We study the demand response (DR) of geo-distributed data centers (DCs) using smart grid's pricing signals set by local electric utilities. The geo-distributed DCs are suitable candidates for the DR programs due to their huge energy consumption and flexibility to distribute their energy demand across time and location, whereas the price signal is well-known for DR programs to reduce the peak-to-average load ratio. There are two dependencies that make the pricing design difficult: 1) dependency among utilities; and 2) dependency between DCs and their local utilities. Our proposed pricing scheme is constructed based on a two-stage Stackelberg game in which each utility sets a real-time price to maximize its own profit in Stage I and based on these prices, the DCs' service provider minimizes its cost via workload shifting and dynamic server allocation in Stage II. For the first dependency, we show that there exists a unique Nash equilibrium. For the second dependency, we propose an iterative and distributed algorithm that can converge to this equilibrium, where the “right prices” are set for the “right demands.” We also verify our proposal by trace-based simulations, and results show that our pricing scheme significantly outperforms other baseline schemes in terms of flattening the power demand over time and space.