Performance Evaluation for Clustering Algorithms in Object-Oriented Database Systems

TL;DR

This paper introduces a simulation-based methodology to evaluate object clustering algorithms in object-oriented databases, demonstrating CK's superior performance over Cactis and ORION in response time and overhead.

Contribution

It proposes a novel modeling approach for comparing clustering policies and provides empirical performance comparisons of three prominent algorithms.

Findings

CK outperforms Cactis and ORION in response time

CK has lower clustering overhead

Cactis is better than ORION in some metrics

Abstract

It is widely acknowledged that good object clustering is critical to the performance of object-oriented databases. However, object clustering 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 we performed 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.