TABLE OF CONTENTS
Title page i
Declaration ii
Certification iii
Dedication iv
Acknowledgements v
Table of Contents vi
List of Tables viii
List of Figures ix
Abbreviations xi
Abstract xii
CHAPTER 1: INTRODUCTION
1.1 Background of the Study 1
1.2 Problem Statement 5
1.3 Aim and Objectives of the Study 5
1.4 Scope of the Study 5
1.5 Significance of the Study 6
1.6 Organization of the Research Work 6
CHAPTER 2: LITERATURE REVIEW
2.1 Introduction 7
2.2 Introduction to Database 8
2.2.1 Functions of database 11
2.2.2 Types of databases 11
2.3 Introduction to Database Management System (DBMS) 13
2.3.1 Objectives of DBMS 13
2.3.2 Functions of DBMS 14
2.3.3 Components of a DBMS 16
2.4 Control System Overview 16
2.4.1 Control actions and controller characteristics 19
2.5 PID Controller 20
2.5.1 Ziegler-Nichols rules for tuning PID controllers 21
2.5.2.1 Ziegler-Nichols first method 21
2.5.2.2 Second Method 22
2.6 Review of Related Works 23
2.7 Identified Knowledge Gaps 42
CHAPTER 3: MATERIALS AND METHODS
3.1 Materials 44
3.2 Methods 44
3.3 Database Characteristics/ Mathematical Model 46
3.4 Fuzzy Inference System (FIS) 54
3.5 Database Fuzzy Rule Format 54
3.6 Database Management 56
3.7 Computer Simulations 54
CHAPTER 4: RESULTS AND DISCUSSION
4.1 Results 59
4.2 Comparisons and Relationships of Functions 61
4.3 Database (A) Management 70
4.4 Database (B) Management 71
4.5 Comparison of the Optimized Control and the Conventional Control 75
CHAPTER 5: CONCLUSION AND RECOMMENDATIONS
5.1 Conclusion 79
5.2 Recommendations 80
5.3 Contribution to Knowledge 80
REFERENCES
APPENDICES
LIST OF TABLES
4.1: Comparison of database structures 67
LIST OF FIGURES
2.1: The feedback control loop 18
2.2: Block diagram of an industrial controller 19
2.3: PID control of a plant 20
2.4: Unit step response of a plant 21
2.5: Transfer function curve 22
2.6: Closed-loop system with a proportional controller 22
3.1: Method of the work 45
3.2: A scheme for deriving traffic flow value at each traffic line point. 46
3.3: The block diagram of the DBMS to be modeled 48
3.4: Design of fuzzy inference system 51
3.5: Membership functions input1 52
3.6: Membership functions input 2. 52
3.7: Membership functions output 1 53
3.8: Fuzzy plot of membership functions output 2 53
3.9: Fuzzy rule editor 55
3.10: Conventional model 56
3.11: Optimized model 57
3.12: SIMULINK model showing subsystem data processing in conventional model 57
3.13: SIMULINK model showing subsystem data access rate of the conventional model 58
4.1: Graph of database fuzzy controller response. 59
4.2: Graph of SIMULINK result showing data flow 60
4.3: Graph of fuzzy controller input rules. 60
4.4: SIMULINK result comparing data processing with data
access rate in the database 61
4.5: Data tracking 62
4.6: 3D plot of data access Z, database X, and data flow rate Y. 63
4.7: 3D plot of data process Z, database X, and data flow rate Y. 64
4.8: Plot showing the speed at which the database sends and receives data 65
4.9: Plot showing the speed at which the database saves
and discards data in the database 65
4.10: Database error calculation. 66
4.11: Graph of database A against data size 68
4.12: Graph of database B against data size 69
4.13: The graph of database A against data structure 69
4.14: The graph of database B against data structure 70
4.15 (a): 3D plot of the database A with its data structure. 70
4.16 (b): 3D plot of the database A with its data size. 71
4.17 (a): 3D plot of the database B with its data structure. 72
4.18 (b): 3D plot of the database B with its data size. 72
4.19: 3D plot showing relationship among database B with data size and data structure. 73
4.20: 3D plot showing relationship among database A with data size and data structure 73
4.21: Graph of fuzzy rule output against Time 74
4.22: Graph of data flow against Flow Time 75
4.23: Graph of data access against Time. 76
4.24: Graph of data flow error against flow time. 77
LIST OF ABBREVIATION
AI ARTIFICIAL INTELLIGENCE
ANN ARTIFICIAL NEURAL NETWORK
ATM ASYNCHRONOUS TRANSFER MODE
CAD COMPUTER AIDED DESIGN
CAI COMPUTER AIDED INSTRUCTION
CAL COMPUTER AIDED LEARNING
CPU CENTRAL PROCESSING UNIT
DBA DATABASE ADMINISTRATOR
DBMS DATABASE MANAGEMENT SYSTEM
DRF DYNAMIC RECONFIGURATION ALGORITHM
