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A BALANCED PARTITIONING MECHANISM FOR MULTI- CONTROLLER PLACEMENT IN SOFTWARE-DEFINED
WIDE AREA NETWORKS
MAHER WALEED ASAAD SAAB
DOCTOR OF PHILOSOPHY UNIVERSITI UTARA MALAYSIA
2022
ii
Permission to Use
In presenting this thesis in fulfilment of the requirements for a postgraduate degree from Universiti Utara Malaysia, I agree that the University Library may make it freely available for inspection. I further agree that permission for the copying of this thesis in any manner, in whole or in part, for scholarly purpose may be granted by my supervisor(s) or, in their absence, by the Dean of Awang Had Salleh Graduate School of Arts and Sciences. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to Universiti Utara Malaysia for any scholarly use which may be made of any material from my thesis.
Requests for permission to copy or to make other use of materials in this thesis, in whole or in part, should be addressed to:
Dean of Awang Had Salleh Graduate School of Arts and Sciences UUM College of Arts and Sciences
Universiti Utara Malaysia 06010 UUM Sintok
iii Abstrak
Melalui penggunaan perisian, Rangkaian Tertakrif Perisian (SDN) dapat mengawal rangkaian. Menggunakan pengawal tunggal untuk menguruskan trafik rangkaian yang besar adalah tidak cekap; oleh itu, mempunyai beberapa pengawal adalah keperluan SDN semasa dalam rangkaian kawasan luas (WAN). Walau bagaimanapun, masalah penempatan pengawal (CPP) adalah subjek penyelidikan yang sedang berkembang pesat bagi menempatkan banyak pengawal dengan efisien untuk meningkatkan prestasi rangkaian. Ia mempunyai dua bahagian: cara pengawal perlu diagihkan dan berapa banyak peranti rangkaian setiap pengawal perlu disambungkan. Sehubungan itu, objektif kajian ini adalah untuk mencadangkan Mekanisme Pembahagian Seimbang (BPM) berdasarkan pembahagian rangkaian. Selain itu, BPM direka bentuk berdasarkan algoritma K-means yang diubah suai. BPM terdiri daripada dua pendekatan: kaedah permulaan dan strategi pembahagian. Kaedah permulaan titik terjauh telah diperkenalkan untuk mengurangkan kelewatan hujung ke hujung antara pengawal dan suis. Strategi pembahagian seimbang telah digunakan untuk mengimbangi beban pengawal dan membahagikan rangkaian ke dalam pembahagian seimbang. Penyelidikan ini menggunakan Metodologi Penyelidikan Sains Reka Bentuk (DSRM) untuk mencapai objektifnya. Simulator rangkaian OMNeT++
dikonfigurasikan untuk mensimulasikan prestasi BPM melalui topologi OS3E, dengan dua senario termasuk lima dan enam domain. Algoritma K-means dan CNPA, khususnya, digunakan untuk menilai prestasi BPM. Dari segi pembahagian seimbang, penemuan mendedahkan bahawa BPM mengatasi prestasi algoritma K-means dan CNPA dengan mengekalkan keseimbangan beban yang baik antara pengawal.
Tambahan pula, keputusan menunjukkan bahawa BPM meningkatkan daya pemprosesan dan mengurangkan kelewatan hujung ke hujung antara pengawal dan suis. Di samping itu, BPM menambah baik bilangan paket yang diterima oleh destinasi kepada bilangan paket yang dihantar, masing-masing sebanyak 23% dan 29%
berbanding K-means untuk lima dan enam senario domain. Memandangkan kepelbagaian Internet masa depan dan IoT, hasil penemuan menunjukkan implikasi yang ketara untuk meningkatkan prestasi rangkaian WAN.
Kata kunci: Rangkaian tertakrif perisian, Masalah penempatan Pengawal, Multi Pengawal, Rangkaian Kawasan Luas, Pengelompokan.
iv Abstract
Through softwarization, Software-Defined Networking (SDN) may govern the network. Deploying a single controller to manage enormous network traffic is inefficient; hence, having multiple controllers is a necessity of current SDN in wide area networks (WANs). However, the controller placement problem (CPP) is a thriving research subject for efficiently placing many controllers to improve network performance. It has two parts: how the controllers should be distributed and how many networking devices each controller should be connected to. Consequently, the objective of this study is to propose a Balanced Partitioning Mechanism (BPM) based on the notion of a network partition. Moreover, the BPM is designed based on a modified K-means algorithm. BPM comprises of two approaches: the initialization method and the partitioning strategy. The farthest-point initialization method is introduced to reduce end-to-end delay between the controllers and switches. The balanced partitioning strategy is used to balance controller loads and partition the network into balanced partitions. The research adopted the Design Science Research Methodology (DSRM) to accomplish its objectives. The network simulator OMNeT++ was configured to simulate the performance of BPM over the OS3E topology, with two scenarios including five and six domains. The K-means and CNPA algorithms, in particular, were used to evaluate the performance of BPM. In terms of balanced partitioning, the findings reveal that BPM outperforms the K-means and CNPA algorithms by maintaining a good load balance among controllers.
