Cost Estimation for Queries Experiencing Multiple Contention States in Dynamic Multidatabase Environments

Abstract

Accurate query cost estimation is crucial to query optimization in a multidatabase system. Several estimation techniques for a static environment have been suggested in the literature. To develop a cost model for a dynamic environment, we recently introduced a multistate query-sampling method. It has been shown that this technique is promising in estimating the cost of a query run in any given contention state for a dynamic environment. In this paper, we study a new problem on how to estimate the cost of a large query that may experience multiple contention states. Following the discussion of limitations for two simple approaches, i.e., single state analysis and average cost analysis, we propose two novel techniques to tackle this challenge. The first one, called fractional analysis, is suitable for a gradually and smoothly changing environment, while the second one, called the probabilistic approach, is developed for a rapidly and randomly changing environment. The former estimates a query cost by analyzing its fractions, and the latter estimates a query cost based on Markov chain theory. The related issues including cost formula development, error analysis, and comparison among different approaches are discussed. Experiments demonstrate that the proposed techniques are quite promising in solving the new problem.

Cost-Based Query Optimization in Centralized Relational Databases

“…Several approaches, methods and techniques of query optimization have been proposed for various DBMS (i. e., relational, deductive, distributed, object, parallel). The quality of query optimization methods depends strongly on the accuracy on the efficiency of cost models (Hussein et al 2005, Naacke et al 1998, Zhu et al 2003, Adali et al 1996, Ganguly et al 1996, Gardarin et al 1996.…”

Mobile Join Algorithms based on Mobiles Agents for Large Scale Distributed Query Optimization

“…This function has an objective either to minimize the total time [35], or to minimize the response time [14]. The query optimizer based on a cost model [17,19,20,29,39], generates an optimal execution plan or close to optimal. The methods of classical optimization produce firstly an optimal execution plan.…”

Decentralized Dynamic Query Optimization based on Mobiles Agents for Large Scale Data Integration Systems

“…The general problem of the query optimization can be expressed as follows [14]: let a query q, a space of execution plans E, and a cost function cost, find the execution plan calculating q such as the cost(q) is minimum. Generally, an optimizer can be decomposed into three elements [14]: a search space corresponding to the virtual set of all possible execution plans, a search strategy generating an execution plan close to the optimal, and a cost model [28,35] allowing to annotate operators' trees in the considered search space.…”