This document provides an overview of a proposed research project on designing an energy efficient and secure service framework for software defined networks. It begins with an introduction and background on software defined networks and edge computing. It then discusses the proposed research questions, objectives, methodologies, and work plan. The literature review identifies gaps in existing research related to resource management, traffic forwarding, delays, latency, mobility, energy consumption, and data privacy in SDNs. The proposed methodologies include designing algorithms and frameworks, testing and validation, and publishing research papers.

1. Design of an Energy Efficient and Secure
Service Framework for Software Defined
Networks
Presented by:
Lokesh Pawar
21YCS1004
(Department of Computer Science & Engineering )
LOKESH PAWAR
Co-Supervisor:
Dr. Rohit Bajaj
(Associate Professor, CSE)
CSE 1
Supervisor :
Dr. Gaurav Bathla
(Professor, CSE)

2. Table of Contents
• Introduction
• Software Defined Networks
• Model and Types of SDN
• Software Defined Edge Computing
• Applications of SDN
• Literature Review
• Research Gaps
• Research Questions
• Objectives
• Methodologies
• Work Plan
• References
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3. Introduction: Traditional Networking
• The exponential growth in demand of service from the server.
• Incapability of the traditional network infrastructure in providing
– Ubiquitous Access
– Dynamic Management
– Service Request Failures
– Reconfiguration of the Network
• To avoid these issues cloud infrastructure is utilized.
• The cloud utilization brings in few other challenges:
– Latency
– Cost
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Figure 1: Traditional Cloud Network

4. Software Defined Networks
• SDN is software oriented and runs on the top of the control plane.
• The traditional network is hardware oriented [1].
• SDN can resolve the issues evolved in cloud and traditional infrastructure.
• It has a capability to communicate with the underlying hardware and can manage
the traffic.
• It also provides a new way of managing and controlling the data packets with the
help of centralized multiple servers.
• SDN allows administrators to :
– Control the Network
– Customization
– Resource Management
– Network Capacity
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5. Software Defined Networks
• SDN is divided into three planes:
– Data Plane
– Control Plane
– Application Plane
• Data Plane is responsible for collection, storage
and routing of the data [1].
• Control Plane is responsible for handling the
Control Decisions, Control Synchronizations
and Resource Management.
• Application Plane is responsible for intrusion
detection and load balancing.
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Figure 2: SDN Architecture [1]

6. Model and Types of SDN
• Models of SDN
– Open SDN: This is implemented at the data plane level and it’s a protocol suite for
controlling the switches.
– SDN by API: Instead of using open protocol API’s controls the movement of the data
through the devices in the network.
– SDN Overlay: It runs on the top of existing hardware without disturbing the physical
network.
– Hybrid SDN: A combined service is provided where traditional protocols caters routine
traffic and SDN caters other traffic.
• Types of SDN
– Single Controller (Central SDN): Single controller is used to manage all the Control
Synchronizations, Control Decisions and Resource Management.
– Multiple Controller(Hybrid): Multiple Controllers are used to manage all the Control
Synchronizations, Control Decisions and Resource Management.
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7. Software Defined Edge Computing
• SDEC is implemented on edge services present in the application layer of SDN.
• SDEC is decoupled into Software Defined Edge Devices, Software Defined Edge
Storage and Software Defined Edge Computing Resources.
• They are abstraction and definition for terminal devices edge storage resources and
edge resource management [2].
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Figure 3: Component View of SDEC [2]

8. Applications of Software Defined Network
• Network Monitoring
• Security
• High Performance
• Energy management
• Virtualization
• Intelligent Routing
• Reconfiguration Network
• Dynamic
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9. Literature Review
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Author & Year Findings Journal
Chen J. et al. [18] &
2022
• Designed an automatic load balancing architecture
which is based on reinforcement learning.
• The optimal path is determined by using deep
deterministic policy gradient.
• Pytorch is utilized to implement the RL.
WCMC, Hindawi
Ali J. et al. [28] &
2022
• Clustering technology is used for managing the
network.
• Multi Criteria Decision Making technique was also
used along with Clustering technique.
• This technique was not able to focus on energy and
load balancing, but it is scalable and no latency.
Sensors, MDPI
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10. Literature Review
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Author & Year Findings Journal
Babbar H. et al. [14]
& 2021
• Single Controller can not manage the heavy traffic
due to the restricted capability.
• The author utilized migrating switches technique for
proposing a scalable load balancing algorithm.
• Multi Controller environment was utilized to reach
an optimized solution.
Sustainability,
MDPI
Simoes R. et al. [27]
& 2021
• Network function virtualization technique was used
to reach to optimal solution.
• This technique focused on energy efficiency, load
balancing, security, network quality and it is robust
as well.
Future Internet,
MDPI

