Generating Optimal Query Plans for Distributed Query Processing using Teacher-Learner Based Optimization

Modern day's queries are posed on database spread across the globe, this may impose a challenge on processing queries efficiently, and a strategy is required to generate optimal query plans. In distributed relational database systems, due to partitioning or replication on relations at multiple sites, the relations required by a query to answer, may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent alternatives or query plans for a user query. Though it is not computationally reasonable to explore exhaustively all possible query plans in a large search space, the query plan with most cost-effective option for query processing is measured necessary and must be generated for a given query. In this paper, an attempt has been made to generate such optimal query plans using parameter less optimization technique Teaching-Learner based Optimization (TLBO). The TLBO algorithm was observed to go one better than the other optimization algorithms for the multi-objective unconstrained and constrained benchmark problems. Experimental comparisons of this algorithm with the multi-objective GA based distributed query plan generation algorithm shows that for higher number of relations, the TLBO based algorithm is able to generate comparatively better quality Top-K query plans.