Stochastic models of pull-based data replication in P2P systems

We consider pull-based data synchronization issues between a source and its replicas in P2P networks. Under continuous information change and lazy synchronization, these systems are highly susceptible to serving outdated content, which negatively affects their performance and user satisfaction. To understand these scenarios, we first introduce a novel model of interaction between two stochastic point processes - updates at the source and downloads at the replica - and derive the probability that a random query against the replica retrieves fresh content. Unlike prior work, we assume non-Poisson dynamics and determine statistical properties of the replication process that make it perform better for a given download rate. The second half of the paper applies these results to several more difficult algorithms - cascaded replication, cooperative caching, and redundant querying from the clients. Surprisingly, we discover that optimal cooperation involves just a single peer and that redundant querying can hurt the ability of the system to handle load (i.e., may lead to lower scalability).