AI-Driven Network Function Virtualization and Intelligent Cloud-Edge Computing for Future Networks

Dear Colleagues,

With the rapid advancements in AI and cloud-edge computing, future networks are evolving to support increasingly complex and dynamic applications, such as autonomous systems, immersive extended reality (XR), and ultra-reliable low-latency services. The integration of AI-driven network function virtualization (NFV) and intelligent cloud-edge computing has become crucial for enhancing network efficiency, adaptability, and scalability to meet these emerging demands. The development of efficient and intelligent NFV and cloud-edge computing faces several challenges, including real-time network optimization, dynamic service orchestration, heterogeneous resource management, and security concerns. Addressing these challenges requires advancements in AI-driven optimization models, autonomous decision-making mechanisms, and collaborative computing frameworks across cloud and edge environments. Key technologies such as deep learning-based network function orchestration, reinforcement learning for resource allocation, and AI-powered intent-based networking are expected to revolutionize NFV and cloud-edge architectures.

This Special Issue of Electronics highlights recent breakthroughs in AI-driven NFV and intelligent cloud-edge computing, promoting innovative research that enhances the intelligence, efficiency, and security of future networks. We invite researchers to contribute original articles and reviews, with topics including, but not limited to, the following research areas:

• AI-driven architectures for NFV and cloud-edge computing;
• Intelligent orchestration and autonomous management in NFV environments;
• AI-enabled optimization for dynamic resource allocation and service placement;
• Distributed and federated AI for cloud-edge collaborative intelligence;
• Digital twins and predictive analytics for network function optimization;
• Self-adaptive and intent-based networking for NFV and cloud-edge environments;
• AI-driven workload balancing and service migration strategies;
• Real-time data-driven decision-making and automation in future networks;
• Convergence of AI, NFV, and cloud-edge computing for emerging applications;
• AI-powered security, trust, and privacy-preserving mechanisms in NFV and edge networks.

Prof. Dr. Bo Cheng
Guest Editor

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• cloud-edge computing
• network function virtualization (NFV)
• artificial intelligence
• future intelligent networks