Network emulation focusing on QoS-Oriented satellite communication

This chapter proposes network emulation basics and a complete case study of QoS-oriented Satellite Communication

MeDICINE: Rapid Prototyping of Production-Ready Network Services in Multi-PoP Environments

Virtualized network services consisting of multiple individual network functions are already today deployed across multiple sites, so called multi-PoP (points of presence) environ- ments. This allows to improve service performance by optimizing its placement in the network. But prototyping and testing of these complex distributed software systems becomes extremely challenging. The reason is that not only the network service as such has to be tested but also its integration with management and orchestration systems. Existing solutions, like simulators, basic network emulators, or local cloud testbeds, do not support all aspects of these tasks. To this end, we introduce MeDICINE, a novel NFV prototyping platform that is able to execute production-ready network func- tions, provided as software containers, in an emulated multi-PoP environment. These network functions can be controlled by any third-party management and orchestration system that connects to our platform through standard interfaces. Based on this, a developer can use our platform to prototype and test complex network services in a realistic environment running on his laptop.Comment: 6 pages, pre-prin

Model-Driven Cyber Range Training: A Cyber Security Assurance Perspective

Security demands are increasing for all types of organisations, due to the ever-closer integration of computing infrastructures and smart devices into all aspects of the organisational operations. Consequently, the need for security-aware employees in every role of an organisation increases in accordance. Cyber Range training emerges as a promising solution, allowing employees to train in both realistic environments and scenarios and gaining hands-on experience in security aspects of varied complexity, depending on their role and level of expertise. To that end, this work introduces a model-driven approach for Cyber Range training that facilitates the generation of tailor-made training scenarios based on a comprehensive model-based description of the organisation and its security posture. Additionally, our approach facilitates the auto- mated deployment of such training environments, tailored to each defined scenario, through simulation and emulation means. To further highlight the usability of the proposed approach, this work also presents scenarios focusing on phishing threats, with increasing level of complexity and difficulty

Accelerated physical emulation of Bayesian inference in spiking neural networks

The massively parallel nature of biological information processing plays an important role for its superiority to human-engineered computing devices. In particular, it may hold the key to overcoming the von Neumann bottleneck that limits contemporary computer architectures. Physical-model neuromorphic devices seek to replicate not only this inherent parallelism, but also aspects of its microscopic dynamics in analog circuits emulating neurons and synapses. However, these machines require network models that are not only adept at solving particular tasks, but that can also cope with the inherent imperfections of analog substrates. We present a spiking network model that performs Bayesian inference through sampling on the BrainScaleS neuromorphic platform, where we use it for generative and discriminative computations on visual data. By illustrating its functionality on this platform, we implicitly demonstrate its robustness to various substrate-specific distortive effects, as well as its accelerated capability for computation. These results showcase the advantages of brain-inspired physical computation and provide important building blocks for large-scale neuromorphic applications.Comment: This preprint has been published 2019 November 14. Please cite as: Kungl A. F. et al. (2019) Accelerated Physical Emulation of Bayesian Inference in Spiking Neural Networks. Front. Neurosci. 13:1201. doi: 10.3389/fnins.2019.0120