Validation of SDN policies: a property-based testing perspective

[Abstract] Software-defined networks are being widely adopted and used in large and complex networks supporting critical operations. Their increasing importance highlights the need for effective validation of SDN topologies and routing policies both prior and during operation. The policies that configure an SDN deployment come from several, possibly conflicting sources. This may lead to undesired effects such as node isolation, network partitions, performance drops and routing loops. Such effects can be formulated as automatically testable reusable conditions using property-based testing (PBT). This approach allows to automatically determine and formulate as a counterexample the minimum set of conflicting rules. The approach is especially useful when policies are configured in an incremental manner. PBT techniques are particularly good at automatic counterexample shrinking and have the potential of being extremely effective in this area.Swedish Foundationfor Strategic Research; RIT17-0035

Secure optical layer flexibility in 5G networks

We propose an adaptive resource allocation framework for on-demand communications in a software-defined mobile fronthaul (MFH) network that supports dynamic processing resource sharing. Our theoretical and experimental studies point to the feasibility of secure bidirectional transmission with guaranteed bit error rate (BER) service using adaptive modulation and coding.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Caching as an Image Characterization Problem using Deep Convolutional Neural Networks

Caching of popular content closer to the mobile user can significantly increase overall user experience as well as network efficiency by decongesting backbone network segments in the case of congestion episodes. In order to find the optimal caching locations, many conventional approaches rely on solving a complex optimization problem that suffers from the curse of dimensionality, which may fail to support online decision making. In this paper we propose a framework to amalgamate model based optimization with data driven techniques by transforming an optimization problem to a grayscale image and train a convolutional neural network (CNN) to predict optimal caching location policies. The rationale for the proposed modelling comes from CNN's superiority to capture features in grayscale images reaching human level performance in image recognition problems. The CNN is trained with optimal solutions and numerical investigations reveal that the performance can increase by more than 400% compared to powerful randomized greedy algorithms. To this end, the proposed technique seems as a promising way forward to the holy grail aspect in resource orchestration which is providing high quality decision making in real time.Comment: 7 pages, 5 figure

5G network slicing with QKD and quantum-safe security

We demonstrate how the 5G network slicing model can be extended to address data security requirements. In this work we demonstrate two different slice configurations, with different encryption requirements, representing two diverse use-cases for 5G networking: namely, an enterprise application hosted at a metro network site, and a content delivery network. We create a modified software-defined networking (SDN) orchestrator which calculates and provisions network slices according to the requirements, including encryption backed by quantum key distribution (QKD), or other methods. Slices are automatically provisioned by SDN orchestration of network resources, allowing selection of encrypted links as appropriate, including those which use standard Diffie-Hellman key exchange, QKD and quantum-resistant algorithms (QRAs), as well as no encryption at all. We show that the set-up and tear-down times of the network slices takes of the order of 1-2 minutes, which is an order of magnitude improvement over manually provisioning a link today

The Evolution of 5G Communications within the Scope of the Fourth Industrial Revolution

A Quarta Revolução Industrial é uma consequência da última transformação digital e consiste na substituição de seres humanos por robôs. Está associada à utilização massiva de robôs, inteligência artificial, grandes dados, Internet das Coisas (IoT), computação quântica ou impressão 3D. A transformação digital e a transformação ambiental estão amplamente associadas, uma vez que a primeira permite uma utilização mais eficiente dos recursos, o que tende a reduzir a pegada de carbono, e permite a geração de energias renováveis. A Quinta Geração de Comunicações Celulares (5G) é disruptiva, uma vez que consiste numa mudança de paradigma relacionado com as gerações anteriores. As Comunicações 5G dão uma forte contribuição para a implementação da Quarta Revolução Industrial numa vasta gama de áreas, tais como em veículos autónomos, cidades inteligentes, indústrias e agricultura inteligentes, cirurgias remotas, etc. Enquanto as comunicações 5G visam implementar alguns dos requisitos da Quarta Revolução Industrial, a Sexta Geração de Comunicações Celulares (6G), prevista para 2030, visa complementar essa implementação de uma forma mais profunda.The Fourth Industrial Revolution is a consequence of the latest digital transformation and consists of the replacement of humans by robots. It is associated to the massive use of robots, artificial intelligence, big data, Internet of Things (IoT), quantum computing or 3D printing. Digital transformation and environmental transformation are widely associated as the former allows a more efficient use of the resources, which tends to reduce the carbon footprint, and allows the generation of renewable energies. The Fifth Generation of Cellular Communications (5G) is disruptive, as it consists of a change of paradigm relating to the previous generations. 5G Communications give a strong contribution to the implementation of the Fourth Industrial Revolution in a wide range of areas, such as in autonomous vehicles, smart cities, smart industries and agriculture, remote surgeries, etc. While 5G communications aim to implement some of the requirements of the Fourth Industrial Revolution, the Sixth Generation of Cellular Communications (6G), expected by 2030, aims to complement such implementation in a deeper manner.La Cuarta Revolución Industrial es una consecuencia de la última transformación digital y consiste en la sustitución de los humanos por robots. Está asociada al uso masivo de robots, inteligencia artificial, big data, Internet de las Cosas (IoT), computación cuántica o impresión 3D. La transformación digital y la transformación medioambiental están ampliamente asociadas ya que la primera permite un uso más eficiente de los recursos, lo que tiende a reducir la huella de carbono, y permite la generación de energías renovables. La Quinta Generación de Comunicaciones Celulares (5G) es disruptiva, ya que consiste en un cambio de paradigma respecto a las generaciones anteriores. Las comunicaciones 5G contribuyen fuertemente a la implementación de la Cuarta Revolución Industrial en una amplia gama de áreas, como en los vehículos autónomos, las ciudades inteligentes, las industrias y la agricultura inteligentes, las cirugías a distancia, etc. Mientras que las comunicaciones 5G pretenden implementar algunos de los requisitos de la Cuarta Revolución Industrial, la Sexta Generación de Comunicaciones Celulares (6G), prevista para 2030, pretende complementar dicha implementación de manera más profunda.info:eu-repo/semantics/acceptedVersio

Deep Reinforcement Learning for Resource Management in Network Slicing

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves challenging technical issues and urgently looks forward to intelligent innovations to make the resource management consistent with users' activities per slice. In that regard, deep reinforcement learning (DRL), which focuses on how to interact with the environment by trying alternative actions and reinforcing the tendency actions producing more rewarding consequences, is assumed to be a promising solution. In this paper, after briefly reviewing the fundamental concepts of DRL, we investigate the application of DRL in solving some typical resource management for network slicing scenarios, which include radio resource slicing and priority-based core network slicing, and demonstrate the advantage of DRL over several competing schemes through extensive simulations. Finally, we also discuss the possible challenges to apply DRL in network slicing from a general perspective.Comment: The manuscript has been accepted by IEEE Access in Nov. 201