UNIVERSITI PUTRA MALAYSIA IMPROVING RESOURCE …

UNIVERSITI PUTRA MALAYSIA

IMPROVING RESOURCE MANAGEMENT WITH MULTI-INSTANCE BROKER SCHEDULING ALGORITHM IN

HIERARCHICAL GRID COMPUTING

BAKRI BIN YAHAYA

FSKTM 2016 35

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IMPROVING RESOURCE MANAGEMENT WITH MULTI-INSTANCE BROKER SCHEDULING ALGORITHM IN

HIERARCHICAL GRID COMPUTING

By

BAKRI BIN YAHAYA

Thesis Submitted to the School of Graduate Studies, Universiti Putra Malaysia, in Fulfillment of the Requirements for the

Degree of Doctor of Philosophy

September 2016

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COPYRIGHT

All materials contained within the thesis, including without limitation text, logos, icons, photographs and all other artworks, is copyright material of Universiti Putra Malaysia unless otherwise stated. Use may be made of any

material contained within the thesis for non-commercial purposes from the copyright holder. Commercial use of material may only be made with the express, prior, written permission of Universiti Putra Malaysia.

Copyright © Universiti Putra Malaysia

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Abstract of thesis presented to the Senate of Universiti Putra Malaysia in fulfillment of the requirement for the Degree of Doctor of Philosophy

IMPROVING RESOURCE MANAGEMENT WITH MULTI-INSTANCE

BROKER SCHEDULING ALGORITHM IN HIERARCHICAL GRID COMPUTING

By

BAKRI BIN YAHAYA

September 2016

Chairman : Associate Professor Rohaya Latip, PhD Faculty : Computer Science and Information Technology

This research addresses the performance issue of grid computing based on

resource management, resource broker, resource scheduling and algorithm for middleware in the grid environment. The improved Hierarchical Load Balancing Algorithm (iHLBA) was chosen as the benchmark algorithm as it

focuses on scheduling for hierarchical grid computing environment. The main purpose of this research is to propose a design of scheduling algorithm for hierarchical grid computing. For this purpose, processing performance, namely

makespan time, response time, throughput, job distribution and the threshold, is the focus parameters of this study. Thus, Discrete Event Simulation is used

as the research methodology in this study. In brief, this research aims to solve resource management issues so as to enhance the grid computing performance.

The iHLBA scheduling algorithm was tested and it was found that the main issue was the delay occurring on the job submission. This issue needs to be

addressed properly in order to obtain the targeted performance. Secondly, the receiving process of iHLBA is followed through on pre-defined settings without making any adjustment to the resource items of computing power on the

destination node. Therefore, the issue that occurs is the grid performance in terms of throughput. Hence, resource items such as the CPU, memory and network bandwidth capacity have to be considered when gauging the grid

computing power. The distribution of job into grid resources is an interesting aspect to be explored so as to gain highest fairness rate compared to the current performance.

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Multi-Instance Broker Scheduling Algorithm (MiBSA) has been proposed as a new scheduling algorithm to get rid of the drawback from the iHLBA algorithm.

The Self-Adaptive Broker Manager, Multi-Instance Broker, Dual Layer Computing Allocation and Performance Recommender are the new modules introduced to achieve the research objectives. The designed modules is in the

form of algorithms, GridSim, Java Development Kits and Java Language used to support the design process whereas the Netbeans software serves as the platform to simulate the experiment so as to obtain the results. Results from

each of the variables and algorithms had been compared and statistical methods such as the t-test and normality test were used to verify the results.

Overall, the final results showed that the new designed algorithm (i.e., MiBSA algorithm) has successfully improved grid processing performance and successfully surpassed the performance of previous algorithm.

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Abstrak tesis yang dikemukakan kepada Senat Universiti Putra Malaysia sebagai memenuhi keperluan untuk Ijazah Doktor Falsafah

PERBAIKAN PENGURUSAN SUMBER DENGAN PELBAGAI-INSTANCE

BROKER PENJADUALAN ALGORITMA DALAM PENGKOMPUTERAN GRID BERHIERARKI

Oleh

BAKRI BIN YAHAYA

September 2016

Pengerusi : Profesor Madya Rohaya Latip, PhD Fakulti : Sains Komputer dan Teknologi Maklumat

