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Home»Articles»AI Based SDN Technology Integration with their Challenges and Opportunities

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Asian Journal of Computer Science and Technology (AJCST)

Editor Dr. K. Ganesh
Print ISSN : 2249-0701
Frequency : Quarterly

AI Based SDN Technology Integration with their Challenges and Opportunities

Author : Shabana, Sallauddin Mohmmad, Kotha Shankar and Yerrolla Chanti
Volume 8 No.3 Special Issue:June 2019 pp 165-169

Abstract

Software Defined Networking (SDN) is a new architecture that provides innovative set of tools for facing the network challenges. Using of software-based control plane and data plane, SDN is easily managing the adapted tradition network functions. In the real time scenario SDN is managing resources of different types of networks such as mobile networks, IoT and internet communication and data centers. To manage these all inter discipline functionalities from various networks SDN need of AI integration. Artificial intelligence (AI) is an extensive scientific revolution which enables SDN/NVF to solve problems by emulating complex network processes such as routing, dynamic configurations, bandwidth availability and domain name processing and transmission in communication etc. This paper presents a comprehensive review of the application of AI techniques for improving performance of SDN and NVF. The ML based AI techniques learn the data plane functionalities such as MAC, implementation of routing algorithms, firewall systems and load balance. AI-based ML techniques and also learn topology abstraction and NOS in the control plane in the network then monitoring the devices, reduce the non-linearity in network communication, create quality of transmission and finally deploy the autonomic controlling on systems. ML-based AI techniques can able to create SDN in autonomic and self-managing networks which allows SDN to learn about network subsequently take automatic decisions. This article describes on how to interpret the AI techniques and wide deployment of autonomic learns nature in the SDN also describes the opportunities and challenges in AI based SDN.

Keywords

SDN, NVF, Artificial Intelligence, Machine Learning

Full Text:

References

[1] D. Kreutz, F. M. Ramos, and P. Verissimo, “Towards secure and dependable software-defined networks,” in Proceedings of the second ACM SIGCOMM workshop on Hot topics in software defined networking – HotSDN ’13. New York, New York, USA: ACM Press, 2013, pp. 55.
[2] M. Casado, T. Garfinkel, A. Akella, M. J. Freedman, D. Boneh, N. McKeown, and S. Shenker, “SANE: a protection architecture for enterprise networks,” 15th USENIX Security Symposium, pp. 137151, 2006.
[3] F. Hu, Q. Hao, and K. Bao, “A survey on software-defined network (SDN) and open flow: From concept to implementation,” IEEE Commun. Surveys & Tutorials, Vol. 16, No. 4, pp. 2181–2206, Fourth Quarter 2014.
[4] C. V. N. I. Forecast, “Cisco visual networking index: Global mobile data traffic forecast update 2016-2021,” Cisco Public Information, February, 2017.
[5] F. Tram`er, F. Zhang, F. E. Epfl, A. Juels, M. K. Reiter, and T. Ristenpart, “Stealing Machine Learning Models via Prediction APIs,” in Proceedings of the 25th USENIX Security Symposium, 2016.
[6] Indira Paudel, Jeevan Pokhrel, Bachar Wehbiy, Ana Cavalli and Badii Jouaber. “Estimation of video QoE from MAC parameters in wireless network: A Random Neural Network approach”. International Symposium on Communications and Information Technologies (ISCIT), 2014.
[7] Yaqian Kang, Huifang Chen, Lei Y.-J. Kim, K. He, M. Thottan and J. G.Deshpande, “Virtualized and self-configurable utility communications enabled by software-defined networks”. In Proceedings of 5th IEEE International Conference on Smart Grid Communications (SmartGridComm), 2014.
[8] Sallauddin Mohmmad, Shabana and Ranganath Kanakam, “Provisioning Elasticity on IoT’s Data In Shared-Nothing Nodes”, International Journal of Pure and Applied Mathematics, Vol. 117, No. 7, pp. 165-173, Oct. 2017.
[9] Sallauddin Mohmmad, Dr. M. Sheshikala and Shabana, “Software Defined Security (SDSec): Reliable centralized security system to decentralized applications in SDN and their challenges” Jour. of Adv. Research in Dynamical & Control Systems, Vol. 10, 10-Special Issue, pp. 147-152, 2018
[10] A´ ngel Leonardo Valdivieso Caraguay, Alberto Benito Peral, Lorena Isabel Barona L´opez, and Luis Javier Garc´ıa Villalba. “SDN: evolution and opportunities in the development IoT applications”. International Journal of Distributed Sensor Networks, 2014.
[11] Zhijing Qin, Luca Iannario, Carlo Giannelli, Paolo Bellavista, Grit Denker, and Nalini Venkatasubramanian, “Mina: A reflective middleware for managing dynamic multi network environments”. In 2014 IEEE Network Operations and Management Symposium (NOMS), pp. 1– 4, 2014.

