A efficient mapping algorithm with novel node-ranking approach for embedding virtual networks

Virtual network embedding (VNE) problem has been widely accepted as an important aspect in network virtualization (NV) area: how to efficiently embed virtual networks, with node and link resource demands, onto the shared substrate network that has finite network resources. Previous VNE heuristic algorithms, only considering single network topology attribute and local resources of each node, may lead to inefficient resource utilization of the substrate network in the long term. To address this issue, a topology attribute and global resource-driven VNE algorithm (VNE-TAGRD), adopting a novel node-ranking approach, is proposed in this paper. The novel node-ranking approach, developed from the well-known Google PageRank algorithm, considers three essential topology attributes and global network resources information before conducting the embedding of given virtual network request (VNR). Numerical simulation results reveal that the VNE-TAGRD algorithm outperforms five typical and latest heuristic algorithms that only consider single network topology attribute and local resources of each node, such as long-term average VNR acceptance ratio and average revenue to cost ratio

Non-linear Attributed Graph Clustering by Symmetric NMF with PU Learning

We consider the clustering problem of attributed graphs. Our challenge is how we can design an effective and efficient clustering method that precisely captures the hidden relationship between the topology and the attributes in real-world graphs. We propose Non-linear Attributed Graph Clustering by Symmetric Non-negative Matrix Factorization with Positive Unlabeled Learning. The features of our method are three holds. 1) it learns a non-linear projection function between the different cluster assignments of the topology and the attributes of graphs so as to capture the complicated relationship between the topology and the attributes in real-world graphs, 2) it leverages the positive unlabeled learning to take the effect of partially observed positive edges into the cluster assignment, and 3) it achieves efficient computational complexity, $O((n^2+mn)kt)$, where $n$ is the vertex size, $m$ is the attribute size, $k$ is the number of clusters, and $t$ is the number of iterations for learning the cluster assignment. We conducted experiments extensively for various clustering methods with various real datasets to validate that our method outperforms the former clustering methods regarding the clustering quality

Efficient and Secure 5G Core Network Slice Provisioning Based on VIKOR Approach

Network slicing in 5G is expected to essentially change the way in which network operators deploy and manage vertical services with different performance requirements. Efficient and secure slice provisioning algorithms are important since network slices share the limited resources of the physical network. In this article, we first analyze the security issues in network slicing and formulate an Integer Linear Programming (ILP) model for secure 5G core network slice provisioning. Then, we propose a heuristic 5G core network slice provisioning algorithm called VIKOR-CNSP based on VIKOR, which is a multi-criteria decision making (MCDM) method. In the slice node provisioning stage, the node importance is ranked with the VIKOR approach by considering the node resource and topology attributes. The slice nodes are then provisioned according to the ranking results. In the slice link provisioning stage, the k shortest path algorithm is implemented to obtain the candidate physical paths for the slice link, and a strategy for selecting a candidate physical path is proposed to increase the slice acceptance ratio. The strategy first calculates the path factor P which is the product of the maximum link bandwidth utilization of the candidate physical path and its hop-count, and then chooses the candidate physical path with the smallest P to host the slice link. Extensive simulations show that the proposed algorithm can achieve the highest slice acceptance ratio and the largest provisioning revenue-to-cost ratio, satisfying the security constraints of 5G core network slice requests. f

Geographic Information Systems, Spatial Data Analysis and Spatial Modelling. - Problems and Possibilities -

This article is the position paper for the ESF-GISDATA Specialist Meeting on GIS & Spatial Analysis, Amsterdam, 1-5December1993. The focus here is on the two major themes of the meeting: Spatial Data Analysis and Spatial Modelling. Special emphasis is laid on specific problems and possibilities for interfacing spatial analysis tools (i.e. spatial data analysis techniques and spatial models) and GIS. Both GIS application fields, the environmental sciences and the social sciences, are taken into consideration. (authors' abstract)Series: Discussion Papers of the Institute for Economic Geography and GIScienc