Advances in Artificial Intelligence and Machine Learning for Networking
摘要截稿:
全文截稿: 2019-10-01
影响因子: 11.42
期刊难度:
CCF分类: A类
中科院JCR分区:
• 大类 : 计算机科学 - 1区
• 小类 : 工程:电子与电气 - 1区
• 小类 : 电信学 - 1区
Overview
Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged in the networking domain with great promise. They can be clustered into AI/ML techniques for network engineering and management, network design for AI/ML applications, and system aspects. AI/ML techniques for network management, operations and automation improve the way we address networking today. They support efficient, rapid, and trustworthy management operations. The current interest in softwarization and network programmability fuels the need for improved network automation in agile infrastructures, including edge and fog environments. Network design and optimization for AI/ML applications address the complementary topic of supporting AI/ML-based systems through novel networking techniques, including new architectures and performance models. A third topic area is system implementation and open-source software development.
This special issue will focus on networking aspects (mostly, network layer and above). Work with primary contribution to physical layer concepts or wireless access should be submitted to other venues. Prospective authors are invited to submit high-quality, original manuscripts on topics including, but not limited to:
1. Fundamental Frameworks
- Network theory inspired by machine learning
- Transfer learning and reinforcement learning for networking
- Big data analytic frameworks for networking data
2. Network analytics
- Machine learning, data mining and big data analytics for networking
- Representation learning on operational data
- Data mining, statistical modeling, and machine learning for network management
- User experience-driven network planning
- Learning algorithms and tools for network diagnostics and root cause analysis
3. Network decision making and optimization
- Protocol design and optimization using machine learning
- Network architecture and optimization for AI/ML applications at scale
- Resource allocation for shared/virtualized networks using machine learning
- Energy-efficient network operations based on AI/ML algorithms
- AI/ML Algorithms for network security
- Network Reliability, robustness and safety based on AI/ML concepts
- Security for networks optimized and operated based on AI/ML concepts
4. Network automation
- Self-driving networks
- Self-Learning and adaptive networking protocols and algorithms
- Deep learning and reinforcement learning in network control & management
- Predictive or self-aware networking maintenance
- Open-source AI software for networking or networked applications