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Cloud computing enables the delivery of computing, software, storage, and data access through web browsers as a metered service. In addition to commercial applications, an increasing number of large-scale workflow-based scientific applications are being supported by cloud computing. In order to meet the rapidly growing and dynamic computing demands of scientific users, the cloud service provider needs to employ efficient and cost-effective job schedulers to guarantee workflow completion time as well as improve resource utilization for high throughput. Based on rigorous cost models, we formulate a delay-constrained optimization problem to maximize resource utilization and propose a two-step workflow scheduling algorithm to minimize the cloud overhead within a user-specified execution time bound. The extensive simulation results illustrate that our approach consistently achieves lower computing overhead or higher resource utilization than existing methods within the execution time bound. Our approach also significantly reduces the total execution time by strategically selecting appropriate mapping nodes for prioritized modules.