Kajian ini menangani isu prestasi pengkomputeran grid berasaskan

pengurusan sumber, broker sumber, penjadualan sumber dan algoritma untuk perisian tengah dalam persekitaran grid.” improve Hierarchical Load Balancing Algorithm” (iHLBA) dipilih sebagai algoritma penanda aras kerana ia

memfokuskan kepada penjadualan untuk persekitaran pengkomputeran grid berhierarki. Tujuan utama kajian ini adalah untuk mencadangkan rekabentuk algoritma penjadualan bagi pengkomputeran grid berhierarki. Bagi tujuan ini,

prestasi pemprosesan iaitu masa makespan, masa tindakbalas, daya pemprosesan, pengagihan kerja dan nilai ambang adalah parameter yang

difokuskan dalam kajian ini. Oleh itu, Discrete Event Simulation digunakan sebagai metodologi penyelidikan dalam kajian ini. Secara ringkasnya, kajian ini bermatlamat untuk menyelesaikan isu-isu pengurusan sumber bagi

meningkatkan prestasi pengkomputeran grid.

Algoritma penjadualan iHLBA telah diuji dan didapati bahawa isu utama ialah

kelewatan yang berlaku pada penyerahan beban kerja. Isu ini perlu ditangani dengan baik bagi mendapatkan prestasi sasaran. Keduanya, proses penerimaan iHLBA diikuti melalui tetapan yang telah ditetapkan tanpa

membuat sebarang penyelarasan kepada item-item sumber kuasa pengkomputeran pada nod destinasi. Oleh itu, isu yang berlaku adalah isu prestasi grid dari segi daya pemprosesan. Maka, kapasiti item-item sumber

seperti CPU, ingatan dan lebar jalur rangkaian perlu dipertimbangkan apabila mengukur kuasa pengkomputeran grid. Pengagihan kerja ke dalam sumber grid adalah satu aspek yang menarik untuk diterokai bagi mendapatkan kadar

kesamarataan yang lebih tinggi berbanding dengan prestasi semasa.

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Pelbagai–Instance Broker Penjadualan Algoritma (MiBSA) telah dicadangkan sebagai algoritma penjadualan yang baharu untuk menyingkirkan kelemahan

algoritma iHLBA. Pengurus Broker Sesuai Diri (Self-Adaptive Broker Manager), Broker Pelbagai-Instance (Multi-Instance Broker), Dual Lapis Penguntukan Pengkomputeran (Dual Layer Computing Allocation) dan Pengesyor Prestasi

(Performance Recommender) adalah modul baharu yang diperkenalkan untuk mencapai objektif penyelidikan. Modul-modul yang direkabentuk adalah dalam format algoritma, GridSim, Java Development Kits, dan Bahasa Java

digunakan untuk menyokong proses merekabentuk manakala perisian NetBeans berfungsi sebagai platform untuk mensimulasikan eksperimen bagi

mendapatkan keputusan. Hasil daripada setiap pembolehubah dan algoritma telah dibandingkan dan kedah statistik seperti ujian-t dan ujian normaliti telah digunakan untuk mengesahkan keputusan. Secara keseluruhannya,

keputusan akhir menunjukkan bahawa algoritma baharu yang direkabentuk (iaitu, MiBSA) telah berjaya meningkatkan prestasi pemprosesan dan algoritma MiBSA berjaya mengatasi prestasi algoritma sebelumnya.

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ACKNOWLEDGEMENTS

My thanks to Allah for all things throughout my voyage of knowledge exploration. Syukran Ya Allah, Ya Zal Jala Li Wal Ikram for giving me good health and stability to learn. Through Him, everything is possible.

First and foremost, I would like to express my sincere gratitude to my supervisor, Associate Professor Dr. Rohaya Latip, for giving me the opportunity

to carry out this project. Her comments and suggestions for further development, as well as assistance during writing this thesis, are invaluable to me. Her talent, knowledge, interest and research style have provided me with

the opportunity to learn and made me become a better PhD candidate. I would like to express my sincere thanks and appreciation to the supervisory committee members, Dr. Azizol Haji Abdullah and Professor Dr. Mohamed

Othman, for their guidance and valuable suggestions throughout this work and for making this a success.

My deepest appreciation to my parents, Mrs. Zaini Haji M. Dali, Haji Norsham bin Abdul Wahab and Hajah Sapiah binti Mohamed, and my late parents, Haji

Yahaya bin Mat Lazim, and Hajah Jamaliah binti Maarif, and to all my siblings for their encouragement and support, which have helped to make it possible for me to achieve this work. To my loyal friends, Che Suhaimi Che Hashim,

Mohd Khairul Hafizan Wong Abdullah, Mohd Sholihin Abdullah, Abdul Rahim Abdul Rahman, Mohd Hanif Ahmad and the late Djessfan Dato Zainal Abidin, who have never ceased to support me throughout my PhD journey. Last but

not least, I am extremely thankful to my family, especially my wife, Mrs. Suhaila binti Norsham, who has been very supportive and patiently waiting for me and helped me in many ways to complete my studies. To all my kids,

Amirah Syahdina, Amir Danish, Amirah Maisarah and Amir Elman Uzair, thanks for the motivation and love. Finally, I hope that I have fulfilled the principal intent of my studies.