Furthermore, the results show that BPM improves throughput and reduces end-to-end delay between the controllers and switches. In addition, BPM improves the number of packets received by the destination to the number of packets sent by 23% and 29%
compared to the K-means for five and six domain scenarios, respectively. Given the diversity of future Internet and IoT, the findings have significant implications for improving the performance of WAN networks.
Keywords: Software-defined networking, Controller placement problem, Multi- controller, Wide area networks, Clustering.
iv
Declaration Associated with This Thesis
Some of the works presented in this thesis have been published as listed below:
[1] Maher Saab, Shahrudin Awang Nor, Yousef Fazea “Software-Defined Networking and OpenFlow Technologies: Challenges and Future Directions of Programmable Networks”, Journal of Advanced Research in Dynamical and Control Systems, 12(2):809-817. 2020. DOI:10.5373/JARDCS/V12I2/S20201100. (Scopus Indexed).
[2] Maher Saab, Shahrudin Awang Nor, Yousef Fazea, Mohamed Elshaikh “Multi- Controller Placement Problem in Software-Defined Networking: A Survey”, 6th International Conference on Internet Applications, Protocols and Services (NETAPPS2020).
[3] Maher Saab, Shahrudin Awang Nor, Yousef Fazea “Multi-Controller Scalability in Software-Defined Wide Area Network: A Critical Review”, International Journal of Computing and Mathematics, Volume: 5 Issue: 4 December 2021.
v
Acknowledgement
In the name of ALLAH, Most Gracious, Most Merciful. Throughout my studies at the Universiti Utara Malaysia, I have been fortunate enough to have the help and support of many people. I would like to convey my heartfelt gratitude to Dr. Shahrudin bin Awang Nor, my supervisor, for his knowledge, advice, and support. His vast knowledge and rational style of thinking have been really beneficial to me. His patience, support, and personal mentorship have served as a solid foundation for this thesis. My heartfelt thanks also goes out to Dr. Yousef Fazea, my co-supervisor, for his constructive comments and unwavering support during my studies. He is always willing to meet with me, even on weekends and outside hours for consultations as well sharing his experience in providing an important reading materials that have greatly aided me in my studies. You have gone out of your way to help me in many circumstances. I owe you a lot. Further, my truthful acknowledgement goes to Dr.
Mohamed Elshaikh (Universiti Malaysia Perlis) for his encouragement, motivation, insight discussion and his help on the implementation of BPM in OMNeT++. Besides my supervisors, I owe my greatest thanks to my parents, who are the source of my success, for their unending love, support, and encouragement. I am also indebted to my sisters, brothers, and friends for their unwavering love, prayers, care, and sacrifices in educating and preparing me for the future. Finally, I want to express my heartfelt gratitude to my son, my beautiful diamond, Aws, who has missed me greatly throughout my journey in Malaysia.
