The combination of 5G and Multi-access Edge Computing (MEC) technologies can bring significant benefits to vehicular networks, providing means for achieving enhanced Quality of Service (QoS), and Quality of Experience (QoE) of wide variety of vehicular applications. Although beneficial in terms of latency reduction, the edge of the architecture for communication networks produces enormous heterogeneity of network services and resources. This challenge becomes even more severe when different administration domains are taken into consideration. Thus, efficient network Management and Orchestration (MANO) of network resources and services are inevitable. As ETSI provided guidelines and standardization for NFV MANO components, the MEC platform can be used to host network services, while MANO systems are in charge of network service management and orchestration. In this paper, we focus on the specific impact that the Virtualized Infrastructure Manager (VIM) has on the performance of the whole MANO system, used for management and orchestration of MEC services and resources in vehicular networks by enabling the on-demand service instantiation, and service teardown. In our testbed-based evaluation, we measured the network service instantiation and termination delays when evaluating: a) OpenStack and Amazon Web Services (AWS) as VIMs for Open Source MANO (OSM), and b) OpenStack and Docker in case of Open Baton. Such performance analysis with a strong experimental component can serve as a baseline for researchers and industry towards exploiting the opportunities that existing MANO solutions provide.

The network edge presses an urgent need for efficient network management and orchestration (MANO), in order to efficiently cope with the wide heterogeneity in services and resources, while providing a low-latency for the hosted services. Based on ETSI standardization, the MEC platform can be managed and orchestrated by NFV MANO components. In this demo, we show how to measure the impact of the Virtualized Infrastructure Manager (VIM), which is a component of the NFV MANO, on the performance of the MANO system. In our testbed-based experimentation, we evaluated the performance in terms of time needed for a MANO system to instantiate/terminate a network service on top of the MEC platform. Open Source MANO (OSM) and Open Baton are used as MANO entities, while for the VIM environments we investigated the impact of OpenStack and Amazon Web Services (AWS) on the above-mentioned OSM, and the impact of OpenStack and Docker on Open Baton.

By providing storage and computational resources at the network edge, which enables hosting applications closer to the mobile users, Multi-Access Edge Computing (MEC) uses the mobile backhaul, and the network core more efficiently, thereby reducing the overall latency. Fostering the synergy between 5G and MEC brings ultra-reliable low-latency in data transmission, and paves the way towards numerous latency-sensitive automotive use cases, with the ultimate goal of enabling autonomous driving. Despite the benefits of significant latency reduction, bringing MEC platforms into 5G-based vehicular networks imposes severe challenges towards poorly scalable network management, as MEC platforms usually represent a highly heterogeneous environment. Therefore, there is a strong need to perform network management and orchestration in an automated way, which, being supported by Software Defined Networking (SDN) and Network Function Virtualization (NFV), will further decrease the latency. With recent advances in SDN, along with NFV, which aim to facilitate management automation for tackling delay issues in vehicular communications, we study the closed-loop life-cycle management of network services, and map such cycle to the Management and Orchestration (MANO) systems, such as ETSI NFV MANO. In this paper, we provide a comprehensive overview of existing MANO solutions, studying their most important features to enable network service and resource orchestration in MEC-enhanced vehicular networks. Finally, using a real testbed setup, we conduct and present an extensive performance analysis of Open Baton and Open Source MANO that are, due to their lightweight resource footprint, and compliance to ETSI standards, suitable solutions for resource and service management and orchestration within the network edge.

Regardless of the context to which it is applied, sharing resources is well-recognized for its considerable benefits. Since 5G networks will be service-oriented, on-demand, and highly heterogeneous, it is utmost important to approach the design and optimization of the network from an end-to-end perspective. In addition, in order to ensure end-to-end performance, this approach has to entail both wireless and optical domains, altogether with the IoT, edge, and cloud paradigms which are an indispensable part of the 5G network architecture. Shifting from the exclusive ownership of network resources toward sharing enables all participants to cope with stringent service requirements in 5G networks, gaining significant performance improvements and cost savings at the same time. The main objective of this paper is to survey the literature on resource sharing, providing an in-depth and comprehensive perspective of sharing by recognizing the main trends, the techniques which enable sharing, and the challenges that need to be addressed. By providing a taxonomy which brings the relevant features of a comprehensive sharing model into focus, we aim to enable the creation of sharing models for more efficient future communication networks. We also summarize and discuss the relevant issues arising from network sharing, that should be properly tackled in the future.

The computer network industry is preparing for the revolution caused by the virtualization of the network infrastructure. The virtualization of network functions, together with the network programmability, is a fundamental skill-set required in both electrical engineers and computer scientists, which will become even more significant in the upcoming years. Therefore, in this article, we present an 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. To structure the content of the framework’s sessions, we explain the fundamental concepts behind the SFC. Moreover, we detail the framework objectives, sessions, and how we plan to evaluate it. We also present facts and works that endorse the hands-on teaching preparing students for the research and development in the new generation communication networks arena. After, we discuss the methodology of the educational framework alignment with joint ACM/IEEE Computer Engineering Curricula for Network Management courses. Next, we present possible outcomes during the course evaluation using the survey. We conclude which this educational framework achieves the necessary core learning outcomes to empower students concerning the practical steps when deploying and managing communication networks and the virtualization techniques which nowadays are being applied.

Due to the varying conditions in traffic and resource availability in networks nowadays, maintaining continuity of network service and satisfying QoS and QoE requirements became a challenging task. If considered in a highly diversified environment in terms of technology and administration, it gets even more complicated, and appropriate service and resource management solutions are mandatory. Thus, the aim of this paper is to present our specific perspective of the ongoing European H2020 5G-CARMEN project, addressing the importance of proactive reconfiguration of network services and their migration between different domains. Leveraging 5G technology and MEC platform, our management platform for automated low-latency-aware VNF placement and migration will enable orchestration of network services and resources across different administrative and technology domains.

An important aspect of managing multi access point (AP) IEEE 802.11 networks is the support for mobility management by controlling the handover process. Most handover algorithms, residing on the client station (STA), are reactive and take a long time to converge, and thus severely impact Quality of Service (QoS) and Quality of Experience (QoE). Centralized approaches to mobility and handover management are mostly proprietary, reactive and require changes to the client STA. In this paper, we first created an Software-Defined Networking (SDN) modular handover management framework called HuMOR, which can create, validate and evaluate handover algorithms that preserve QoS. Relying on the capabilities of HuMOR, we introduce ABRAHAM, a machine learning backed, proactive, handover algorithm that uses multiple metrics to predict the future state of the network and optimize the load to ensure the preservation of QoS. We compare ABRAHAM to a number of alternative handover algorithms in a comprehensive QoS study, and demonstrate that it outperforms them with an average throughput improvement of up to 139%, while statistical analysis shows that there is significant statistical difference between ABRAHAM and the rest of the algorithms.

This article presents a low-cost laboratory that has been designed and developed to enhance learning experience and help students gain skills and knowledge in the field of distributed systems. In order to build a comprehensive distributed file system, we used the laboratory consisting of 40 card-sized Raspberry Pi devices, with the accent on stability, scalability, and low cost. Aiming to assess the impact of this new learning environment on the learning process and its outcomes, we surveyed students following the completion of three project stages during the 17 laboratory exercises in one academic year, ensuring that we maintained the same subjects of study during the experiments. Supported by interesting answers on various sets of questions, we provide a valuable insight into students' experience, obstacles and observations during system's implementation. This particular insight paves the way toward further laboratory improvement, adopting this approach in other courses related to ours, and encouraging teachers to embrace similar practice regardless of the field of education.