11. Literature Review
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Author & Year Findings Journal
Nsaif M. et. al.[29]
& 2021
• Authors used an Integer Programming Model
• This technique focused upon Energy efficiency,
load balancing and network quality performance.
Electronics,
MDPI
Lee S. et al. [20] &
2020
• They have proposed a tool which uses fuzzing
technologies for discovering unidentified attacks.
• Author believes that present studies are not
sufficient for discovering security ambiguities.
Computers &
Security, Elsevier
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12. Literature Review
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Author & Year Findings Journal
Hayjneh A. et al.
[23] & 2020
• A system model for effective usage of SDN was
presented.
• Also drawn the attention on mitigating the
masquerading attack.
Computers,
MDPI
Hosny W. et al. [16]
& 2019
• Proposed an algorithm for controlling and adaptive
load balancing in SDN.
• Compared the algorithm on the basis of throughput
and Response Time.
JCNC, Hindawi

13. Literature Review
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Author & Year Findings Journal
Gebremariam A.
[6] & 2018
• Proposed an algorithm for resource slicing where
first respondents can share their activities.
• They have used Stochastic Geometric tool for
building a model.
• Low latency emergency services can be availed
using the model and algorithm so proposed.
Wireless
Communic
ation and
Mobile
Computing,
Hindawi
Hu Y. et al. [8] &
2017
• An algorithm has been given by the author for
controlling the congestion.
• A new method was proposed to judge the node
congestion.
• Write Name of the method
Scientific
Programmi
ng,
Hindawi
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14. Literature Review
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Author & Year Findings Journal
Neves P. et al. [10]
& 2016
• Heterogeneous Traffic induces performance
challenges.
• The Author has proposed a novel framework using
NFV and SDN.
IJDSN,
Hindawi
Ahmad I. et al.[24]
& 2015
• It is a survey article, deals in security threat and
challenges in SDN.
• SDN increases the visibility of the network hence
security can be easily managed.
• Author has shown concern on Future challenges
as well.
IEEE
Communic
ation
Surveys &
Tutorials

15. Literature Review
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Author & Year Findings Journal
Zhong H. et al.
[15] & 2015
• Given an efficient load balancing scheme based
on variance analysis.
• Open Flow Switching Technology was utilized
to achieve the results.
• The whole work was conducted with Single
Controller.
• Obtained results were low cost, increased
reliability and scalable.
Mobile
Information
System,
Hindawi
Moshref M. et al.
[11] & 2014
• Described a new framework which
dynamically balances the resources.
• The proposed algorithm does not use apriori
approach.
• It dynamically searches for sufficient resource
with desired accuracy.
SIGCOMM’1
4, ACM.
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16. Research Gaps
• Efficient Management of Limited Resources [3][5][9]
• Traffic Forwarding for Definitive Service Delivery[7][12]
• Combating Network Delays[17][25]
• Latency Management[18][20]
• Seamless Mobility[1][2][29]
• Energy Consumption[5][9][28]
• Data Privacy[21][25]
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17. Research Questions
• How the allocation of the available resources to the requesting nodes using
a resource allocation strategy can be done.
• How Quality of service enhancement can be managed for application layer
devices where lower-latency and high data rate is required by the
devices.(limiting to processing, storage and energy).
• The maintenance of traffic flow and balancing the load of the network at
the time of huge traffic generation, certain strategies can be framed to
manage the traffic flow and balance the load.
• How the authentication challenges be served for reliable networks.
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18. Objectives
• To study and analyze the existing schemes for energy efficient and secure
Software Defined Networks.
• To design and implement an energy efficient and secure framework for
Software Defined Networks.
• To evaluate and validate the performance of the proposed framework.
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19. Methodology 1-Research Design
Methodology for Objective 1
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Figure 4: A Taxonomy for SDEC

