Empirical evaluation of data allocation algorithms for distributed multimedia database systems

Given a distributed multimedia database system and a set of queries as well as their frequencies from each site, the objective of a data allocation algorithm is to locate the multimedia data objects (MDOs) at different sites so as to minimize the total data transfer cost incurred in executing the queries. The data allocation problem, however, is NP-complete, and thus requires fast heuristics to generate efficient solutions. We propose three data allocation algorithms which are based on a genetic technique, an evolutionary process, and neural networks. We have implemented and evaluated these algorithms on our distributed multimedia database system test-bed. A comparison of the algorithms reveals trade-offs between their solution quality and time-complexity