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Intelligent Software-Defined Network for Cognitive Routing Optimization Using Deep Extreme Learning Machine Approach

Fahd Alhaidari1, Sultan H. Almotiri2, Mohammed A.Al Ghamdi2, Muhammad Adnan Khan3,*, Abdur Rehman4, Sagheer Abbas4, Khalid Masood Khan3, Atta-ur-Rahman5

1 Department of Computer Information System, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia
2 Department of Computer Science, Umm Al-Qura University, Makkah City, 715, Saudi Arabia
3 Department of Computer Science, Lahore Garrison University, Lahore, 54792, Pakistan
4 School of Computer Science, NCBA&E, Lahore, 54000, Pakistan
5 Department of Computer Science, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, Dammam, 31441, Saudi Arabia

* Corresponding Author: Muhammad Adnan Khan. Email:

Computers, Materials & Continua 2021, 67(1), 1269-1285.


In recent years, the infrastructure, instruments, and resources of network systems are becoming more complex and heterogeneous, with the rapid development of current internet and mobile communication technologies. In order to efficaciously prepare, control, hold and optimize networking systems, greater intelligence needs to be deployed. However, due to the inherently dispensed characteristic of conventional networks, Machine Learning (ML) techniques are hard to implement and deployed to govern and operate networks. Software-Defined Networking (SDN) brings us new possibilities to offer intelligence in the networks. SDN’s characteristics (e.g., logically centralized control, global network view, software-based site visitor analysis, and dynamic updating of forwarding rules) make it simpler to apply machine learning strategies. Various perspectives of fiber-optic communications including fiber nonlinearity coverage, optical performance checking, cognitive shortcoming detection/anticipation, and arranging and improvement of software-defined networks are examined in Machine Learning (ML) applications. This research paper has presented an imaginative framework concept called Intelligent Software Defined Network (ISDN) for Cognitive Routing Optimization (CRO) using Deep Extreme Learning Machine (DELM) approach (ISDN-CRO-DELM) in light of the new challenges in the development and operation of communication systems, and capturing motivation from how living creatures deal with difficulty and usability. The proposed methodology develops around the planned applications of progressive DELM methods and, specifically, probabilistic generative models for framework wide learning, demonstrating, improvement, and information description. Furthermore, ISDN-CRO-DELM, suggest to integrate this learning framework with the ISDN for CRO and reconfiguration approaches at the system level. MATLAB 2019a is used for DELM simulation and superior results show the effectiveness of the proposed framework.


Cite This Article

F. Alhaidari, S. H. Almotiri, M. A.Al Ghamdi, M. Adnan Khan, A. Rehman et al., "Intelligent software-defined network for cognitive routing optimization using deep extreme learning machine approach," Computers, Materials & Continua, vol. 67, no.1, pp. 1269–1285, 2021.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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