Topology-aware virtual network embedding through bayesian network analysis

Multiple heterogenous virtual networks are given the ability to run on a shared infrastructure simultaneously as independent slices in the network virtualization environment. However, a major challenge is how to map multiple virtual networks, with specific node and link constraints, onto the shared substrate network, known as virtual network embedding problem. By taking topology attribute into account, topology-aware virtual network embedding algorithms efficiently improve the performance by leveraging a node ranking method based on Markov chain. However, as the basis of node ranking, the resource evaluation of node which is calculated as the product of its CPU and bandwidth may be incorrect. Moreover, a greedy matching strategy is always applied in the node mapping stage, which may lead to unnecessary bandwidth consumption by ignoring the relationships between the mapped substrate nodes and the mapping one. In this paper, we re-think the topology-aware virtual network embedding from a statistical perspective by proposing a statistical method to generate a dependency matrix representing the importance of every node and the relationships between every two nodes in the substrate network. Based on this dependency matrix, bayesian network analysis is leveraged to iteratively select the substrate node, with the closest relationship to the selected ones, to achieve node mapping process. Extensive simulations were conducted and the results show that our proposed algorithm has better performance in the long-term run.