Multi-objective Parametric Query Optimization for Distributed Database Systems

Abstract

A classical query optimization compares solutions on single cost metric, not capable for multiple costs. A multi-objective parametric optimization (MPQ) approach is potentially capable for optimization over multiple cost metrics and query parameters. This paper demonstrated an approach for multi-objective parametric query optimization (MPQO) for advanced database systems such as distributed database systems (DDBS). The query equivalent plans are compared according to multiple cost metrics and query related parameters (modeled by a function on metrics), cost metrics, and query parameters are semantically different and computed at different stage of optimization. MPQO also generalizes parametric optimization by catering the multiple metrics for query optimization. In this paper, performance of MPQO variants based on nature-inspired optimization; ‘Multi-Objective Genetic Algorithm’ and a parameter-less optimization ‘Teaching-learning- based optimization’ are also analyzed. MPQO builds a parametric space of query plans and progressively explores the multi-objective space according to user tradeoffs on query metrics. In heterogeneous and distributed database system, logically unified data is replicated and distributed across multiple distributed sites to achieve high reliable and available data system; this imposed a challenge on evaluation of Pareto set. An MPQO attempt exhaustively determines the optimal query plans on each end of parametric space.