Dynamic Virtual Network Embedding Algorithm Based on Graph Convolution Neural Network and Reinforcement Learning

Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by the heur...

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Vydáno v:IEEE internet of things journal Ročník 9; číslo 12; s. 9389 - 9398
Hlavní autoři: Zhang, Peiying, Wang, Chao, Kumar, Neeraj, Zhang, Weishan, Liu, Lei
Médium: Journal Article
Jazyk:angličtina
Vydáno: Piscataway IEEE 15.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Artificial neural networks
Clustering algorithms
Convolution
Dynamic characteristics
Dynamic scheduling
Embedding
Fitness
Fitness matrix
Flexibility
graph convolution neural network
Heuristic algorithms
Heuristic methods
Internet of Things
Machine learning
Neural networks
Reinforcement learning
reinforcement learning (RL)
Resource allocation
Search problems
virtual network embedding (VNE)
Virtual networks
ISSN:2327-4662, 2327-4662
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Popis
Shrnutí:Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by the heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This article proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.
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ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2021.3095094