Many different types of applications simultaneously execute in current data centers (DCs). To provide low cost and improved performance, each application is typically deployed in distributed DCs. Tasks of users around the world first go through Internet service providers (ISPs) which deliver data between distributed DCs and users. However, capacities and bandwidth cost of different ISPs vary. Besides, energy cost of multiple DCs located in different geographical places is different. With the growth of tasks, the DC provider's energy and ISP bandwidth cost is huge and continues to increase. Therefore, due to the energy and bandwidth cost difference in different geographical places, it is highly difficult to minimize the DC provider's total cost. Therefore, to tackle the problem, this work proposes a cost-sensitive task routing approach that can jointly specify the optimal selection of available ISPs for the arriving tasks, and the optimal number of switched-on servers in each DC. Finally, the simulation with tasks in Google's data center shows the proposed cost-sensitive task routing approach can effectively decrease the DC provider's cost, and raise system throughput in comparison with some typical scheduling method.