Submission Deadline: 01 February 2021 (closed) View: 219
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.