Topology-Aware Virtual Network Embedding Through Bayesian Network Analysis
Multiple heterogeneous 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.