A Virtual Network Embedding Algorithm Based on RBF Neural Network

With the emergence of network virtualization, the infrastructure can be effectively integrated to overcome the "ossification" of the Internet. The biggest challenge in network virtualization is the problem of virtual network embedding. Unfortunately, most of the researches on virtual network embedding only focus on static algorithms, which allocate fixed or invariable resources to virtual networks until the end of their lifetime. However, the demand of virtual requests for resources are dynamically changing and fluctuating in reality. Therefore, the traditional static schemes not only greatly reduce the utilization of substrate resources, but also decrease the revenue of the service providers. In this paper, we aim to satisfy the dynamic requirement of resources for virtual networks. We propose a dynamic embedding algorithm (RBFVNE) which is based on RBF neural network to learn and predict the dynamic changes of resources, and then we dynamically adjust and allocate resources according to the predicted results. Simulation results show that our approach can well integrate substrate resources to solve above problems and performs well than static embedding algorithms.