Virtual Network Mapping Based on the Prediction of Support Vector Machine

Network virtualization plays an important role in the development of the network because of its dynamics and flexibility on network infrastructure configuration. Virtual network mapping is the main method to realize the network virtualization. Most of the current virtual network mappings always allocate network resources in an exclusive and excessive way. For example, the entire bandwidth amount of the virtual network will be allocated according to its peak demand of the traffic. However, the truth is that the actual traffic needs of virtual network are changing constantly throughout its lifetime, and the distribution of static mapping will inevitably lead to the underutilization of the assigned resource, high user cost as well as low carrier revenue. In order to solve the above problems, we need to predict the changes of virtual network's demands accurately and adjust the allocation of resources dynamically. In this paper, we propose a virtual network mapping method based on support vector machine (SVM) to dynamically allocate and adjust the network resources. In addition, to improve the accuracy of regression forecasting, the relatively better prediction parameters are selected in the proposed method. Experimental results show that the proposed embedding method can make full use of resources, improve the acceptance rate of the virtual networks, and increase the revenue of the operators significantly.