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This thesis was submitted to the Senate of the Universiti Putra Malaysia and has been accepted as fulfillment of the requirement for the degree of Doctor

of philosophy. The members of the Supervisory Committee are as follows:

Rohaya Latip, PhD Associate Professor Faculty of Computer Science and Information Technology

Universiti Putra Malaysia (Chairman)

Azizol Haji Abdullah, PhD

Lecturer Faculty of Computer Science and Information Technology Universiti Putra Malaysia

(Member)

Mohamed Othman, PhD Professor Faculty of Computer Science and Information Technology

Universiti Putra Malaysia (Member)

ROBIAH BINTI YUNUS, PhD

Professor and Dean School of Graduate Studies Universiti Putra Malaysia

Date:

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Declaration by graduate student

I hereby confirm that: this thesis is my original work;

quotations, illustrations and citations have been duly referenced;

this thesis has not been submitted previously or concurrently for any otherdegree at any institutions;

intellectual property from the thesis and copyright of thesis are fully-ownedby Universiti Putra Malaysia, as according to the Universiti Putra Malaysia(Research) Rules 2012;

written permission must be obtained from supervisor and the office ofDeputy Vice-Chancellor (Research and innovation) before thesis is

published (in the form of written, printed or in electronic form) includingbooks, journals, modules, proceedings, popular writings, seminar papers,manuscripts, posters, reports, lecture notes, learning modules or any other

materials as stated in the Universiti Putra Malaysia (Research) Rules 2012; there is no plagiarism or data falsification/fabrication in the thesis, and

scholarly integrity is upheld as according to the Universiti Putra Malaysia(Graduate Studies) Rules 2003 (Revision 2012-2013) and the UniversitiPutra Malaysia (Research) Rules 2012. The thesis has undergone

plagiarism detection software

Signature: _______________________________ Date: _______________

Name and Matric No: Bakri Bin Yahaya, GS26630

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Declaration by Members of Supervisory Committee

This is to confirm that: the research conducted and the writing of this thesis was under our

supervision; supervision responsibilities as stated in the Universiti Putra Malaysia

(Graduate Studies) Rules 2003 (Revision 2012-2013) were adhered to.

Signature:

Name of Chairman of Supervisory

Committee:

Associate Professor Dr. Rohaya Latip

Signature:

Name of Member of Supervisory

Committee:

Dr. Azizol Haji Abdullah

Signature:

Name of Member of Supervisory

Committee:

Professor Dr. Mohamed Othman

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TABLE OF CONTENTS

Page

ABSTRACT i ABSTRAK iii ACKNOWLEDGEMENTS v

APPROVAL vi DECLARATION viii LIST OF TABLES xiii

LIST OF FIGURES xv LIST OF ABBREVIATIONS xix

CHAPTER 1 INTRODUCTION 1

1.1 Research Background 1 1.2 Research Motivation 2 1.3 Problem Statement 3

1.4 Research Objectives 4 1.5 Research Scope 5

1.6 Research Contribution 5 1.7 Thesis Organisation 6

2 RELATED WORKS 8 2.1 Introduction 8 2.2 Grid Resource Management 8

2.3 Grid Scheduling and Load Balancing Characteristics 11 2.3.1 Comparison of Static and Dynamic

Characteristics 12

2.3.2 Comparison of Centralised and Decentralised Characteristics

14

2.4 Scheduling in Grid Computing 17

2.4.1 Grid Scheduling Algorithm Strategy 17 2.4.2 Grid Scheduling Model 21 2.4.3 Grid Scheduling Related Work 25

2.4.3.1 Batch Mode Heuristic Scheduling Algorithms

26

2.4.3.2 Online Mode Heuristic Scheduling

Algorithms

28

2.5 Load Balancing in Grid Computing 29

2.5.1 Load Balancing Algorithm Strategies 29 2.5.1.1 Control Strategy 29 2.5.1.2 Components Strategy 31

2.5.1.3 Other Issues 34 2.5.2 Load Balancing Hierarchical Architecture 36 2.5.3 Load Balancing Related Work 37

2.6 Related Algorithms 41

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2.6.1 Ant Colony Optimisation (ACO) 42 2.6.1.1 Ant Colony Optimisation Process Flow 42

