Generating Query Plans for Distributed Query Processing Using Genetic Algorithm

Query Processing is a key determinant in the overall performance of distributed databases. It requires processing of data at their respective sites and transmission of the same between them. These together constitute a distributed query processing strategy (DQP). DQP aims to arrive at an efficient query processing strategy for a given query. This strategy involves generation of efficient query plans for a distributed query. In case of distributed relational queries, the number of possible query plans grows exponentially with an increase in the number of relations accessed by the query. This number increases further when the relations, accessed by the query, have replicas at different sites. Such a large search space renders it infeasible to find optimal query plans. This paper presents a query plan generation algorithm that attempts to generate optimal query plans, for a given query, using genetic algorithm. The query plans so generated involve fewer sites, thus leading to efficient query processing. Further, experimental results show that the proposed algorithm converges quickly towards optimal query plans for an observed crossover and mutation probability.