Asian Journal of Computer Science and Technology is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and research papers covering all aspects of future computer and Information Technology areas. Topics include, but are not limited to:

Foundations of High-performance ComputingTheory of algorithms and computability

Parallel & distributed computing

Computer networks

Neural networks

LAN/WAN/MAN

Database theory & practice

Mobile Computing for e-Commerce

Future Internet architecture

Protocols and services

Mobile and ubiquitous networks

Green networking

Internet content search

Opportunistic networking

Network applications

Network scaling and limits

Artifial Intelligences

Pattern/Image Recognitions

Communication Network

Information Security

Knowledge Management

Management Information systems

Multimedia communicatiions

Operations research

Optical networks

Software Engineering

Virtual reality

Web Technologies

Wireless technology

Software Defined Networking (SDN) is a new architecture that provides innovative set of tools for facing the network challenges. Using of software-based control plane and data plane, SDN is easily managing the adapted tradition network functions. In the real time scenario SDN is managing resources of different types of networks such as mobile networks, IoT and internet communication and data centers. To manage these all inter discipline functionalities from various networks SDN need of AI integration. Artificial intelligence (AI) is an extensive scientific revolution which enables SDN/NVF to solve problems by emulating complex network processes such as routing, dynamic configurations, bandwidth availability and domain name processing and transmission in communication etc. This paper presents a comprehensive review of the application of AI techniques for improving performance of SDN and NVF. The ML based AI techniques learn the data plane functionalities such as MAC, implementation of routing algorithms, firewall systems and load balance. AI-based ML techniques and also learn topology abstraction and NOS in the control plane in the network then monitoring the devices, reduce the non-linearity in network communication, create quality of transmission and finally deploy the autonomic controlling on systems. ML-based AI techniques can able to create SDN in autonomic and self-managing networks which allows SDN to learn about network subsequently take automatic decisions. This article describes on how to interpret the AI techniques and wide deployment of autonomic learns nature in the SDN also describes the opportunities and challenges in AI based SDN.

Editor-in-Chief
Dr. K. Ganesh
Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
Expert at McKinsey and Company, India
[email protected]
Editorial Advisory Board
Dr. Eng. Hamid Ali Abed AL-Asadi
Department of Computer Science, Basra University, Iraq
[email protected]
Dr. Norjihan Binti Abdul Ghani
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Christos Bouras
Department of Computer Engineering & Informatics, University of Patras, Greece
[email protected]
Dr. Maizatul Akmar Binti Ismail
Department of Information System, University of Malaya, Malaysia
[email protected]
Dr. Harold Castro
Department of Systems Engineering and Computing, University of the Andes, Colombia
[email protected]
Dr. Busyairah Binti Syd Ali
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. Sri Devi Ravana
Department of Information system, University of Malaya, Malaysia
[email protected]
Dr. Karpaga Selvi Subramanian
Department of Computer Engineering, Mekelle University, Ethiopia
[email protected]
Dr. Mazliza Binti Othman
Department of Computer System & Technology, University of Malaya, Malaysia
[email protected]
Dr. Chiam Yin Kia
Department of Software Engineering, University of Malaya, Malaysia
[email protected]
Dr. OUH Eng Lieh
Department of Information Systems, Singapore Management University, Singapore
[email protected]

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    Table of Contents

    Editorial Note

    Editorial Dr. K. Ganesh

    Editor-in-Chief
    Dr. K. Ganesh
    Global Lead, Supply Chain Management, Center of Competence and Senior Knowledge
    Expert at McKinsey and Company, India
    [email protected]
    Editorial Advisory Board
    Dr. Eng. Hamid Ali Abed AL-Asadi
    Department of Computer Science, Basra University, Iraq
    [email protected]
    Dr. Norjihan Binti Abdul Ghani
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Christos Bouras
    Department of Computer Engineering & Informatics, University of Patras, Greece
    [email protected]
    Dr. Maizatul Akmar Binti Ismail
    Department of Information System, University of Malaya, Malaysia
    [email protected]
    Dr. Harold Castro
    Department of Systems Engineering and Computing, University of the Andes, Colombia
    [email protected]
    Dr. Busyairah Binti Syd Ali
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. Sri Devi Ravana
    Department of Information system, University of Malaya, Malaysia
    [email protected]
    Dr. Karpaga Selvi Subramanian
    Department of Computer Engineering, Mekelle University, Ethiopia
    [email protected]
    Dr. Mazliza Binti Othman
    Department of Computer System & Technology, University of Malaya, Malaysia
    [email protected]
    Dr. Chiam Yin Kia
    Department of Software Engineering, University of Malaya, Malaysia
    [email protected]
    Dr. OUH Eng Lieh
    Department of Information Systems, Singapore Management University, Singapore
    [email protected]

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