2.6.1.2 Ant Colony Optimisation Drawbacks 45 2.6.2 Genetic Algorithm (GA) 46 2.6.2.1 Genetic Algorithm Process Flow 47

2.6.2.2 Genetic Algorithm Evolution 48 2.6.2.3 Genetic Algorithm Drawbacks 48 2.6.3 Particle Swarm Optimization Algorithm (PSO) 49

2.6.3.1 Particle Swarm Optimization Process Flow

50

2.6.3.2 Particle Swarm Optimization Population Topology

52

2.6.3.3 Particle Swarm Optimization

Evolution

54

2.6.3.4 Particle Swarm Optimization Drawbacks

55

2.7 iHLBA Algorithm 56 2.7.1 Benchmark Algorithm Simulation Environment 57 2.7.2 Benchmark Algorithm Process Flow 59

2.7.3 Benchmark Simulation Environment Specification

61

2.8 Summary 61

3 RESEARCH METHODOLOGY 64

3.1 Introduction 64

3.2 Research Methodology Model 64 3.3 Research Steps 66 3.4 Algorithm Strategy 67

3.4.1 Performance Metrics 68 3.4.2 Algorithm Calculation Technique 71

3.4.3 Inter Module Communication 74 3.5 Proposed System 75 3.5.1 System Component Design 76

3.6 Simulation Model in GridSim 79 3.6.1 Research Simulation Environment 82 3.6.2 Hardware Requirement for Simulation 83

3.6.3 Evaluation 83 3.6.4 Validation of Results 84 3.7 Summary 84

4 MULTI-INSTANCE BROKER SCHEDULING

ALGORITHM 85

4.1 Introduction 85 4.2 Multi-Instance Broker Scheduling Algorithm

Framework 85

4.2.1 Multi-Instance Broker Scheduling Algorithm Properties

89

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4.2.2 Multi-Instance Broker Scheduling Algorithm Characteristics

90

4.2.3 Functional Requirement of MiBSA Algorithm 93 4.3 Performance Recommender 94 4.3.1 Performance Recommender Characteristics 94

4.3.2 Performance Recommender Process Flow 95

5 SELF-ADAPTIVE BROKER MANAGER, RESULTS AND

DISCUSSION

99

5.1 Introduction 99

5.2 Self-Adaptive Broker Manager Module Characteristics 99 5.3 Self-Adaptive Broker Manager Process Flow 100 5.4 Self-Adaptive Broker Manager Algorithm 102

5.5 Self-Adaptive Broker Manager Decision Strategy 102 5.6 Makespan Time Results 105 5.7 Response Time Results 111

5.8 Summary 117

6 MULTI-INSTANCE BROKER RESULT AND

DISCUSSION

118

6.1 Introduction 118 6.2 Multi-Instance Broker Characteristics 119

6.3 Multi-Instance Broker Process Flow 119 6.4 Multi-Instance Broker Algorithm 120 6.5 Multi-Instance Broker Module Decision Strategy 121

6.6 Job Distribution Results 122 6.7 Threshold Results 128 6.8 Summary 135

7 DUAL-LAYER COMPUTING ALLOCATION RESULT AND

DISCUSSION

136

7.1 Introduction 136 7.2 Dual Layer Computing Allocation Characteristics 138

7.3 Dual Layer Computing Allocation Process Flow 139 7.4 Dual Layer Computing Allocation Algorithm 140 7.5 Dual Layer Computing Allocation Decision Strategy 141

7.6 Throughput Results 142

8 CONCLUSION AND FUTURE WORKS 149 8.1 Introduction 149

8.2 Future Works 150

REFERENCES 151

APPENDICES 167 BIODATA OF STUDENT 179 LIST OF PUBLICATIONS 180

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LIST OF TABLES

Table Page 2.1 Comparison of Static and Dynamic Characteristics 13

2.2 Comparison of Centralised and Decentralised

Characteristics 15

2.3 Scheduling Based Strategy Option 18

2.4 Four Policies of Load Balancing Methods 32 2.5 Network Structure of Load Balancing Strategy 33

2.6 Network Structure Load Balancing Previous Work 38

2.7 Comparison of Load Balancing Algorithm 41

2.8 Drawbacks of the ACO Algorithm 46 2.9 Drawbacks of the GA Algorithm 49

2.10 System Component Details 59

2.11 Benchmark Simulation Environment 61 2.12 A Summary of Algorithm Drawbacks 62

3.1 The Current Utilisation Status of Each Cluster 72

3.2 Status of Cluster Utilisation after Calculation 73 3.3 New Status of Remaining Computing Power 73

3.4 New Status of All Parameters 74

3.5 Status of Dependency of Parameters 78

3.6 Job Attributes and Format 81 3.7 Resource Attributes and Format 81

3.8 MiBSA Algorithm Simulation Environment 82

3.9 Computing Power Composition in the Simulation 82

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3.10 Computer Specification Used to Run the Simulation 83

