Software-Defined Networking (SDN) and Network Function Virtualization (NFV) technologies enable a major technological breakthrough for telecom operators' networks. In this paper, we present the NFV over IP over WDM (NIW) library, an open-source framework for SDN/NFV metropolitan networks, created in the context of the Metro-Haul project. NIW is a library added to the Net2Plan open-source network planning software, specifically to model, provision, design and evaluate SDN/NFV networks. We introduce the major components and functionalities of NIW in a tutorial manner, including an Excel file loader meant to ease data loading for its manipulation through NIW data structure and its available methods. Finally, an illustrative example shows the basic library usage taking into account NFV over IP over WDM network resources in a reference topology.

After more than ten years of research and development, Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are finally going mainstream. The fifth generation telecommunication standard (5G) will make use of novel technologies to create increasingly intelligent and autonomous networks. The METRO-HAUL project proposes an advanced SDN/NFV metro-area infrastructure based on an optical backbone interconnecting edge-computing nodes, to support 5G and advanced services. In this work, we present the METRO-HAUL planning tool subsystem that aims to optimize network resources from two different perspectives: off-line network design and on-line resource allocation. Off-line network design algorithms are mainly devoted to capacity planning. Once network infrastructure is in production stages and operational, on-line resource allocation takes into account flows generated by end-user-oriented services that have different requirements in terms of bandwidth, delay, Quality Of Service (QoS) and set of VNFs to be traversed. Through the paper, we describe the components inside the planning tool, which compose a framework that enables intelligent optimization algorithms based on Machine Learning (ML) to assist the control plane in taking strategic decisions. The proposed framework aims to guarantee a fair behavior towards past, current and future requests as network resource allocation decisions are assisted with ML approaches. Additionally, interaction schemes are proposed between the open-source JAVA-based Net2Plan tool, ML libraries and algorithms in Python easing algorithm development and prototyping for rapid interaction with SDN/NFV control and orchestration modules.

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The Industry 4.0 (I4.0) aims to develop a framework where the new technologies interoperate with each other and with employees, creating a smart and efficient environment. Although there are many public and private initiatives focused on boosting the deployment of I4.0 in all sectors worldwide, the adoption is slower than expected. One of the main reasons is the lack of training in those technologies involved in I4.0, the so-called key-enabling technologies (KET). In this article, the current status of I4.0 adoption from the industry, employees, and training point of view is analyzed. The lack of I4.0 competences in the curricula of vocational education training (VET) and higher education (HE) is also highlighted. Finally, the European innovative training action IN4WOOD is presented as a successful open and free training tool developed to offer students, employees, and managers an easy way to learn, use, and deploy KET of I4.0. Although the main target users of the training action are those in the furniture and woodworking sector, it has been designed to be useful also for users in other business sectors. The training tool is composed of more than 300 video learning pills, practical use cases, gamification, and evaluation test for measuring the level of knowledge acquired. The training tool has been tested in a pilot launched in four European countries. The results from the pilot prove that the IN4WOOD training helps to fill the skill gaps identified in the current VET/HE students and improves the competitiveness of employees, managers, and enterprises.

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