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Reference: TEC2017-90034-C2-2-R

Leaving the current 4th generation of mobile communications behind, 5G will represent a disruptive paradigm shift integrating 5G Radio Access Networks (RANs), ultra-high capacity access/metro/core optical networks and intra-datacenter network and computational resources into a single converged 5G network infrastructure. Thanks to an extensive deployment of network virtualization techniques leveraged by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) technologies, such a 5G network infrastructure will have to be capable of inter-connecting anything (people, things, processes, contents, etc.) anywhere, no matter the geographic location, and over a set of network services truly meeting their diverse communication requirements (e.g., in terms of bandwidth, latency, reliability, etc.). Furthermore, these network services will have to be orchestrated end-to-end over several network and IT resource segments with high scalability, dynamicity and reactivity upon unexpected traffic and resource state changes, all this in an energy-efficient fashion. The ALLIANCE-B project ambitiously aims at architecting, from top to bottom, a converged 5G-enabled network infrastructure satisfying those needs to effectively realize the envisioned upcoming Digital Society.  ALLIANCE-B investigates the appropriateness of several networking solutions for 5G, such as SDN/NFV on top of an ultra-high capacity spatially and spectrally flexible all-optical network infrastructure. Evaluation activities will not only consist of theoretical and simulation-based results, but also experimental activities over representative network test-beds implementing the aforementioned networking solutions for 5G, as a way to completely assess their performance in real network scenarios. ALLIANCE relies on cognitive QoE-driven management and orchestration, which optimises level service quality without network resource over-provisioning. In particular, an ambitious goal of the ALLIANCE-B project is to design and implement Machine Learning (ML) techniques toward optimal end-to-end service provisioning.

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