Distributed Query Processing Plans generation using Teacher Learner Based Optimization

TL;DR

This paper presents a novel approach using Teacher-Learner Based Optimization (TLBO) to generate cost-effective distributed query plans, outperforming genetic algorithms especially with complex queries involving many relations.

Contribution

It introduces TLBO as a parameterless optimization method for distributed query plan generation, demonstrating superior performance over genetic algorithms in complex scenarios.

Findings

TLBO outperforms genetic algorithms in generating top query plans.

TLBO produces higher quality plans for queries with many relations.

Experimental results validate TLBO's effectiveness in distributed database query optimization.

Abstract

With the growing popularity, the number of data sources and the amount of data has been growing very fast in recent years. The distribution of operational data on disperse data sources impose a challenge on processing user queries. In such database systems, the database 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 or alternatives of a user query. Though it is not computationally reasonable to explore exhaustively all possible query plans in a large search space, thus a strategy is requisite to produce optimal query plans in distributed database systems. The query plan with most cost-effective option for query processing is measured necessary and must be generated for a given query. This paper attempts to generate such optimal query plans using a parameter less optimization technique Teaching-Learner Based Optimization(TLBO). The TLBO algorithm was experiential to go one better than the other optimization algorithms for the multi objective unconstrained and constrained benchmark problems. Experimental comparisons of TLBO based optimal plan generation with the multiobjective genetic algorithm 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.