A growing number of global companies select Green Cloud Data Centers (GCDCs) to manage their delay-constrained applications. The fast growth of users' tasks dramatically increases the energy consumed by GCDC, e.g., Google. The random nature of tasks brings a big challenge of scheduling tasks of each application with limited infrastructure resources of GCDCs. This work accurately computes a mathematical relation between task service rates and the number of tasks refusal in GCDC. Besides, it proposes a Temporal Task Scheduling (TTS) algorithm investigating the temporal variation in geo-distributed cloud data centers to schedule all tasks within their delay constraints. Furthermore, a novel dynamic hybrid meta-heuristic algorithm is developed for the formulated profit maximization problem, based on genetic simulated annealing and particle swarm optimization. The proposed algorithm can guarantee that differentiated service qualities can be provided with higher overall performance and lower energy cost. Trace-driven simulations demonstrate that larger throughput and profit is achieved than several existing scheduling algorithms.