vi
Table of Contents
Permission to Use ... ii
Abstrak ... iii
Abstract ... iv
Acknowledgement ... v
Table of Contents ... vi
List of Tables ... x
List of Figures ... xi
List of Abbreviations ... xv
INTRODUCTION ... 1
1.1 Towards Software-Defined Networking ... 3
1.1.1 Overview of Software-Defined Networking ... 4
1.1.2 Multi-Controller Placement Problem ... 7
1.2 Research Motivation ... 10
1.3 Research Problem ... 12
1.4 Research Questions ... 14
1.5 Research Objectives ... 14
1.6 Research Scope ... 15
1.7 Research Steps ... 15
1.8 Research Framework ... 16
1.9 Significance of the Research ... 17
1.10 Organization of the Thesis ... 18
LITERATURE REVIEW ... 21
2.1 Background of programmable networks ... 22
2.2 Software-Defined Networking ... 23
2.2.1 Software-Defined Networking Components ... 25
2.2.1.1 SDN Application Plane ... 26
2.2.1.2 SDN Control Plane ... 29
2.2.1.3 SDN Data Plane ... 32
vii
2.2.2 Communication between the control plane and the data plane
Standardization ... 33
2.3 OpenFlow-based SDN Architecture ... 38
2.4 Graph Partitioning Algorithms ... 44
2.4.1 K-means Clustering Algorithm ... 46
2.4.2 K-center Clustering Algorithm ... 49
2.4.3 K-medoids Clustering Algorithm ... 50
2.5 Multi-Controller Placement Problem in WAN SDN ... 52
2.6 Summary ... 66
RESEARCH METHODOLOGY ... 67
3.1 Research Approach ... 68
3.2 The First Phase: Problems Awareness ... 70
3.3 The Second Phase: Suggestion ... 72
3.4 The Third Phase: Development ... 76
3.5 The Fourth Phase: Evaluation ... 81
3.5.1 Performance Evaluation Techniques for Network System ... 81
3.5.1.1 Analytical Modeling ... 82
3.5.1.2 Measurement ... 83
3.5.1.3 Simulation ... 84
3.5.2 Evaluation Environment ... 86
3.5.2.1 OMNeT++ Simulator ... 88
3.5.3 Experimentation Design ... 89
3.5.4 Experiment Steps ... 93
3.5.5 Performance Metrics Selection ... 95
3.6 The Fifth Phase: Conclusion ... 97
3.7 Summary ... 97
... 99
4.1 Multi-controller Placement in WAN SDN ... 99
4.2 Standard Clustering Algorithms Based on Partition for CPP ... 107
viii
4.2.1 K-means Clustering Algorithm ... 108
4.2.2 K-center Clustering Algorithm ... 111
4.2.3 K-medoids Clustering Algorithm ... 112
4.3 Scope of Performance ... 113
4.4 Simulation Environment ... 115
4.5 Simulation Experiments ... 117
4.5.1 Performance Over Five Controllers ... 118
4.5.1.1 K-means Clustering Algorithm ... 118
4.5.1.2 K-center Clustering Algorithm ... 121
4.5.1.3 K-medoids Clustering Algorithm ... 123
4.5.2 Performance Over Six Controllers ... 125
4.5.2.1 K-means Clustering Algorithm ... 126
4.5.2.2 K-center Clustering Algorithm ... 128
4.5.2.3 K-medoids Clustering Algorithm ... 131
4.6 Discussion and Evaluation ... 133
4.6.1 Number of Nodes ... 134
4.6.2 Maximum End-to-End Delay ... 135
4.7 Summary ... 137
... 138
5.1 Introduction ... 139
5.2 Initialization Method for K-means Algorithm ... 139
5.3 Balance Partition Strategy for K-means Algorithm ... 142
5.4 Modified Standard K-means Clustering Algorithm ... 144
5.5 Description of Experiments and Rationale ... 148
5.6 Verification of the Standard K-means Clustering Algorithm Modification ... 148
5.7 Validation of the Standard K-means Clustering Algorithm Modification ... 149
5.8 Summary ... 159
... 161
ix
6.1 Introduction ... 161
6.2 Simulation Environment ... 162
6.3 Performance Evaluation of BPM ... 167
6.3.1 Performance Evaluation of BPM versus K-means Algorithm ... 167
6.3.1.1 Performance Over Five Controllers ... 169
6.3.1.2 Performance Over Six Controllers ... 174
6.3.2 Performance Evaluation of BPM versus CNPA Algorithm ... 180
6.3.2.1 Performance Over Five Controllers ... 181
6.3.2.2 Performance Over Six Controllers ... 183
6.4 Discussion on BPM Performance ... 186
6.4.1 BPM versus Standard K-means Algorithm ... 187
6.4.2 BPM versus CNPA algorithm ... 188
6.5 Summary ... 189
... 191
7.1 Summary of the Research ... 191
7.2 Research Contributions ... 194
7.3 Research Limitation ... 195
7.4 Future Works ... 196
REFERENCES ... 198
x List of Tables
Table 1.1 Software-defined networking (SDN) vs conventional network ... 7
Table 2.1 SDN Applications ... 29
Table 2.2 Comparison of OpenFlow controllers ... 31
Table 2.3 OpenFlow Versions with Switch Specification Improvements ... 43
Table 2.4 Literature Review Table ... 64
Table 2.5 Literature Review Table (continued)………..65
Table 3.1 Comparison Between Different Evaluation Approaches ... 86
Table 3.2 OS3E Nodes Indices ... 91
Table 4.1 Parameters Used in the Simulation ... 116