DTAC DEPLOYABLE TESTBED FOR AUTONOMIC COMPUTING
EFT ELECTRONI FUNDS TRANSFER
FCS FEEDBACK CONTROL REALTIME SCHEDULING
FIS FUZZY INFERENCE SYSTEM
FLS FUZZY LOGIC SYSTEM
GA GENERIC ALGORITHM
IP INTERNET PROTOCOL
MATLAB MATHEMATICS LABORATORY
MYSQL MY STRUCTURE QUERY LANGUAGE
OLTP ONLINE TRANSACTION PROCESSING
PID PROPORTIONAL INTEGRAL DERIVATIVE
PV PROCESS VARIABLE
QLSM QUERY LOCALITY SET MODEL
QOS QUALITY OF SERVICE
RAID REDUNTANT ARRAY OF INDEPENDENT DISKS
RDBMS RELATIONAL DATABASE MANAGEMENT SYSTEM
RISC REDUCED INSTRUCTION SET COMPUTER
SP SET POINT
STR STRING PROGRAMMING LANGUAGE
TCT TOTAL CYCLE TIME
XML EXTENSIBLE MARKUP LANGUAGE
CHAPTER 1
INTRODUCTION
1.1 BACKGROUND OF THE STUDY
A database-management system (DBMS) is a collection of linked data and an array of applications for accessing it. A database is a collection of connected data having an implied meaning that is stored in a database management system (DBMS). The database is a collection of data that provides information that is relevant to a business. The basic aim of a database management system (DBMS) is to proffer a suitable and efficient mechanism to store and retrieve database information. We use the term "data" to refer to known facts that can be recorded and have implicit meaning (Dana, 2006).
A datum, or data unit, is a symbol or combination of symbols that is utilized to represent anything. The heart of what we understand by information is the relationship between symbols and the things they represent. As a result, information is interpreted data — data that has been given semantics. The practical application of information is referred to as knowledge. While information may be easily transmitted, saved, and shared, the same cannot be true for knowledge. Personal experience is required for knowledge. Referring back to the scientific experiment, a third person reading the results will have information about it, while the person who conducted the experiment personally will have knowledge about it (Kurt, 1994).
Database systems are made to handle massive amounts of data. Data management necessitates both the creation of structures for storing data and the establishment of methods for manipulating data. Furthermore, despite system crashes or efforts at illegal access, the database system must preserve the security of the information stored. If data is to be shared across multiple users, the system must avoid any unexpected outcomes. Because information is so important in most organizations, computer scientists have developed a large body of concepts and techniques for managing data (Zheng, 2000).
Raw data, which was originally transferred manually from paper to punched cards and then into data-entry terminals, is now delivered into the system from a variety of sources, including Asynchronous Transfer Mode (ATMs), Electronic Funds Transfer, and direct consumer entry through the Internet, under the title Data Management Systems.
Many businesses rely on complex database management systems, because it is important to keep them running efficiently (Kurt et al., 1994). Tuning in database system is looking at all aspects of it, including hardware and software, in order to identify bottlenecks and make query response times as fast as feasible. Noon and Getta (2016), define tuning as the process of selecting server hardware, setting up RAID systems, deploying clusters, and customizing operating systems as well as the database itself for optimal performance. Database administrators or system administrators are usually in charge of database tuning. Because database tuning is such a complicated operation, machine learning is being used to automate it.
Database tuning is the process of database administrators optimizing a database's performance. In the enterprise, this usually means the maintenance of a large database management system (DBMS) such as Oracle or MySQL. This includes optimizing the performance of the database itself as well as the underlying hardware (Weikum and Chaudhuri, 2000). There has been a surge in interest in self-managing database and information systems that can automatically modify their configuration and behavior to meet performance needs over the last decade (Surajit et al., 2004).
