better performance than internal ones. However, due to inter-dependencies between sub-tasks, the complexity of the workflow, and the heterogeneity of clouds, it is a challenge to achieve the optimal tradeoff between cost and performance. This paper presents a novel workflow scheduler designed to achieve the optimal end-to-end execution time and cost when deploying such complex workflows in heterogeneous computing nodes in clouds. Specifically, our scheduling algorithm addresses the tradeoff between the execution cost, the computing time, and the data transfer delay between sub-tasks. Our scheduler can handle complex workflows that contain recursively paralleled sub-flows caused by branch and merging sub-tasks. Experiments indicate that our scheduler can efficiently compute the near optimal deployment compared with greedy and evolutionary algorithms for both end-to-end execution time and corresponding cost.
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