Node Importance of Data Center Network Based on Contribution Matrix of Information Entropy

With the development of cloud computing, data center network (DCN) architectures as the core of the cloud platform received a surge of interesting from both the industry and academia. However, assessments of those new DCN architectures are mainly concentrated in load balancing, improvement of architectures and as well as some research of performance analysis in visualized environment. Moreover, none of them focus on the security in DCN architectures. In this paper, we propose contribution matrix method based on information entropy theory from the point of view of node importance which is the basic of the topology vulnerability. In addition, we conduct an experimental evaluation of the state-of-the-art Multi-rooted Tree and FiConn architectures, each respectively as a representative of the switch-centric network architecture and server-centric network architecture. Firstly, we use an undirected graph theory to describe the architecture. Secondly, we obtain the associated matrix of betweenness and degree, and calculate their weights by information entropy theory. And then, according to the definition 3 (see Section III-B); we obtain the contribution matrix for each one. Last but not least, we get the value of node importance for each node of normalized. Compared with other methods, our proposed method is effective and has a much higher accuracy. Furthermore, our method is generic and can be widely used for the new DCN architectures.