20. Methodology 2-Research Design
• An optimized load balancing scheme will be introduced for providing
effective services in software defined edge computing.
• The incoming data packets to the switch are sent to the distributed SDN
controllers, these controllers have an access to the state of the art lower
layers and upper layers to take the forwarding decisions.
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Figure 5: Generalized Multi-Controller Oriented Model

21. Methodology 3-Research Process
• To achieve this objective, a multi-controller Software Defined Network
(SDN) framework will be created. A policy based attack detection (Pbad)
mechanism will be ran on the top of each Software Defined Network
Controller (SDNC).
• This policy based attack detection mechanism will be implemented in the
northbound interface of Software Defined Network Controller (SDNC).
Every Autonomous System will be managed and controlled by SDN
Controller.
• With the help of policy based attack detection (Pbad) the attacks can be
traced and actions can be taken to mitigate the potential attacks.
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22. Methodology 3-Research Process
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Figure 5: A System Model

23. Methodology 4-Research Process
• To achieve this objective the proposed scheme and framework will be
tested for accuracy and efficiency using realistic parameters.
• The scheme comprises of SDN and edge nodes so the proposed scheme
can be simulated on:
– Mininet
– Network Simulator (NS-3)
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24. Research Paper
• Paper Submitted and under review: “Binary Tree Based Data Gathering
Routing Scheme for Wireless Sensor Networks”
• Paper Communicated: In Wireless Personal Communication: “A
bibliographic review on SDN and Edge Computing”
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25. Work Plan
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A: Literature Review and Final Synopsis Submission.
B: Designing of Algorithms/Framework.
C: Drafting the real requirement of all the objectives.
D: Real-Time testing and validation of the proposed algorithms/framework.
E: Drafting the Thesis.
F: Research Paper Publication.

