Vehicular cloud (VC) platforms integrate heterogeneous and distributed resources of moving vehicles to offer timely and cost-effective computing services. However, the dynamic nature of VCs (i.e., limited contact duration among vehicles), caused by vehicles' mobility, poses unique challenges to the execution of computation-intensive applications/tasks with directed acyclic graph (DAG) structure, where each task consists of multiple interdependent components (subtasks). In this paper, we study scheduling of DAG tasks over dynamic VCs, where multiple subtasks of a DAG task are dispersed across vehicles and then processed by cooperatively utilizing vehicles' resources. We formulate DAG task scheduling as a 0-1 integer programming, aiming to minimize the overall task completion time, while ensuring a high execution success rate, which turns out to be NP-hard. To tackle the problem, we develop a ranking and foresight-integrated dynamic scheduling scheme (RFID). RFID consists of (i) a dynamic downward ranking mechanism that sorts the scheduling priority of different subtasks, while explicitly taking into account for the sequential execution nature of DAG; (ii) a resource scarcity-based priority changing mechanism that overcomes possible performance degradations caused by the volatility of VC resources; and (iii) a degree-based weighted earliest finish time mechanism that assigns the subtask with the highest scheduling priority to the vehicle which offers rapid task execution along with reliable transmission links. Our simulation results reveal the effectiveness of our proposed scheme in comparison to benchmark methods.