4.1 Basic Modules on the MiBSA Algorithm 86

4.2 New Modules Introduced on the MiBSA Algorithm 89 4.3 Multi-Instance Broker Scheduling Algorithm Properties 90

4.4 Characteristics of the Multi-Instance Broker Scheduling

Algorithm

91

4.5 Characteristics of Performance Recommender 94

5.1 Self-Adaptive Broker Manager Characteristics 100

5.2 Range of CPU and Job against Weightage 103 5.3 Range of the Calculated Weight and Number of Broker

Instances

103

5.4 Percentage of Makespan Time Improvement of the

MiBSA Algorithm in Comparison to iHLBA

110

5.5 Percentage of Response Time Decrement of the MiBSA

Algorithm in Comparison to iHLBA

116

6.1 Multi-Instance Broker Characteristics 119

6.2 Percentage of Load Distribution Improvement of the

MiBSA Algorithm in Comparison to iHLBA

127

6.3 Percentage of Threshold Improvement of the MiBSA

Algorithm in Comparison to iHLBA

134

7.1 Experimental Scenario Representing Computing Power

Allocation

137

7.2 Characteristics of the Dual Layer Computing Allocation 139

7.3 Percentage of Throughput Performance Improvement of

the MiBSA Algorithm in Comparison to iHLBA 147

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LIST OF FIGURES

Figure Page 2.1 Anatomy of Grid Resource Management (Kertész and

Prokosch, 2010)

9

2.2 Scheduling and Load Balancing Characteristics Diagram 11

2.3 The Centralised Scheduling Model 22

2.4 The Decentralised Scheduling Model 23 2.5 Hierarchical Scheduling Model 25

2.6 Suri and Manpreet Load Balancing Steps 30

2.7 Three (3) Level Steps of Load Balancing Technique 34

2.8 Basic Model Associated to Cluster Architecture 36 2.9 Three (3) Layers Cluster Grid Architecture 37

2.10 Ant Colony Optimisation Process Flow 43

2.11 An Example of Pseudo-code of ACO Algorithm 44 2.12 Genetic Algorithm Process Flow 47

2.13 Flow Diagram Illustrating the PSO Algorithm 51

2.14 An Example of Pseudo-code of the PSO Algorithm 52 2.15 Topology Used in PSO Algorithm 53

2.16 Benchmark Algorithm Framework Structure 58

2.17 Benchmark Algorithm Process Flow 60

3.1 Research Methodology Model 65 3.2 Research Methodology Diagram 66

3.3 Entry and Exit of Modules Based on Events 75

3.4 New System Components Design 77

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3.5 A Flow Diagram in GridSim Based Simulations 79

4.1 The Multi-Instance Broker Scheduling Algorithm

Framework 88

4.2 Functional Diagram from GIS to the End of Processes 93

4.3 Historical Makespan Time Process Flow 96

4.4 Expected Makespan Time Process Flow 97 5.1 Self-Adaptive Broker Manager Process Flow 101

5.2 Algorithm of Self-Adaptive Broker Manager 102

5.3 A Comparison of Makespan Time Performance of MiBSA and iHLBA on 2000 Jobs

105


106


107


108

5.7 A Comparison of Makespan Time Performance of MiBSA and iHLBA on 10,000 Jobs

109

5.8 The Overall Makespan Time Performance Increment

Comparison of MiBSA and iHLBA 110

5.9 A Comparison of Response Time Performance of MiBSA

and iHLBA on 2000 Jobs 112







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5.13 A Comparison of Response Time Performance of MiBSA and iHLBA on 10,000 Jobs

