A peer-to-peer server network system consists of a large number of autonomous servers logically connected in a peer-to-peer way where each server maintains a collection of documents. When a query of storing new documents is received by the system, a distributed search process determines the most relevant servers and redirects the documents to them for processing (compressing and storing at the right document base). In this paper, we model this distributed search process as a distributed sequential decision making problem using a set of interactive Markov Decision Processes (MDP), a specific stochastic game approach, which represent each server's decision making problem. The relevance of a server to a document is regarded as a reward considering the capacity of the storage and the goodness score of a server. We show that using a central MDP to derive an optimal policy of how to distribute documents among servers leads to high complexity and is inappropriate to the distributed nature of the application. We present then interactive MDPs approach transforming this problem into a decentralized decision making process.