Special Issue "Intelligent Software-defined Networking (SDN) Technologies for Future Generation Networks"

Submission Deadline: 01 February 2021 (closed)
Guest Editors
Dr. Jehad Ali, Ajou University, South Korea.
Dr. Muhammad Sajjad Khan, Korea Polytechnic University, South Korea.
Dr. Khalil Khan, Pak-Austria Institute of Applied Science and Technology, Pakistan.
Dr. Rahim Khan, Abdul Wali Khan Univesity Mardan, Pakistan.


Software-Defined networking (SDN) dynamically and efficiently manage resources to provision diverse services leveraging controller intelligence and programmability. SDN enable the network systems to orchestrate and estimate the available resources and dynamically adapt to the environment for a maximize resource utilization.

Deep learning (DL) is becoming a successful way to boost the SDN controller intelligence as a promising machine learning solution. Machine learning and Artificial intelligence (AI) techniques provide effectiveness for adaptation in network communication. The controller trained with AI and Machine learning sophisticated algorithms can enhance the provision of End-to-End (E2E) Services, Security, and resources management.

This Special Issue look forward to state-of-the-art technologies for the SDN using machine learning techniques, covering new research results with a wide range of elements within the intelligent SDN technology for future generation networks.

Potential topics include but are not limited to the following:

• Low-latency SDN

• AI or Machine learning-based Software-defined networks

• Energy-efficient Software-defined Networks

• Load balancing in energy constrained environments using Software-defined networks

• AI solutions for enhancing availability of the control plan

• Controller placement problem optimization using game-theoretic and machine learning approaches

• Leveraging Software-defined Networks for 5G resource and mobility management

• E2E latency reduction in Software-defined networks

• Security and privacy for Software-defined networks

• Benchmarking Controllers performance

• Efficient Fault management leveraging AI for Software-defined networks

• Application of Software-defined networks in network slicing, Fog computing, Resource management, and edge computing

• SDN for mission-critical applications

• Other related issues.

• Artificial intelligence
• Machine learning
• Tactile internet
• 5G
• Interoperability
• Low latency

Published Papers
  • Q-Learning Based Routing Protocol for Congestion Avoidance
  • Abstract The end-to-end delay in a wired network is strongly dependent on congestion on intermediate nodes. Among lots of feasible approaches to avoid congestion efficiently, congestion-aware routing protocols tend to search for an uncongested path toward the destination through rule-based approaches in reactive/incident-driven and distributed methods. However, these previous approaches have a problem accommodating the changing network environments in autonomous and self-adaptive operations dynamically. To overcome this drawback, we present a new congestion-aware routing protocol based on a Q-learning algorithm in software-defined networks where logically centralized network operation enables intelligent control and management of network resources. In a proposed routing protocol,… More
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  • Kernel Search-Framework for Dynamic Controller Placement in Software-Defined Network
  • Abstract In software-defined networking (SDN) networks, unlike traditional networks, the control plane is located separately in a device or program. One of the most critical problems in these networks is a controller placement problem, which has a significant impact on the network’s overall performance. This paper attempts to provide a solution to this problem aiming to reduce the operational cost of the network and improve their survivability and load balancing. The researchers have proposed a suitable framework called kernel search introducing integer programming formulations to address the controller placement problem. It demonstrates through careful computational studies that the formulations can design… More
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