115

5.14 The Overall Response Time Performance Increment

Comparison of MiBSA and iHLBA 116

6.1 The Multi-Instance Broker Process Flow 120

6.2 The Multi-Instance Broker Algorithm 121

6.3 A Comparison of the Load Distribution Performance Increment of MiBSA and iHLBA on 2000 Jobs

123


123


124


125

6.7 A Comparison of the Load Distribution Performance Increment of MiBSA and iHLBA on 10,000 Jobs

126

6.8 The Overall Load Distribution Performance Increment Comparison of MiBSA and iHLBA

127

6.9 A Comparison of the Cluster Threshold Performance of MiBSA and iHLBA on 2000 Jobs

129

6.10 A Comparison of the Cluster Threshold Performance of

MiBSA and iHLBA on 4000 Jobs 130






MiBSA and iHLBA on 10,000 Jobs 133

6.14 The Overall Threshold Performance Increment of MiBSA

and iHLBA 134

7.1 The Background Load versus Scenarios Results 138

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7.2 Dual Layer Computing Allocation Process Flow 140

7.3 Dual Layer Computing Allocation Algorithm 141

7.4 A Comparison of Throughput Performance of MiBSA and iHLBA on 2000 Jobs

143

7.5 A Comparison of Throughput Performance of MiBSA and iHLBA on 4000 Jobs

144

7.6 A Comparison of Throughput Performance of MiBSA and

iHLBA on 6000 Jobs 145


iHLBA on 8000 Jobs 146


iHLBA on 10,000 Jobs 147

7.9 The Overall Throughput Performance Increment of

MiBSA and iHLBA 148

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LIST OF ABBREVIATIONS

ACL Average Cluster Load ACO

Ant Colony Optimisation

ACP

Average Computing Power

ACPN

Average Computing Power Per Node AL

Average Load of the System

API

Application Programme Interface

BGL

Background Load BRMT

Best Recorded Makespan Time

CE’s

Cluster Elements

CP

Computing Power

CS

Cluster Schedulers CW

Calculated Weight

DRM

Domain Resource Manager

EG-EDF

Earliest Gap-Earlier Deadline First FCFS

First Come First Serves

FIFO

First In First Out

GA

Genetic Algorithm GIS

Grid Information Server

GRM

Grid Resource Management

GWA

Grid Workload Archive

IDE

Integrated Development Environment iHLBA

Improved Hierarchical Load Balancing Algorithm

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JDK Java Development Kit

MET

Minimum Execution Time MFTF

Most Fit Task First

MiBSA

Multi-Instance Broker Scheduling Algorithm

MIPS

Million Instruction per Second

PSO

Particle Swarm Optimization SI-DDLM

Sender Initiated Decentralised Dynamic Load Balancing

SS

System Scheduler

TW

Total number of Workload VO

Virtual Organisation

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CHAPTER 1

INTRODUCTION

1.1 Research Background

The diversity of grid has made the grid computing environment uncertain and rather challenging to be managed. Heterogeneous character that contributes to its dynamic dimension has brought an extra challenge to the grid computing management of resource. Today, grid computing has reached the state of maturity, but its practicality, usability and demands have encouraged its continuous improvement. Performance is the most famous topic researched on right from the beginning until now. Some focus areas of grid computing performance are scheduling and load balancing which implicate issues related to delay, makespan time and job distribution.

Foster and Kesselman (1999) reported that scheduling is one of the challenging issues in a grid environment. According to Buyya et al. (2000), scheduler is responsible for discovery, resource selection and job assignment in grid. Furthermore, Buyya et al. (2002) added that resource management and scheduling systems for grid computing need to manage resources and application execution depending on resource consumers and owners requirements, and they need to continuously adapt to changes based on resource availability. The statements cited by Foster and Kesselman (1999) and Buyya et al. (2002) highlight the significance, importance and role of scheduling in preserving and improving grid computing performance.

Another important aspect that can contribute to the improvement of grid computing performance is load balancing. Coulouris et al. (1994) clarified that an important issue in grid environment is performance degradation due to load imbalance. Kabalan et al. (2002) emphasised on the fact that more focus should be put on resource management so as to avoid overloading and idling issues. Li and Lan (2004) reported that load balancing issues had been studied extensively because of its critical role in grid computing. Initial load possesses by each resource represent an amount of work to be performed and each might have a different processing capacity. Thus, to reduce the time needed to perform all tasks, the job has to be evenly distributed (Yagoubi et al., 2006). Yagoubi and Slimani (2007) explained that the biggest obstruction for distributed computing to achieve the targeted goals for job distribution on multiple hosts was the absence of an effective algorithm.

