With the immense opportunities to make a communication network programmable, the virtualization of network functions and software defined networking are gaining momentum in both industry and research circles, being a fundamental skill-set for both electrical engineers and computer scientists. Therefore, in this article, we present and evaluate the educational framework for Service Function Chaining (SFC) practical teaching to undergraduate students aiming to prepare them for future Information and Communication Technologies (ICT) and communication networks market that will demand skillful professionals in the domain. The educational framework was designed for the Network Management course at the University of Antwerp, with the goal to bridge the gap between network programmability concepts applied in industry and those taught at the University. We evaluate the educational framework with two extensive surveys as a feedback from students that provided us with the opportunity to measure and quantify students’ experience and satisfaction with the framework. In particular, based on the challenging environment imposed by COVID-19, we identify the gaps in this educational framework and address improvements for both theoretical and practical components according to the students’ needs. Our educational framework and the thorough evaluation serve as a useful guideline on how to modernize the engineering courses and keep up with the pace of technology.

The plethora of heterogeneous and diversified services in 5G and beyond requires from networks to be flexible, adaptable, and programmable, i.e., to be able to correspondingly adapt to changes. As human intervention might significantly increase delays in MANagement and Orchestration (MANO) operations, automation and intelligence become imperative for orchestrating services and resources, especially the ones with stringent requirements for latency and capacity, such as Vehicle-to-Everything (V2X) services. As virtualization and Artificial Intelligence (AI) promise to mitigate those challenges towards enabling true automation in MANO operations, in this paper we present our effort towards building and fully utilizing the real-life testbeds, such as Smart Highway and Virtual Wall, located in Belgium, to conduct realistic experimentation and validation of distributed orchestration intelligence in a dynamic network such as V2X system.

Next-generation mobile networks are expected to flaunt highly (if not fully) automated management. To achieve such a vision, Artificial Intelligence (AI) and Machine Learning (ML) techniques will be key enablers to craft the required intelligence for networking, i.e., Network Intelligence (NI), empowering myriad of orchestrators and controllers across network domains. In this paper, we elaborate on the DAEMON architectural model, which proposes introducing a NI Orchestration layer for the effective end-to-end coordination of NI instances deployed across the whole mobile network infrastructure. Specifically, we first outline requirements and specifications for NI design that stem from data management, control timescales, and network technology characteristics. Then, we build on such analysis to derive initial principles for the design of the NI Orchestration layer, focusing on (i) proposals for the interaction loop between NI instances and the NI Orchestrator, and (ii) a unified representation of NI algorithms based on an extended MAPE-K model. Our work contributes to the definition of the interfaces and operation of a NI Orchestration layer that foster a native integration of NI in mobile network architectures.

As manual Management and Orchestration (MANO) of services and resources might delay the execution of MANO operations and negatively impact the performance of 5G and beyond Vehicle-to-Everything (V2X) services, applying AI in MANO to enable automation and intelligence is an imperative. The Network Function Virtualization (NFV), Software Defined Networking (SDN), and Artificial Intelligence (AI), could all together mitigate those challenges, and enable true automation in MANO operations. Thus, in this demo paper we will showcase the use of real-life testbed environments (Smart Highway and Virtual Wall, Belgium) and the Proof-of-Concept that we build to conduct realistic experimentation and validation of intelligent and distributed MANO in a dynamic network such as a V2X system.

In this paper we demonstrate a framework to support research on Cooperative Awareness Messages (CAMs) through a monitoring dashboard, deploying a portable environment named CAM Application Framework (CAMAF); it manages the received CAMs and updates a corresponding specific monitor for each active Cooperative Intelligent Transportation System (C-ITS) entity. Each monitor is configurable by choosing CAM fields and making or changing algorithms to display the desired information. We have tested our proposal in a C-ITS testbed with real live traffic in the SmartHighway localted in Antwerp, Belgium.

In this paper, we study and present a management and orchestration framework for vehicular communications, which enables service continuity for the vehicle via an optimized application-context relocation approach. To optimize the transfer of the application-context for Connected and Automated Mobility (CAM) services, our MEC orchestrator performs prediction of resource availability in the edge infrastructure based on the Long Short-Term Memory (LSTM) model, and it makes a final decision on relocation by calculating the outcome of a Multi-Criteria Decision-Making (MCDM) algorithm, taking into account the i) resource prediction, ii) latency and bandwidth on the communication links, and iii) geographical locations of the vehicle and edge hosts in the network infrastructure. Furthermore, we have built a proof-of-concept for the orchestration framework in a real-life distributed testbed environment, to showcase the efficiency in optimizing the edge host selection and application context relocation towards achieving continuity of a service that informs vehicle about the driving conditions on the road.

