Low-Cost Deployment Scheme of VNF and PNF in Optical Datacenter Networks

In this paper, we study the problem of network function deployment while user demands are served by both virtualized network functions (VNFs) and physical network functions (PNFs). A hybrid deployment scheme of VNFs and PNFs (HDVP) is proposed. A heuristic algorithm is developed for the HDVP scheme with the aim of minimizing the total cost of VNFs and PNFs. The results show the HDVP scheme achieves lower cost than the conventional only PNFs serving user demands scheme.

Robust and Fast Converging Cross-Layer Failure Correction in Segment-Routed Networks

Due to overlay technologies, service providers have a logical view of the underlay network and can optimize the experience quality without modifying the physical network. However, the cross-layer interaction inevitably causes network fluctuation due to their inconsistent optimization objectives. Aside from that, network failures that occur in both layers not only cause network performance degradation but also significantly increase the frequency of cross-layer interaction. These problems make the network fluctuate for a long time, reduce the network performance, and influence the user experience, especially for time-sensitive applications. In this paper, we design a cross-layer architecture in which the logical layer can satisfy the service function chain demands and maximize the user experience and physical layer so it can optimize the overall network performance. Our cross-layer architecture can make proactive corrections in both layers. Furthermore, we investigate the cross-layer interaction and design two strategies to eliminate fluctuations and make the network converge quickly.

Prediction Method of College Students’ Psychological Pressure Based on Deep Neural Network

Aiming at the problems of low prediction accuracy and efficiency and poor prediction effect in the current psychological pressure prediction methods, a psychological pressure prediction method for college students based on deep neural network is proposed. The structure and algorithm of depth neural network and gray theory model are analyzed. Using the deep neural network, this paper establishes the sample set data of college students’ psychological pressure prediction and constructs the college students’ psychological pressure prediction model combined with the deep neural network algorithm of gray theory. The physical network information model is formed through the relationship between neurons. According to the dynamic changes of college students’ psychological pressure in each neuron of the physical network, the prediction of college students’ psychological pressure is completed. The experimental results show that the proposed method is effective in predicting college students’ psychological pressure and can effectively improve the accuracy and efficiency of college students’ psychological pressure prediction.

The Inter-Human Cortex

This chapter outlines a theory of co-evolution of contexts and histories in human culture by making an analogy with the microscopic functionality of the human brain, and in particular Eugene Izhikevich’s idea of polychronization by mapping the network of ‘firing’ events in a biological neural network onto a network of ‘human events’ in the physical network of humans. The article utilizes the new theory to focus on the evolution of sound art by pointing to the multiplicity of origin contexts, and it examines a particular example of sound art installation, The Fragmented Orchestra (Jane Grant, John Matthias, and Nick Ryan) to exemplify the theory of the inter-human cortex.

A novel algorithm for finding top-k weighted overlapping densest connected subgraphs in dual networks

The use of networks for modelling and analysing relations among data is currently growing. Recently, the use of a single networks for capturing all the aspects of some complex scenarios has shown some limitations. Consequently, it has been proposed to use Dual Networks (DN), a pair of related networks, to analyse complex systems. The two graphs in a DN have the same set of vertices and different edge sets. Common subgraphs among these networks may convey some insights about the modelled scenarios. For instance, the detection of the Top-k Densest Connected subgraphs, i.e. a set k subgraphs having the largest density in the conceptual network which are also connected in the physical network, may reveal set of highly related nodes. After proposing a formalisation of the approach, we propose a heuristic to find a solution, since the problem is computationally hard. A set of experiments on synthetic and real networks is also presented to support our approach.

Application of an Interference Cancellation Detector in a Two-Way Relaying System with Physical Network Coding

In this paper, we investigate the performance of a detector recently proposed by us that is applied in the relay station receiving signals from two terminals concurrently exchanging data in the two-way relaying system. This is one of the potential configurations to save resources in fifth-generation systems, similar to non-orthogonal multiple access, which is also considered for such systems. Two-way relaying can be implemented using physical network coding. This technique originates from the network coding idea, in which network nodes can perform some mathematical operations. The idea of the investigated detector lies in the application of tentative decisions about weaker signals in the detection of stronger ones and then, after improved detection of stronger user signals, achieving more reliable decisions about the weaker ones. We compare the performance of the proposed detector with the performance of a detector in which the relay makes decisions on the data symbols received from the stations participating in two-way relaying on a symbol-by-symbol basis. Simulation results performed for two-way relaying with physical network coding reported in this paper confirm the superiority of the proposed detector when compared with the standard physical network coding solution applied in the relay node.