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

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Networks".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 526

Special Issue Editor


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State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China
Interests: network services and intelligence; the Internet of Things technology; multimedia communications
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Special Issue Information

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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Keywords

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

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Published Papers (1 paper)

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Research

17 pages, 737 KiB  
Article
DRL-Based Fast Joint Mapping Approach for SFC Deployment
by You Wu, Hefei Hu and Ziyi Zhang
Electronics 2025, 14(12), 2408; https://doi.org/10.3390/electronics14122408 - 12 Jun 2025
Viewed by 296
Abstract
The rapid development of Network Function Virtualization (NFV) enables network operators to deliver customized end-to-end services through Service Function Chains (SFCs). However, existing two-stage deployment strategies fail to jointly optimize the placement of Virtual Network Functions (VNFs) and the routing of service traffic, [...] Read more.
The rapid development of Network Function Virtualization (NFV) enables network operators to deliver customized end-to-end services through Service Function Chains (SFCs). However, existing two-stage deployment strategies fail to jointly optimize the placement of Virtual Network Functions (VNFs) and the routing of service traffic, resulting in inefficient resource utilization and increased service latency. This study addresses the challenge of maximizing the acceptance rate of service requests under resource constraints and latency requirements. We propose DRL-FJM, a novel dynamic SFC joint mapping orchestration algorithm based on Deep Reinforcement Learning (DRL). By holistically evaluating network resource states, the algorithm jointly optimizes node and link mapping schemes to effectively tackle the dual challenges of resource limitations and latency constraints in long-term SFC orchestration scenarios. Simulation results demonstrate that compared with existing methods, DRL-FJM improves total traffic served by up to 42.6%, node resource utilization by 17.3%, and link resource utilization by 26.6%, while achieving nearly 100% SFC deployment success. Moreover, our analysis reveals that the proposed algorithm demonstrates strong adaptability and robustness under diverse network conditions. Full article
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