VOODB: A Generic Discrete-Event Random Simulation Model to Evaluate the Performances of OODBs

International audiencePerformance of object-oriented database systems (OODBs) is still an issue to both designers and users nowadays. The aim of this paper is to propose a generic discrete-event random simulation model, called VOODB, in order to evaluate the performances of OODBs in general, and the performances of optimization methods like clustering in particular. Such optimization methods undoubtedly improve the performances of OODBs. Yet, they also always induce some kind of overhead for the system. Therefore, it is important to evaluate their exact impact on the overall performances. VOODB has been designed as a generic discrete-event random simulation model by putting to use a modelling approach, and has been validated by simulating the behavior of the O2 OODB and the Texas persistent object store. Since our final objective is to compare object clustering algorithms, some experiments have also been conducted on the DSTC clustering technique, which is implemented in Texas. To validate VOODB, performance results obtained by simulation for a given experiment have been compared to the results obtained by benchmarking the real systems in the same conditions. Benchmarking and simulation performance evaluations have been observed to be consistent, so it appears that simulation can be a reliable approach to evaluate the performances of OODBs

OCB: A Generic Benchmark to Evaluate the Performances of Object-Oriented Database Systems

International audienceWe present in this paper a generic object-oriented benchmark (the Object Clustering Benchmark) that has been designed to evaluate the performances of clustering policies in object-oriented databases. OCB is generic because its sample database may be customized to fit the databases introduced by the main existing benchmarks (e.g., OO1). OCB's current form is clustering-oriented because of its clustering-oriented workload, but it can be easily adapted to other purposes. Lastly, OCB's code is compact and easily portable. OCB has been implemented in a real system (Texas, running on a Sun workstation), in order to test a specific clustering policy called DSTC. A few results concerning this test are presented

Dynamic Clustering in Object-Oriented Databases: An Advocacy for Simplicity

International audienceWe present in this paper three dynamic clustering techniques for Object-Oriented Databases (OODBs). The first two, Dynamic, Statistical & Tunable Clustering (DSTC) and StatClust, exploit both comprehensive usage statistics and the inter-object reference graph. They are quite elaborate. However, they are also complex to implement and induce a high overhead. The third clustering technique, called Detection & Reclustering of Objects (DRO), is based on the same principles, but is much simpler to implement. These three clustering algorithm have been implemented in the Texas persistent object store and compared in terms of clustering efficiency (i.e., overall performance increase) and overhead using the Object Clustering Benchmark (OCB). The results obtained showed that DRO induced a lighter overhead while still achieving better overall performance

An improved method for database design.

Chan, Chi Wai Alan.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 121-126).Abstracts in English and Chinese.Abstract --- p.vAcknowledgements --- p.viiiList of Figures --- p.ixList of Tables --- p.xiChapter 1. --- Introduction --- p.12Chapter 1.1. --- Object-oriented databases --- p.12Chapter 1.2. --- Object-oriented Data Model --- p.14Chapter 1.3. --- Class and Object Instances --- p.15Chapter 1.4. --- Inheritance --- p.16Chapter 1.5. --- Constraint --- p.18Chapter 1.6. --- Physical Design for OODB Storage --- p.19Chapter 1.7. --- Problem Description --- p.20Chapter 1.8. --- Genetic Algorithm --- p.22Chapter 1.8.1. --- Constraint Handling Methods in GA --- p.25Chapter 1.9. --- Contributions of this work --- p.27Chapter 1.10. --- Outline of this work --- p.30Chapter 2. --- Literature Review --- p.32Chapter 2.1. --- Object-oriented database --- p.32Chapter 2.2. --- Object-Oriented Data model --- p.33Chapter 2.3. --- Physical Storage Model for OODBs --- p.35Chapter 2.3.1. --- Home Class (HC) Model --- p.36Chapter 2.3.2. --- Repeated Class (RC) Model --- p.38Chapter 2.3.3. --- Split Instance (SI) Model --- p.39Chapter 2.4. --- Solving physical storage design for OODBs --- p.40Chapter 2.5. --- Transaction-Based Approach --- p.41Chapter 2.6. --- Minimize database operational cost --- p.42Chapter 2.7. --- Combinational Optimization Method --- p.43Chapter 2.8. --- Research in Genetic Algorithm --- p.46Chapter 2.9. --- Implementation in GA --- p.47Chapter 2.10. --- Fitness function --- p.49Chapter 2.11. --- Crossover operation --- p.50Chapter 2.12. --- Encoding and Representation --- p.51Chapter 2.13. --- Parent Selection in Crossover Operation --- p.52Chapter 2.14. --- Reproductive selection --- p.53Chapter 2.14.1. --- Selection of Crossover Operator --- p.54Chapter 2.14.2. --- Replacement --- p.54Chapter 2.15. --- The Use of Constraint Handling Method --- p.55Chapter 2.15.1. --- Penalty function --- p.56Chapter 2.15.2. --- Decoder gives instruction to build feasible solution --- p.57Chapter 2.15.3. --- Adjustment method --- p.58Chapter 3. --- Solving Physical Storage Problem for OODB using GA --- p.60Chapter 3.1. --- Physical storage models for OODB --- p.61Chapter 3.2. --- Database operation for transactions --- p.62Chapter 3.3. --- Properly designed physical storage structure --- p.68Chapter 3.4. --- Fitness Evaluation --- p.69Chapter 3.5. --- Initial population --- p.72Chapter 3.6. --- Cross-breeding --- p.72Chapter 3.7. --- GA Operators --- p.74Chapter 3.8. --- Physical Design Problem Formulation for GA --- p.75Chapter 3.9. --- Representation and Encoding --- p.75Chapter 3.10. --- Solving Physical Storage Problem for OODB in GA --- p.76Chapter 3.10.1. --- Representation of design solution --- p.76Chapter 3.10.2. --- Encoding --- p.78Chapter 3.10.3. --- Initial population --- p.80Chapter 3.10.4. --- Parent Selection for breeding --- p.80Chapter 3.11. --- Traditional Constraint handling method --- p.83Chapter 3.11.1. --- Improve the Performance of Inheritance Constraint Handling methods --- p.85Chapter 3.12. --- Weakness in Gorla's GA approach --- p.87Chapter 4. --- Proposed Methodology --- p.88Chapter 4.1 --- Enhanced Crossover Operator --- p.90Chapter 4.2. --- Infeasible Solutions and Enhanced Adjustment Method --- p.93Chapter 4.3. --- Propagation Adjustment Method --- p.97Chapter 5. --- Computational Experiments --- p.99Chapter 5.1. --- Introduction --- p.99Chapter 5.2. --- Experiment Objective --- p.101Chapter 5.3. --- Tools and Setup --- p.102Chapter 5.4. --- Crossover Operator --- p.105Chapter 5.5. --- Mutation Operator --- p.105Chapter 5.6. --- Termination condition --- p.106Chapter 5.7. --- Computational Experiments --- p.107Chapter 5.7.1. --- An Illustrative Example ´ؤ UNIVERSITY database --- p.107Chapter 5.7.2. --- Simulation ´ؤ 9 classes and 25 classes --- p.115Chapter 5.7.3. --- Result --- p.116Chapter 6. --- Conclusions --- p.118Chapter 6.1. --- Summary of Achievements --- p.118Chapter 7. --- Bibliography --- p.121Chapter 8. --- Appendix --- p.12

On the classification and evaluation of prefetching schemes

Abstract available: p. [2