Project BURDS introduces event based distributed data replication as a practical method for including redundacy in distributed systems. Data redundacy helps improve the availability of data under failures, and may also enhance the response time of data access by substituting local accesses for remote ones. Traditionally, data is replicated by copying a data object's state, or value. BURDS explores use of partially replicating state transition (or event) histories instead, in an attempt to show that they are more suitable for representing abstract data types in distributed computing environments. In addition BURDS, research intends to develop an experimental technique that ensures that results of performance comparisons between different replication methods are repeatable and analyzable. The complexity, the deep layering, and the non determinism of the environment of a distributed system render these goals particularly hard to achieve. However, recently available tools acquired for BURDS have explicit support for these goals. Finally, the implementation activity of BURDS should contribute to theoretical investigations into problems that are illuminated by BURDS. Two examples of these are: (1) modeling the potential information flow patterns in networks that may fail by partitioning, and (2) transforming an event history into a value.