Table 4.2 Bandwidth Types Used in the Simulation ... 116
Table 4.3 Comparison of K-means, K-center, and K-medoids Algorithms in terms of Imbalance Partition and Average Maximum End-to-End Delay ... 134
Table 5.1 Pseudocode of the Modified K-means clustering algorithm ... 147
Table 5.2 Parameters Used in the Simulation ... 151
Table 5.3 Bandwidth Types Used in the Simulation ... 152
Table 6.1 Parameters Used in the Simulation ... 168
Table 6.2 Bandwidth Types Used in the Simulation ... 168
xi List of Figures
Figure 1.1: Conventional Network vs Software-Defined Networking [6] ... 4
Figure 1.2: Simplified view of the Software-Defined Networking Architecture [20] . 5 Figure 1.3: Single Controller Approach ... 8
Figure 1.4: Multi-Controller Approach ... 9
Figure 1.5: Multi-Controller Location in WAN SDN ... 10
Figure 1.6: Research Scope ... 15
Figure 1.7: Research Framework ... 17
Figure 2.1: Basic SDN Architectural Components [70] ... 26
Figure 2.2: P1520 Reference Model ... 34
Figure 2.3: Example SoftRouter Network ... 35
Figure 2.4: ForCES Model ... 36
Figure 2.5: SDN OpenFlow Components ... 39
Figure 2.6: Flow Table in OpenFlow ... 40
Figure 2.7: Flow Table Entry for OpenFlow ... 40
Figure 2.8: Components OpenFlow-Compliant Switch [86] ... 41
Figure 2.9: An Example of the k-way partition problem ... 45
Figure 2.10: An Example of the K-means Clustering Algorithm ... 48
Figure 2.11: An Example of the K-center Clustering Algorithm ... 50
Figure 2.12: An Example of the K-medoids Clustering Algorithm ... 51
Figure 2.13: Split WAN SDN into Four Domains ... 55
Figure 3.1: Research Approach ... 70
Figure 3.2: Main Activities Involved in Problems Awareness Phase ... 71
Figure 3.3: Main Activities Involved in Suggestion Phase ... 73
Figure 3.4: Conceptual Model of BPM ... 75
Figure 3.5: Main Steps in the Development Phase ... 77
Figure 3.6: Performance Evaluation Techniques ... 81
Figure 3.7: OS3E Simulation Topology ... 90
Figure 3.8: Simulation Steps (Adopted from [147]) ... 94
xii
Figure 4.1: The end-to-end delay of packet transmission in SDN ... 101
Figure 4.2: An example of network partition and the connection map between the controllers and the switches ... 105
Figure 4.3: The Basic Flowchart of Standard K-means Clustering Algorithm ... 109
Figure 4.4: The Basic Flowchart of the K-center Clustering Algorithm ... 112
Figure 4.5: The Basic Flowchart of the K-medoids Clustering Algorithm ... 113
Figure 4.6: OS3E topology to investigate the performance of the standard clustering algorithms ... 114
Figure 4.7: Partitioning OS3E topology using the standard K-means into five domains ... 119
Figure 4.8: The maximum end-to-end delay of OS3E for 100 different runs with five partitions by the standard K-means ... 120
Figure 4.9: Partitioning OS3E topology using the standard K-center into five domains ... 122
Figure 4.10: The maximum end-to-end delay of OS3E for 100 different runs with five partitions by the standard K-center ... 122
Figure 4.11: Partitioning OS3E topology using the standard K-medoids into five domains ... 124
Figure 4.12: The maximum end-to-end delay of OS3E for 100 different runs with five partitions by the standard K-medoids ... 125
Figure 4.13: Partitioning OS3E topology using the standard K-means into six domains ... 127
Figure 4.14: The maximum end-to-end delay of OS3E for 100 different runs with six partitions by the standard K-means ... 128
Figure 4.15: Partitioning OS3E topology using the standard K-center into six domains ... 129
Figure 4.16: The maximum end-to-end delay of OS3E for 100 different runs with six partitions by the standard K-center ... 130
Figure 4.17: Partitioning OS3E topology using the standard K-medoids into six domains ... 132
xiii
Figure 4.18: The maximum end-to-end delay of OS3E for 100 different runs with six partitions by the standard K-medoids ... 133 Figure 5.1: K-means Modification Flowchart ... 146 Figure 5.2. Implementation of K-means clustering algorithm modification in
OMNeT++ ... 149 Figure 5.3: OS3E Simulation Topology of Modified K-means Algorithm ... 150 Figure 5.4:Demonstration of the network partition of the Internet2 OS3E when the value of K from 1 to 6 ... 157 Figure 5.5: Maximum end-to-end delay comparison with different number of
