Cloud computing promises to provide computing resources to a large number of service applications in an on-demand manner. Traditionally, cloud providers such as Amazon only provide guaranteed allocation for compute and storage resources, and fails to support the bandwidth requirements and performance isolation among these applications. To address this limitation, recently a number of proposals advocate providing both guaranteed server and network resources in the form of Virtual Data Centers (VDCs). This raises the problem of optimally allocating both servers resources and data center networks to multiple VDCs in order to optimize total revenue, while minimizing the total energy consumption in the data center. However, despite recent studies on this problem, none of the existing solutions have considered the possibility of using VM migration to dynamically adjust the resource allocation, in order to meet the fluctuating resource demand of VDCs. In this paper, we propose VDC Planner, a migration-aware dynamic virtual data center embedding framework that aims at achieving high revenue while minimizing the total energy cost over-time. Our framework supports various usage scenarios, including VDC embedding, VDC scaling as well as dynamic VDC consolidation. Through experiments using realistic workload traces, we show our proposed approach achieves both higher revenue and lower average scheduling delay compared to existing solutions in the literature.