An RBF neural network-based dynamic virtual network embedding algorithm

作者：Zheng, Xiangwei^*; Zhang, Yuang; Zhang, Hui; Xue, Qingshui

来源：Concurrency and Computation: Practice and Experience (CCPE) , 2019, 31(23): e4516.

DOI：10.1002/cpe.4516

• dynamic VN embedding
• network virtualization
• resource allocation
• static VN embedding
• supervised RBF neural network
• virtual network embedding

Network virtualization is a promising technology, which is used to solve the existing shortcomings of the Internet architecture and in which virtual network (VN) embedding plays a vital role in resource allocation. However, current virtual network embedding algorithms mainly deal with static VN embedding; the allocations of network resources are constant and excessive in the VN requests' lifetime. In reality, the user's resource requirements usually change with time; hence, static VN embedding algorithms can lead to low utilization of substrate resources and reduction of the operators' revenues. To solve the aforementioned problem, we focus on the virtual network embedding considering dynamic demands in this paper. We apply supervised radial basis RBF) neural network to predict the changes of virtual requests and dynamically reallocate substrate resources by adjusting already allocated resources. Simulation experiments show that compared with the static algorithms, dynamic VN embedding schemes with supervised RBF can more accurately predict the demand changes of virtual networks, which adaptively adjusts resources allocation and management and therefore improves resource utilization of substrate networks.

• 出版日期2019-12-10
• 单位山东师范大学; 上海应用技术大学