The goal of cloud federation strategies is to define a mecha- nism for resources sharing among federation collaborators. Those mecha- nisms must be fair to guaranty the common benefits of all the federation members. This paper focuses on intermediate data allocation cost in fed- erated cloud storage. Through a federation mechanism, we propose a mixed integer linear programming model (MILP) to assist multiple data centers hosting intermediate data generated from a scientific community. Under the constraints of the problem, an exact algorithm is proposed to minimize intermediate data allocation cost over the federated data centers storage, taking into account scientific users requirements, inter- mediate data dependency and data size. Experimental results show the cost-efficiency and scalability of the proposed federated cloud storage model