Replicating data in distributed systems is often needed for availability and performance. In unstructured peer-to-peer networks, with epidemic messaging for query routing, replicating popular data items is also crucial to ensure high probability of finding the data within a bounded search distance from the requestor. This paper considers such networks and aims to maximize the probability of successful search. Prior work along these lines has analyzed the optimal degrees of replication for data items with non-uniform but global request rates, but did not address the issue of where replicas should be placed and was very very limited in the capabilities for handling heterogeneity and dynamics of network and workload.

This paper presents the integrated P2R2 algorithm for dynamic replication that addresses all these issues, and determines both the degrees of replication and the placement of the replicas in a provably near-optimal way. We prove that the P2R2 algorithm can guarantee a successful-search probability that is within a factor of 2 of the optimal solution. The algorithm is efficient and can handle workload evolution. We prove that, whenever the access patterns are in steady state, our algorithm converges to the desired near-optimal placement. We further show by simulations that the convergence rate is fast and that our algorithm outperforms prior methods.