As a kind of large-scale user-oriented dataintensive computing, cloud computing allows users to utilize on-demand computation, storage, data and services from around the world in a pay-as-you-go model. In cloud environment, applications need access to mass datasets that may each be replicated on different resources (or data centers). Mass data moving influences the execution efficiency of application to a large extent, while the economic cost of each replica itself can never be overlooked in such a model of business computing. Based on the above two considerations, how to select appropriate data centers for accessing replicas and creating a virtual machine(VM for short) to execute applications to make execution efficiency high and access cost low as far as possible simultaneously is a challenging and urgent problem. In this paper, a cost-aware resource selection model based on Weighted Set Covering Problem (WSCP) is proposed, according to the principle of spatial locality of data access. For the model, we apply a Weighted Greedy heuristic to produce an approximately optimal resource set for each task. Finally, verifies the validity of the model in simulation environment, and evaluate the performance of the algorithm presented. The result shows that WSCP-based heuristic can produce an approximately optimal solution in most cases to meet both execution efficiency and economic demands simultaneously, compared to other two strategies.