This paper addresses the problem of fine-grained data replication in large distributed systems, such as the Internet, so as to minimize the user access delays. With fine-grained data replication, certain data objects, as opposed to a complete site, are duplicated at multiple servers. In this paper, we abstract the distributed system as an agent-based model wherein mobile agents on behalf of their nodes continuously compete for allocation and reallocation of data objects. However, since these agents do not have a global view of the system, the optimization process becomes highly local. This localization may encourage these selfish agents to alter the output of the resource allocation mechanism in their favor by misreporting critical data such as the objects' popularity. This paper proposes a game theoretical resource allocation mechanism involving selfish agents. The mechanism ensures that the agents do not misreport, always follow the rules, and that a global optima is achieved. The mechanism is extensively evaluated against some wellknown algorithms, such as: greedy, branch and bound, game theoretical auctions and genetic algorithms. The experimental results reveal that the mechanism provides excellent solution quality, while maintaining fast execution time.