26. References
• [1]. Rafique W. et al. “Complementing IoT Services Through Software Defined
Networking and Edge Computing: A Comprehensive Survey”, IEEE Communications
Surveys & Tutorials, Vol. 22, No. 3, pp. 1761-1800, 2020.
• [2]. Hu P. et al. “Software-Defined Edge Computing (SDEC): Principle, Open IoT
System Architecture, Applications, and Challenges”, IEEE Internet of Things Journal,
Vol. 7, No. 7, pp. 5934-5945, 2020.
• [3]. Dai M. et al. “A Software-Defined-Networking-Enabled Approach for Edge-Cloud
Computing in the Internet of Things”, IEEE Network, IEEE, pp. 66-73, 2021.
• [4]. Xia W. et al. “A survey on Software-Defined Networking”, IEEE Communications
Surveys & Tutorials, Vol. 17, No. 1, pp. 27-51, 2015.
• [5]. Li Y. et al. “Enhancing the Internet of Things with Knowledge-Driven Software-
Defined Networking Technology: Future Perspectives”, Sensors, MDPI, pp. 1-20, 2020.
• [6].Gebremariam A. et al. “SoftPSN: Software-Defined Resource Slicing for Low-
Latency Reliable Public Safety Networks”, Wireless Communications and Mobile
Computing, Wiley | Hindawi, Vol. 2018, pp. 1-7, 2018.
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27. References
• [7]. Li H. et al. “A Software-Defined Networking Roadside Unit Cloud Resource
Management Framework for Vehicle Ad Hoc Networks”, Journal of Advanced
Transportation, Wiley | Hindawi, Vol. 2022, pp. 1-13, 2022.
• [8]. Hu Y. et al. “Software-Defined Congestion Control Algorithm for IP
Networks”, Scientific Programming, Wiley | Hindawi, Vol. 2017, pp. 1-8, 2017.
• [9]. Qureshi M. et al. “A comparative analysis of resource allocation schemes for
real-time services in high-performance computing systems”, IJDSN, SAGE, Vol. 16
(8), pp 1-35, 2020.
• [10]. Neves P. et al. “The SELFNET Approach for Autonomic Management in an
NFV/SDN Networking Paradigm”, IJDSN, Hindawi, Vol. 2016, pp. 1-17, 2016.
• [11]. Moshref M. et.al. “DREAM: Dynamic Resource Allocation for Software-
defined Measurement”, SIGCOMM’14, ACM, pp. 419-430, 2014.
• [12]. Sarbazi M. et al. “Improving resource allocation in software-defined networks
using clustering”, Cluster Computing 23, Springer, pp. 1199-1210, 2020.
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28. References
• [13]. Semong T. et al. “Intelligent Load Balancing Techniques in Software Defined
Networks: A Survey”, Electronics, MDPI, 9, 1091, 2020.
• [14]. Babbar H. et al. “Load Balancing Algorithm on the Immense Scale of Internet
of Things in SDN for Smart Cities”, Sustainability, MDPI, 13, 9587, 2021.
• [15]. Zhong H. et al. “An Efficient SDN Load Balancing Scheme Based on Variance
Analysis for Massive Mobile Users”, Mobile Information Systems, Hindawi, Vol.
2015,pp. 1-9, 2015.
• [16]. Hosny W. et al. “Generic Controller Adaptive Load Balancing (GCALB) for SDN
Networks”, Journal of Computer Networks and Communication, Hindawi, Vol.
2019,pp. 2019.
• [17]. Babbar H. et al. “Load Balancing Algorithm for Migrating Switches in
Software-Defined Vehicular Networks”, Computers, Materials & Continua, Tech
Press Science, Vol. 67 No. 1, pp. 1301-1316, 2021.
• [18]. Chen J. et al. “ALBRL: Automatic Load-Balancing Architecture Based on
Reinforcement Learning in Software-Defined Networking”, Wireless
Communications and Mobile Computing, Hindawi, Vol. 2022, pp. 1-17, 2022.
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29. References
• [19]. Chen J. et al. “ALBLP: Adaptive Load-Balancing Architecture Based on Link-
State Prediction in Software-Defined Networking”, Wireless Communications and
Mobile Computing, Wiley | Hindawi, Vol. 2022. Pp. 1-16, 2022.
• [20]. Lee S. et al. “A Comprehensive Security Assessment Framework for
Software-Defined Networks”, Computers & Security, Elsevier, pp.1-20, 2020.
• [21]. Varadharajan V. et al. “A Policy-Based Security Architecture for Software-
Defined Networks”, IEEE Transactions On Information Forensics And Security,
Vol. 14,No. 4, pp. 897-912, 2019.
• [22]. Eom T. “A Systematic Approach to Threat Modeling and Security Analysis
for Software Defined Networking”, IEEE Access, Vol. 7 2019, pp. 137432-137445,
2019.
• [23]. Hayjneh A. et.al. “Improving Internet of Things (IoT) Security with
Software-Defined Networking (SDN)”, Computers 2020, MDPI,9,8, pp. 1-14,
2020.
• [24]. Ahmad I. et al. “Security in Software Defined Networks: A Survey”, IEEE
Communication Surveys & Tutorials, Vol. 17, No. 4, pp. 2317-2346, 2015.
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30. References
• [25]. Mavromatis A. et.al. “A Software-Defined IoT Device Management
Framework for Edge and Cloud Computing”, IEEE Internet of Things Journal, Vol.
7, No. 3, pp. 1718-1735, 2020.
• [26]. Munoz R. et al. “Integration of IoT, Transport SDN, and Edge/Cloud
Computing for Dynamic Distribution of IoT Analytics and Efficient Use of
Network Resources”, Journal of Lightwave Technology, Vol. 36, No. 7, pp. 1420-
1428, 2018.
• [27]. Simoes R. et al. “Dynamic Allocation of SDN Controllers in NFV-Based
MEC for the Internet of Vehicles”, Future Internet, MDPI, pp. 1-24, 2021.
• [28]. Ali J. et al. “An Effective Approach for Controller Placement in Software-
Defined Internet-of-Things (SD-IoT)”, Sensors, MDPI, pp. 1-16, 2022.
• [29]. Nsaif M. et al. “An Adaptive Routing Framework for Efficient Power
Consumption in Software-Defined Datacenter Networks”, Electronics, MDPI, pp.
1-18, 2021.
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31. Thank you
Any Queries?
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