Creating replicas of frequently accessed data objects across a read intensive network can lead to reduced communication cost and end-user response time. On the contrary, data replication in the presence of writes incurs extra cost due to multiple updates. The selection of data objects and servers requires solving a constraint optimization problem, which is NP-complete in general. A majority of the current state-of-the-art replica placement techniques suffer from high computational complexity issues. To circumvent such issues, we propose a cooperative game theoretical technique in which the servers (players) in the system collectively deliberate to converge at a replica schema that is beneficial to the system as a whole. In particular we make use of the Aumann-Shapley mechanism of cooperative game theory to propose an effective replica placement technique that yields good solutions when the system is very heavily loaded. Experimental comparisons are made against: 1) branch and bound, 2) greedy, 3) genetic, 4) Dutch auction, and 5) English auction. As demonstrated by the experimental results, the proposed technique maintains superior solution quality in terms of lower communication cost and reduced execution time.