In the context of public safety, 5G offers great opportunities towards enhancing mission-critical services, by running network functions at the network edge to provide reliable and low-latency services. This demo introduces an on-demand Back Situation Awareness (BSA) application service, in a multi-domain scenario, enabling early notification for vehicles of an approaching Emergency Vehicle (EmV), indicating its Estimated Time of Arrival (ETA). The application provides the drivers ample time to create a safety corridor for the EmV to pass through unhindered in a safe manner thereby increasing the mission success. For this demo, we have developed an orchestrated MEC platform on which we have implemented the BSA service following modern cloud-native principles, based on Docker and Kubernetes.

The educational courses that fall into Science, Technology, Engineering and Math (STEM) category require an extensive practical training in laboratories, in order to build and strengthen students’ skills, thereby preparing them for a future job market. In particular, the significant advancements in computer science and engineering press an urgent need to rethink the core of the existing academic courses, their objectives, and the tools for the practical work, due to the need to maintain the balance between the knowledge that academia provides to the students and the actual requirements for students’ future job vacancies. To this end, our educational research includes the design and development of two different types of laboratories, i.e., a low-cost Raspberry Pi-based laboratory, and a laboratory in the cloud, for the practical teaching of the course Distributed systems. In this paper, we present the valuable feedback from our undergraduate students for both types of the aforementioned experimentation approaches, thereby unraveling the pros and cons of both, and analyzing the existing challenges that still need to be properly tackled.

With the advancements in SDN and NFV, both applications and network functions can be re-designed, and deployed at more appropriate locations. Thanks to the MEC platforms, cloud-alike service deployments are offered to the users/vehicles at closer proximity. However, MEC deployments are usually i) constrained in resources, ii) contain heterogeneous and distributed network and computing resources, and iii) cover narrower region that constrains service continuity due to the high mobility of vehicles. Thus, in this paper, we present our approach on collocating MEC platforms with roadside infrastructure (i.e., RSUs) in order to improve the QoS of infotainment services for vehicles on the smart highway. We tackle both challenges presented above by deploying MEC platforms along the highway, thereby having distributed control over each MEC host in the form of Kubernetes master nodes, and one powerful and yet centralized orchestrator in the cloud. Our approach is one of the earliest attempts to collocate MEC with the RSU, and to test the benefits of the smart application placement in a realistic vehicular environment.

5G has opened up possibilities of introducing new use cases and business models that could not be perceived before. In the context of public safety, 5G offers immense opportunities towards enhancing mission success and situation awareness during emergency management. This paper introduces Back-Situation Awareness (BSA) application enabling early warning/notification to vehicles of an approaching emergency vehicle indicating its presence and the time it will arrive. Such an application is expected to give drivers enough time to create a safety corridor for the emergency vehicle to pass through safely and unhindered. We provide details on the system and application design of the BSA application leveraging Multi-Access Edge Computing (MEC) systems that complement the 5G mobile communication system. An evaluation of the application is provided by using data measurements and indicating the accuracy of the computation and notification of the Estimated Time of Arrival (ETA) based on the ETSI C-ITS protocol messages.

The concept of Massive Open Online Course (MOOC) brings the opportunity to adjust both the study content, and the context, based on the teaching needs. Therefore, in this paper, we present our best practices on enabling remote networking laboratories via Blackboard platform, including the Blackboard Collaborate Ultra extension, in order to efficiently react to the challenges of imminent campus closure imposed by COVID-19 breakout. We present an extensive survey as a feedback from students, which allowed us to measure and to quantify students’ experience and satisfaction with the remote teaching setup that successfully served 45 enrolled students. As the results bring the positive attitude towards practices presented in this paper, such teaching practices will foster some of the critical skills nowadays, such as collaboration, self-driven learning, and problem solving, and they can also serve as a successful example on how to efficiently cope with the limited access to traditional classroom resources within various courses.