Simulation of clustering algorithms in OODBs in order to evaluate their performances

TL;DR

This paper introduces a simulation-based methodology to evaluate and compare the performance of different object clustering algorithms in object-oriented databases, highlighting the superior performance of the CK algorithm.

Contribution

It proposes a new simulation methodology for assessing clustering policies and provides a comparative analysis of three existing algorithms using this approach.

Findings

CK outperforms Cactis and ORION in response time

Cactis is better than ORION in certain metrics

Simulation results validate the effectiveness of the proposed methodology

Abstract

A good object clustering is critical to the performance of object-oriented databases. However, it always involves some kind of overhead for the system. The aim of this paper is to propose a modelling methodology in order to evaluate the performances of different clustering policies. This methodology has been used to compare the performances of three clustering algorithms found in the literature (Cactis, CK and ORION) that we considered representative of the current research in the field of object clustering. The actual performance evaluation was performed using simulation. Simulation experiments showed that the Cactis algorithm is better than the ORION algorithm and that the CK algorithm totally outperforms both other algorithms in terms of response time and clustering overhead.