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

doi:10.1109/JIOT.2021.3095094

2022-06-15

张培颖 (Peiying Zhang)

Chao Wang

Neeraj Kumar

张卫山 (Weishan Zhang) *

Lei Liu

DOI：10.1109/JIOT.2021.3095094

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摘要

En 中文

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.

Keyword:

Heuristic algorithms

Clustering algorithms

Internet of Things

Dynamic scheduling

Search problems

Reinforcement learning

Convolution

Fitness matrix

graph convolution neural network

reinforcement learning (RL)

virtual network embedding (VNE)