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Scientific applications are growing rapidly in both data scale and processing complexity due to advances in science instrumentation and network technologies, where Cloud computing as an emerging computing paradigm can offer unprecedented scalability and resources on demand, and is getting more and more adoption in the science community. We present our early effort in designing and building CloudDragon, a scientific computing Cloud platform based on OpenNebula. We take a structured approach that integrates client-side application specification and testing, service-based workflow submission and management, on-demand virtual cluster provisioning, high-throughput task scheduling and execution, and efficient and scalable Cloud resource management. We first analyze the integration efficiency of our approach in a cluster setting and then show a production deployment of the platform.