Network Virtualization
Network virtualization aims to efficiently allocate physical network resources to virtual networks on demand, addressing the complex challenge of resource management in increasingly diverse and dynamic network environments. Current research heavily focuses on employing reinforcement learning, often coupled with graph neural networks, to optimize virtual network embedding—the process of mapping virtual network requests onto the physical infrastructure—with a particular emphasis on improving resource allocation strategies and addressing security concerns. These advancements are crucial for enabling flexible, scalable, and secure network services across various applications, including the Internet of Things and 6G networks, by improving resource utilization and network performance.
Papers
A multi-domain virtual network embedding algorithm with delay prediction
Peiying Zhang, Xue Pang, Yongjing Ni, Haipeng Yao, Xin Li
Network Resource Allocation Strategy Based on Deep Reinforcement Learning
Shidong Zhang, Chao Wang, Junsan Zhang, Youxiang Duan, Xinhong You, Peiying Zhang
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning
Peiying Zhang, Chao Wang, Chunxiao Jiang, Abderrahim Benslimane
Dynamic Virtual Network Embedding Algorithm based on Graph Convolution Neural Network and Reinforcement Learning
Peiying Zhang, Chao Wang, Neeraj Kumar, Weishan Zhang, Lei Liu
Resource Management and Security Scheme of ICPSs and IoT Based on VNE Algorithm
Peiying Zhang, Chao Wang, Chunxiao Jiang, Neeraj Kumar, Qinghua Lu