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The statements discussed above are closely related to and implicitly explain the items listed under grid performance studies. An important aspect in grid computing performance studies is resource management which can be divided into two components. The first component is scheduling and while load balancing makes up the second component. The combination of both components that complement each other will give more significant results. In other words, the statements discussed by Yagoubi and Slimani (2007), Li and Lan (2004), Kabalan et al. (2002), and Coulouris et al. (1994) described and proved the importance of scheduling and the significance of load balancing in grid computing, which need to be further explored to improve grid computing performance. Essentially, scheduling and load balancing are placed under the Grid Resource Management System, which also hosts Resource Broker, Meta Scheduler and other grid services. Generally, this research focuses on Grid Resource Management and Resource Broker because both the functions are important components to grid scheduling and load balancing. To be more specific, this research addresses the grid computing performance on resource management, resource broker, resource scheduling, network component and algorithm for the middleware in the grid environment. In particular, it discusses factors which can help to improve grid performance. Contributions are made to performance problems in job scheduling, load balancing, resources brokering and resource prioritisation. The approach taken in this research in order to address the issue of performance is to design and adapt resource broker and scheduling algorithm for grid environment. Resource broker discusses the multi-broker concept and usage, which is adopted into the hierarchical grid environment within single grid structure that commonly services the multi-VO’s grid environment. The scheduling and load balancing algorithm was used as a platform to define the policies in determining a suitable destination for each of the job. Thus, the objective of this work is to improve the performance of grid computing in terms of delay on job submission, makespan time and job distribution. For this purpose, a new framework was introduced to accommodate the requirement of this research. 1.2 Research Motivation The impacts of a proper and suitable algorithm for scheduling and load balancing are very promising in improving grid computing performance. The important part in developing the algorithm is the embedded strategies that can enhance the performance of grid computing. These are the challenges

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encountered in inventing the new scheduling algorithm in grid computing performance. The evolution of scheduling and load balancing algorithm began with static to dynamic strategies. It has been proven that dynamic strategy increased the abilities of resource management in grid computing. The dynamic characteristic is supported by the responsiveness of changes in grid resource. In addition, the consideration of resource items such as the CPU speed, the memory capacity and network bandwidth capacity have granted more reliable determination of computing power. In order to prove the arguments discussed in this section, this research focuses on the dynamic algorithm strategy due to its advantages over the static strategy. Then, the resource items discussed will be inserted as the source criteria in measuring the computing power of each node. The novelty of this research lies in the proposed resource broker extension, resource computing power reallocation and self-adaptive determination features. 1.3 Problem Statement The importance and capability of scheduling and load balancing to enhance the grid computing performance has been discussed in Section 1.1. The main issues in grid scheduling are related to grid resource management and resource broker management (Buyya et al., 2002; Foster and Kesselman, 1999). Additionally, Coulouris et al. (1994) raised the issue of load balancing, Kabalan et al. (2002) has explained the issue of overloading and idling issues, Li and Lan (2004) explained that load balancing has a critical role in grid. Therefore, this research focuses on solving these issues so as to enhance the grid computing performance. The ”improve Hierarchical Load Balancing Algorithm” (iHLBA) (Lee et al., 2011) was chosen as the benchmark algorithm. This selection was based on:

i. The iHLBA is focusing into both components, which are scheduling and load balancing.

ii. The iHLBA is based on dynamic characteristics which align with this research.

iii. The iHLBA has been proven to overcome the performance of Random algorithm, Most Fit Task Scheduling Algorithm (MFTF) and Ant Colony Optimisation (ACO) scheduling.

The iHLBA scheduling algorithm has been tested and the first issue found is the delay that occurs on job submission. This was due to the duration of resource broker that was tied to the job until the destination node was found.

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The resource broker would only be released after the job had been submitted to the destination node. Therefore, this issue must be addressed properly to obtain the targeted performance. The second issue is that the receiving process on the destination node has been followed through a pre-defined setting without considering any adjustment to the computing power items such as the CPU, memory and network bandwidth. As a consequence, this has affected the overall computing performance. Therefore, the resource items such as the CPU, memory and network bandwidth allocation on the destination node must be adjusted before submitting the job. The third issue is the distribution balance which is important to justify the fairness usage of the resources in the grid computing and reduce the idle resource number. This has also contributed to performance degradation. The lower gap between higher utilisation and lower utilisation rate of resources illustrates that balancing has more fairness rate. Meanwhile, the higher gap in resource utilisation indicates that there are more resources in idle state. In other words, this condition shows that resource utilisation is imbalance. This indirectly affects the overall performance of grid computing. Thus, the distribution of job into the grid resources must be explored to gain higher fairness rate compared to the performance of iHLBA. All of the issues discussed in this section are related to problem being focused in this research, especially on improving the grid computing performance. Briefly, these issues are related to grid resource management and resource broker management. It is believed that success in overcoming all these issues will contribute to the performance improvement. 1.4 Research Objectives This research focuses on grid computing performance which specifically addresses the delay, makespan time and load balancing issues. The propose design strategy was implemented through policies embedded into a particular algorithm. There are several strategies and algorithms proposed to reduce the delay, makespan time and load balancing issues in this research. Thus, the objectives of this research are:

i. to propose a new framework design for hierarchical grid computing. ii. to propose a new scheduling algorithm in an adaptive dynamic resource

broker management.

