Virtual Network Embedding Algorithm
Virtual network embedding (VNE) algorithms optimize the mapping of virtual networks onto physical infrastructure, aiming to efficiently allocate resources while meeting diverse service requirements. Current research heavily emphasizes the use of deep reinforcement learning (DRL) and other metaheuristic algorithms, often incorporating multi-objective optimization to address resource constraints, security concerns, and varying isolation levels. These advancements improve resource utilization, network performance, and service assurance in increasingly complex and heterogeneous network environments, such as those integrating space-air-ground networks and supporting the Internet of Things.
Papers
Multi Objective Resource Optimization of Wireless Network Based on Cross Domain Virtual Network Embedding
Chao Wang, Tao Dong, Youxiang Duan, Qifeng Sun, Peiying Zhang
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
Space-Air-Ground Integrated Multi-domain Network Resource Orchestration based on Virtual Network Architecture: a DRL Method
Peiying Zhang, Chao Wang, Neeraj Kumar, Lei Liu
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