vii
TABLE OF CONTENTS
CHAPTER
1
2
TITLE
PAGE
DECLARATION
ii
DEDICATION
iii
ACKNOWLEDGMENT
iv
ABSTRACT
v
ABSTRAK
vi
TABLE OF CONTENTS
viii
LIST OF TABLES
xi
LIST OF FIGURES
xii
LIST OF ABBREVIATIONS
xiv
INTRODUCTION
1.1
Introduction
1
1.2
Background of Problem
2
1.3
Problem Statement
4
1.4
Research Questions
5
1.5
Aim of study
5
1.6
Objectives of Study
6
1.7
Scope of the Study
6
1.8
Motivation
6
1.9
Organization of the Research
7
LITERATURE REVIEW
2.1
Introduction
8
2.2
Grid Computing
10
2.2.1 Classifying Grid Systems
11
viii
2.3
2.2.2 Grid Architecture
11
Resource Selection
13
2.3.1 Matchmaking-based Resource Selection
14
2.3.1.1 Resource Matchmaking in Grid
Semantically
14
2.3.2 Bidding Resource Selection
17
2.3.3 Learning Resource Selection
18
2.3.3.1 Reinforcement Learning
19
2.3.3.2 Extreme Learning Machine (ELM)
Based
19
2.3.3.3 Learning Automata
21
2.3.4 Decision Theory-based Resource Selection
Algorithms
22
2.3.5 Knowledge-Based Resource Selection
24
2.3.6 Task Partitioning
25
2.3.6.1 CPU Partitioning
26
2.3.6.2 Memory Partitioning
26
2.3.6.3 CPU- Memory Partitioning
27
2.4
Resource Selection Related Work
28
2.5
Ranking Based Approaches
30
2.5.1 Resource Matching Using Grid Resource
Vector
30
2.5.2 Reputation-based
33
2.6
2.5.2.1 Simple Feedback Reputation
34
2.5.2.2 Beta Feedback Reputation
34
2.5.2.3 Weighted Feedback Reputation
35
2.5.2.4 Beta Filtering Reputation
35
2.5.2.5 Beta Deviation Feedback
36
2.5.3 Ranking Based Resource Selection Summary
37
Simulation Tools
39
2.6.1 GridSim
39
2.6.1.1 GridSim Architecture
2.7
40
2.6.2 Other Grid Simulators
44
Summary
45
ix
3
RESEARCH METHODOLOGY
3.1
Introduction
47
3.2
Operational Framework
48
3.3
Literature Review
49
3.4
Problem Formulation
49
3.5
Design Proposed Algorithm
50
3.5.1 Information Collection
52
3.5.2 Job Partitioning
55
3.5.3 Update Ranks
57
Dataset
58
3.6.1 Gridlet
58
3.6.2 Resource
60
3.7
Evaluation of Proposed Algorithm
61
3.8
Assumptions
63
3.6
4
5
6
DESIGN AND IMPLEMENTATION
4.1
Introduction
64
4.2
Initial Ranking
64
4.4
Update Ranks
69
SIMULATION RESULTS AND DISCUSSION
5.1
Introduction
71
5.2
Simulation Model and Grid System
71
5.3
Simulation Setup
74
5.4
Experimental Results
75
5.5
Summary
89
CONCLUSION
6.1
Introduction
90
6.2
Conclusion
90
6.3
Future Works
92
REFERENCES
93
x
LIST OF TABLES
TABLE NO.
2.1
TITLE
PAGE
Summary of previous approaches resource selection
in grid computational
28
Summary of previous approaches ranking based
resource selection
37
Listing of functionalities and features for each
grid simulator
45
3.1
Gridlet Specification
56
3.2
Basic Resource Information and Attributes
57
3.3
Type of Resources
58
5.1
Simulation configuration for resources and jobs
75
5.2
Average success rate of RRS, GRV and MCTP in
three iteration with 100 and 1000 submitted jobs
80
Average waiting time for RRS, GRV and MCTP in
each iteration (s)
83
Maximum execution time on a resource in three
iterations with 500, 2000 and 5000 resources
86
2.2
2.3
5.3
5.4
xi
LIST OF FIGURES
FIGURE NO.
