Distributed Query Plan Generation using Bacterial Foraging Optimization

In distributed database systems, relations are replicated and fragmented at multiple sites to ensure easy availability and greater reliability. This leads to an exponential increase in the possible alternatives available for selecting the set of sites, constituting a query plan, for processing. Computing the optimal query plans, from amongst all possible query plans, is a discrete combinatorial optimization problem. This Distributed Query Plan Generation (DQPG) problem has been addressed using Bacterial Foraging Optimization (BFO) in this paper. Here, a novel BFO based DQPG algorithm (DQPGBFO), which generates the Top-K distributed query plans having the minimum total query processing cost, has been proposed. Experimental comparison of DQPGBFO with the existing Genetic Algorithm (GA) based DQPG algorithm (DQPGGA) shows that the former is able to generate Top-K query plans that have a comparatively lower total cost of processing a distributed query. This, in turn, leads to a reduction in the query response time and thus aids in decision making.