controllers ... 158 Figure 5.6: Throughput comparison with different number of controllers ... 159 Figure 6.1: OMNeT++ Simulation Environment with OpenFlow Module for BPM ... 164 Figure 6.2: OMNeT++ Life Cycle in SDN ... 166 Figure 6.3: Placement of BPM and Standard K-means on OS3E (1) BPM K = 5 and (2) K-means K = 5 ... 169 Figure 6.4: Comparison maximum end-to-end latency CDFs for K= 5 in BPM and K-means ... 170 Figure 6.5: Average maximum end-to-end delay of the K = 5 scenario for BPM and K-means ... 171 Figure 6.6: Throughput in K = 5 scenario for BPM and K-means ... 172 Figure 6.7: PDR in K = 5 scenario for BPM and K-means ... 173 Figure 6.8: Domain imbalance partition in K = 5 scenario for BPM and K-means 174 Figure 6.9: Placement of BPM and Standard K-means on OS3E (1) BPM K = 6 and (2) K-means K = 6 ... 175 Figure 6.10: Comparison maximum end-to-end latency CDFs for K = 6 in BPM and K-means ... 176 Figure 6.11: Average maximum end-to-end delay of the K = 6 scenario for BPM and K-means ... 177 Figure 6.12: Throughput in K = 6 scenario for BPM and K-means ... 178
xiv
Figure 6.13: PDR in K = 6 scenario for BPM and K-means ... 179 Figure 6.14: Domain imbalance partition in K = 6 scenario for BPM and K-means ... 180 Figure 6.15: Placement of BPM and CNPA on OS3E (1) BPM K = 5 and (2) CNPA K = 5 ... 181 Figure 6.16: Average maximum end-to-end delay of the K = 5 scenario for BPM and CNPA ... 182 Figure 6.17: Domain imbalance partition in K = 5 scenario for BPM and CNPA .. 183 Figure 6.18: Placement of BPM and CNPA on OS3E (1) BPM K= 6 and (2) CNPA K = 6 ... 184 Figure 6.19: Average maximum end-to-end delay of the K = 6 scenario for BPM and CNPA ... 185 Figure 6.20: Domain imbalance partition in K = 6 scenario for BPM and CNPA .. 186
xv
List of Abbreviations
AN Active Networks
API Application Programming Interface
CE Control Element
CNPA Clustering-based Network Partition Algorithm
CPP Controller Placement Problem
DBCP Density Based Controller Placement
DCPP Dynamic Controller Placement Problem
DSRM Design Science Research Methodology
FE Forwarding Element
ForCES Forwarding and Control Element Separation
FTCPP Fault Tolerant Controller Placement Problem
GbE Gigabit Ethernet
GUI Graphical User Interface
IEEE Institute of Electrical and Electronics Engineers
IETF Internet Engineering Task Force
ILP Integer Linear Programming
IRTF Internet Research Task Force
NBI Northbound Interface
NE Network Element
NOS Network Operating System
NOX Network Operating System for SDN
ONF Open Networking Foundation
PAM Partition Around Medoids
PDR Packet Delivery Ratio
POX Python based network Operating System
QoS Quality of Service
RAM Random Access Memory
SCPP Static Controller Placement Problem
SDN Software-Defined Networking
xvi
SDNRG Software-Defined Networking Research Group
SPoF Single Point of Failure
TCAM Ternary Content Addressable Memory
TCP Transmission Control Protocol
WAN Wide Area Networks
1
INTRODUCTION
“Any successful system must do what its designer wants it to do. If the system cannot meet this basic demand, it is meaningless to talk about any other thing” [1]. This statement demonstrates how recent technological advances in all sectors, in general, and in computer science and telecommunications, have necessitated the need to redefine Internet network architectures, since traditional ones have started to realize their weaknesses. This is the motivational paradigm for today’s research and development (R&D) community in industry, in academia, and for network operators.
The Internet is a worldwide computer network that connects hundreds of millions of computing devices. According to Cisco's Annual Internet Report [2] [3] [4], over 12 billion devices are already connected to the Internet, with a projected 30 billion devices by 2023. Therefore, future network architecture will necessitate software- based rather than hardware-based, cost-effective, and dynamic management systems [5] [6]. The vertical integration of the tightly coupled basic network planes, such as the control and data planes, is the primary point in current networks. The network traffic is handled by the control plane, while the data plane forwards the traffic based on the control plane's decision. The close coupling of these planes, on the other hand, reduces the network's flexibility and scalability, which is the primary motivation for academics to develop new technology that optimizes the network at a low cost.
Software-Defined Networking (SDN) [7] [8] has emerged as one of the most promising techniques in recent years.
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