TITLE
PAGE
2.1
Literature Review Structure
9
2.2
The layered grid architecture and the internet protocol
architecture
12
Relationships between user, resource broker, information
service and resources
15
2.4
Semantic matchmaking algorithm
16
2.5
Algorithm for time optimization
17
2.5
(a) The abstract process of the matchmaking model.
(b) The abstract process of the bidding model
18
2.6
ELM-based resource selection algorithm
21
2.7
Resource selection algorithm using Learning Automata
on economic grid by considering cost optimization
22
2.8
MASK architecture
23
2.9
CPU-Memory Partition Method
27
2.10
Schema of Metascheduler using Grid Resource Vector
31
2.11
Integrated GRV algorithm
33
2.12
Beta Deviation Feedback Algorithms
36
2.13
GridSim Architecture
42
2.14
Sequence diagram of simulation grid environment
on GridSim
43
Interactions between entities in GridSim using their
I/O entities
44
3.1
Flow chart of operational framework
48
3.2
Design of Proposed Method
52
4.1
Average Total Completion Time with different influence
ratio of processing power and network rate
63
Waiting time of proposed algorithm
57
2.3
2.15
4.2
xii
5.1
5.2
5.3
5.4
5.5
5.6
5.7
5.8
5.9
5.10
5.11
5.12
5.13
5.14
5.15
Comparison of job success rate between RRS, GRV
and MCTP with 500 resources in first iteration for 100 jobs
76
Comparison of job success rate between RRS, GRV
and MCTP with 2000 resources in second iteration for 100 jobs
77
Comparison of job success rate between RRS, GRV
and MCTP with 5000 resources in third iteration for 100 jobs
77
Comparison of job success rate between
RRS, GRV and MCTP with 500 resources in first iteration
78
Comparison of job success rate between
RRS, GRV and MCTP with 2000 resources in second iteration
78
Comparison of job success rate between
RRS, GRV and MCTP with 5000 resources in third iteration
79
Comparison between waiting time of RRS, GRV and MCTP
in first iteration with 500 resources
81
Comparison between waiting time of RRS, GRV and MCTP
in first iteration with 2000 resources
82
Comparison between waiting time of RRS, GRV and MCTP
in first iteration with 5000 resources
82
Comparison between average execution time of RRS, GRV
and MCTP in first iteration with 500 resources
84
Comparison between average execution time of RRS, GRV
and MCTP in first iteration with 2000 resources
Comparison between average execution time of RRS, GRV
and MCTP in first iteration with 5000 resources
85
85
Comparison between completion time of RRS, GRV and
MCTP with 500 resources
87
Comparison between completion time of RRS, GRV and
MCTP with 2000 resources
88
Comparison between completion time of RRS, GRV and
MCTP with 5000 resources
88
xiii
LIST OF ABBREVIATIONS
API
Application Programming Interface
BP
Back Propagation
CA
Custom Attribute
CFP
Call For Proposal
ELM
Extreme Learning Machine
FCFS
First Come First Serve
GRV
Grid Resource Vector
GIS
Grid Information Service
IBL
Instance Based Learning
JVM
Java Virtual Machine
JS
Job Success
LA
Learning Automata
LSF
Load Sharing Facility
MASK
Multi-Agent System broKer
MI
Million Instructions
MIPS
Million Instructions Per Second
OGSA
Open Grid Service Architecture
OS
Operation System
xiv
PBS
Portable Batch Scheduler
PE
Processing Elements
QoS
Quality of Service
RA
Resource Availability
RMS
Resource Management System
RR
Round Robin
SDK
Software Development Kit
SLFN
Single-hidden Layer Feedforward Neural Networks
SLA
Service Level Agreements