Scalable update propagation in epidemic replicated databases

Abstract

Many distributed databases use an epidemic approach to manage replicated data. In this approach, user operations are executed on a single replica. Asynchronously, a separate activity performs periodic pair-wise comparison of data item copies to detect and bring up to date obsolete copies. The overhead due to comparison of data copies grows linearly with the number of data items in the database, which limits the scalability of the system.We propose an epidemic protocol whose overhead is linear in the number of data items being copied during update propagation. Since this number is typically much smaller than the total number of data items in the database, our protocol promises significant reduction of overhead.KeywordsData ItemVersion VectorCorrectness CriterionUser OperationReplica ManagementThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.