Although control theory provides a solid theoretical foundation for the aforementioned issues, applying it to a self-tuned database management system is far from simple. The inherent differences between database/information systems and traditional control systems (mechanical, electrical, and chemical systems) add to the control loop design challenges. This requires careful mathematical study from a control theoretical viewpoint (Wang, 1994).
Controlling feedback is crucial when it comes to database management systems and networks. For example, feedback (also known as closed loop systems) is used to meet reaction time goals by altering scheduling priorities, memory allocations, and network bandwidth allocations. Unfortunately, most computer practitioners construct feedback control on the fly, resulting in undesirable outcomes such as excessive oscillations or delayed adaptability to variations in workloads. Control theory is used to evaluate and construct feedback loops in other mechanical, electrical, aeronautical, and other engineering areas.
Control theory is a organized approach to creating closed loop systems that are constant in the sense that they shun wild fluctuations, perfect in the sense that they accomplish desired outputs (e.g., response time objectives), and swiftly settle to steady state values (e.g., to adjust to workload dynamics). Control theory has recently been applied to the design of various parts of computing. Control theory has been applied to flow control and the construction of new Transmission Control Protocol/Internet Protocol (TCP)/IP versions in data networks, for example (Hellerstein et al., 2004).
Automatic tuning has long been an elusive aim in database technology, but it is more important in current E-services. Studies on self-tuning databases now focus on long-term performance, which tends to adapt a static perspective of problems. Due to the inherent variations in workloads and environmental conditions, it is proposed that maintaining transient performance in such systems is a more essential issue (Kurt et al., 1994).
The workload performance has also been modeled and predicted through research. Researchers can achieve adaptivity by employing a variety of techniques and approaches, such as fuzzy logic, which employs the Fuzzy Inference System (FIS) to predict buffer-hit-ratio, database size, and user count. To deal with such dynamics and maintain performance in self-tuning databases, this work proposes the use of PID controllers based on feedback control theory. It will go over how to apply relevant control-theoretical techniques to solve common problems in this field.
The absence of proper analytical models for complex database systems is among the key challenges in employing formal methods to analyze such problems. System identification approaches can help to tackle this problem in part. Self-tuning database technology, according to one view, should be based on the feedback control loop paradigm, but it must also be founded on mathematical models and their right engineering into system components. Furthermore, composing information services into really self-tuning, higher-level E-services may necessitate a drastic shift away from complex, highly componentized software architectures with tight interfaces between RISC-style “autonomic” components. The purpose of this study was to reduce the work load and data traffic usually observed in database management system by applying a closed-looped control system.
1.2 PROBLEM STATEMENT
Owing to the difficulty currently experience in Database Management Systems demanding high volume of human interventions which often results in poor performance of the system; it is therefore necessary to develop and introduce a control system using PID controller that allows for an automated self-tuning Database Management System in real time. Thus, the volume of human interferences and the resulting associated errors can be reduced to the barest minimum.
1.3 AIM AND OBJECTIVES OF THE STUDY
The aim of the research work is to apply control system theory to a Database Management System (DBMS).
The objectives of this thesis are:
• To review relevant literatures in the field of study.
• To model a prototype control system capable of optimizing the performance of a database management system.
• To simulate the system modeled using MATLAB/SIMULINK.
• To analyze the results gotten and compare with existing theories to validate the work done.
1.4 SCOPE OF THE STUDY
The study will be limited to the design and modeling of a control system in a decision-making database system.
1.5 SIGNIFICANCE OF THE STUDY
The research will help students of electrical/electronic engineering who are carrying out research works on control of a database management system. And also organizations with database management systems (DBMS) will benefit in the discarding of manual operation of their database systems which gives room to irregularities such as fraud, operational delays, timing errors, etc.
1.6 ORGANIZATION OF THE RESEARCH WORK
This thesis is arranged in five chapters:
Chapter 1 presents the introduction and background information of the study.
Chapter 2 presents the literature review of the level of Performance improvement in the database management system on the application of control system. Review of related work, fuzzy logic system and PID controllers is also presented.
In chapter 3, the methodology and processes leading to the development of fuzzy inference system are presented. It also describes the simulation carried out in fuzzy logic toolbox in MATLAB.
Chapter 4 presents the results and discussion of results obtained from the conventional/analytical and fuzzy logic method.
Finally, in chapter 5, conclusion and recommendations for future work is presented.
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