Energy-efficient traffic engineering

The energy consumption in telecommunication networks is expected to grow considerably, especially in core networks. In this chapter, optimization of energy consumption is approached from two directions. In a first study, multilayer traffic engineering (MLTE) is used to assign energy-efficient paths and logical topology to IP traffic. The relation with traditional capacity optimization is explained, and the MLTE strategy is applied for daily traffic variations. A second study considers the core network below the IP layer, giving a detailed power consumption model. Optical bypass is evaluated as a technique to achieve considerable power savings over per-hop opticalelectronicoptical regeneration. Document type: Part of book or chapter of boo

Topology Adaption for the Quantum Internet

In the quantum repeater networks of the quantum Internet, the varying stability of entangled quantum links makes dynamic topology adaption an emerging issue. Here we define an efficient topology adaption method for quantum repeater networks. The model assumes the random failures of entangled links and several parallel demands from legal users. The shortest path defines a set of entangled links for which the probability of stability is above a critical threshold. The scheme is utilized in a base-graph of the overlay quantum network to provide an efficient shortest path selection for the demands of all users of the network. We study the problem of entanglement assignment in a quantum repeater network, prove its computational complexity, and show an optimization procedure. The results are particularly convenient for future quantum networking, quantum-Internet, and experimental long-distance quantum communications.Comment: 17 pages, Journal-ref: Quant. Inf. Proc. (2018

Towards a Distributed Quantum Computing Ecosystem

The Quantum Internet, by enabling quantum communications among remote quantum nodes, is a network capable of supporting functionalities with no direct counterpart in the classical world. Indeed, with the network and communications functionalities provided by the Quantum Internet, remote quantum devices can communicate and cooperate for solving challenging computational tasks by adopting a distributed computing approach. The aim of this paper is to provide the reader with an overview about the main challenges and open problems arising with the design of a Distributed Quantum Computing ecosystem. For this, we provide a survey, following a bottom-up approach, from a communications engineering perspective. We start by introducing the Quantum Internet as the fundamental underlying infrastructure of the Distributed Quantum Computing ecosystem. Then we go further, by elaborating on a high-level system abstraction of the Distributed Quantum Computing ecosystem. Such an abstraction is described through a set of logical layers. Thereby, we clarify dependencies among the aforementioned layers and, at the same time, a road-map emerges

MuxViz: A Tool for Multilayer Analysis and Visualization of Networks

Multilayer relationships among entities and information about entities must be accompanied by the means to analyze, visualize, and obtain insights from such data. We present open-source software (muxViz) that contains a collection of algorithms for the analysis of multilayer networks, which are an important way to represent a large variety of complex systems throughout science and engineering. We demonstrate the ability of muxViz to analyze and interactively visualize multilayer data using empirical genetic, neuronal, and transportation networks. Our software is available at https://github.com/manlius/muxViz.Comment: 18 pages, 10 figures (text of the accepted manuscript

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era