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iii. to propose the design of the Self-Adaptive Broker Manager with the capability to identify suitable broker instances number through self-determination features based on the resource and job information.

iv. to propose a new load balancing algorithm by introducing Broker Instance which is to enhance the load balancing performance of grid computing in a hierarchical environment.

v. to propose Dual Layer Computing Allocation algorithm for enhancing computing power capability of the destination node and reducing makespan time.

The algorithm called iHLBA (Lee et al., 2011) was selected to be the base work for this research to analyse the new algorithm. Herein, the primary goal is to improve the performance by reducing makespan time, minimising delay in job submission which will also cut down grid queuing time and also reducing the gap in distribution rate between the higher and lower utilisation resources. 1.5 Research Scope The experiment was carried out on the hierarchical cluster grid environment as a base platform for both of the algorithms. Logically, the grid environment setup in this research has the grid portal as a communication gateway for user. Grid Information Server (GIS) provides information to the entire grid and scheduling algorithm acts as the policy provider. Finally, there are ten (10) clusters in this grid and each cluster consists of ten (10) computing elements that serve as worker nodes. This environment is based on the iHLBA setup (Lee et al., 2011). This research was carried out through grid simulation. The platform used in this research was Netbeans IDE version 7.1.1, supported by GridSim Toolkits version 5.2, to enable the grid system environment. Finally, the proposed algorithms was design based on the Java Programming Language. Meanwhile, the job used in this research was based on the format of Grid Workload Archive (GWA) as presented by Iosup et al. (2006), which was created through the GridSim function. 1.6 Research Contributions

The main contribution of this research is propose design of a new scheduling algorithm for the hierarchical grid computing environment with an improved performance. Meanwhile, among the novel features of the proposed algorithm are:

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i. A new framework for scheduling the job in hierarchical grid computing. ii. The Self-Adaptive Broker Manager algorithm with the capability to

determine the broker instance number so as to facilitate each request through an embedded formula.

iii. The Broker Instance algorithm with the capability to assist the resource broker management in deciding the destination node for each job.

iv. The Dual Layer Computing Allocation algorithm with the capability to adapt to suitable resource items allocation and provide more computing power to each applicable node.

1.7 Thesis Organisation The remaining parts of the thesis are organised as follows: Chapter 2 provides a review of the grid resource management and scheduling and load balancing type available under grid computing. The current grid scheduling and load balancing algorithms proposed by previous researchers are also presented in this chapter. An introduction to and a discussion on the benchmark algorithm underlying this research will also be given in the same chapter. Chapter 3 presents the methodology applied into this research. Generally, this research was undertaken in four (4) phases. These phases are the preliminary study, system analysis phase, design of the model as a computer programme and discussion of the new system component design, and experimentation and analysis of the model. Finally, the parameters engaged in this research will also be discussed in this chapter. Chapter 4 offers some recommendations of the scheduling algorithm for this thesis. The scheduling algorithm called Multi-Instance Broker Scheduling Algorithm represents the main scheduling algorithm that is supported by other modules. The explanation began with the proposed framework. This is followed by the properties, characteristics and functional requirement of the MiBSA. In addition, Performance Recommender is also presented in this chapter. Chapter 5 discusses the first proposed module called Self-Adaptive Broker Manager which was developed in the form of algorithm. The discussion in this section includes the characteristics, properties and strategy used to implement this module. Results derived for makespan time and response time in relation to this algorithm will also be presented in this chapter. Chapter 6 is for discussion of the second module, namely Multi-Instance Broker, which was developed in the algorithm form. The discussion will include the characteristics, properties and strategy used for the implementation. The

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Multi-Instance Broker module contributes to job distribution and threshold performance. Results for both parts will also be presented in this chapter. Chapter 7 is designated for a discussion on Dual Layer Computing Allocation module. The Dual Layer Computing Allocation is the third module introduced in this research. This module is for computing power allocation adjustment and was developed in the algorithm form as well. A further discussion on this module will cover the characteristics, properties and strategy for the implementation.

Chapter 8 is dedicated for Conclusion and suggestions for future works in the same area. A conclusion to the current research will be given with the hope to shed lights on some directions that can be used for future work.

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