A fundamental paradigm in P2P is that of a large community of intermittently-connected nodes that cooperate to share files. Because nodes are intermittently connected, the P2Pcommunitymustreplicate and replace files as a function of their popularity to achieve satisfactory performance. We develop a suite of distributed, adaptive algorithms for replicating and replacing content in a P2P community. We do this for structured P2P communities, in which a distributed hash table (DHT) substrate is available for locating the node responsible for a key. In particular, we develop the Top-K MFR replication and replacement algorithm, which is not only straightforward to layer on top of a DHT substrate, but also adaptively converges to a nearly-optimal replication profile. Furthermore, we develop an analytical optimization theory for benchmarking the performance of replication/replacement algorithms, including algorithms that employ erasure codes.