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.

Spectrum sharing is a reality closer than one might think. Dynamic and intelligent spectrum allocation, where different networks collaborate to optimize spectrum usage jointly, is required to overcome spectrum scarcity. Artificial Intelligence (AI) can play a major role to solve this complex problem in dynamic environments with continuously changing data requirements. A part of the problem can be solved by using smart AI-enabled flow control. Smart flow control can have a major impact on the spectrum footprint of mobile networks where it can optimize the Quality of Service for the own and neighboring networks. This paper presents the architecture and the basic principles of the dynamic spectrum footprint control based on flow prioritization of the SCATTER radio system, a wireless endto-end communication system that participated in the DARPA Spectrum Collaboration Challenge. The flow control mechanism is a policy-based framework.

Wireless network technologies are becoming more and more popular. Because of this, important parts of the wireless spectrum become overloaded. Static spectrum allocation, which has been the norm for decades, is not suitable anymore. To maintain the high demand for spectrum and the continuous development of new wireless technologies, there is a need for an intelligent, dynamic spectrum allocation mechanism, where different network technologies collaboratively optimize the spectrum usage. New wireless network paradigms, such as Neutral Host Networks (NHNs) and private 5G, require a smart, spectrum-footprint-aware flow control algorithm to overcome the spectrum scarcity in collaborative way. This article presents a strategy, vision and flow control mechanism to implement collaboration in a Quality of Service (QoS)-driven way. The solution in this article is based on policies which may activate depending on its current and neighbor's network states. Through a flow ordering and selection strategy, these policies optimize the spectrum footprint, based on the performance and QoS-requirements of the own and surrounding networks. The proposed algorithm is tested extensively and validated on a large scale during the DARPA Spectrum Collaboration Challenge (SC2) competition. The results of the SC2 final event and intermediate scrimmages showed that the proposed approach increased the score, indicating increased inter-network collaboration was achieved.

Network function (NF) virtualization is a promising way for service providers to improve configurability of the offered network services. First, NFs and their corresponding flows can be embedded in the substrate network in an automated way. Second, the modularity offered by the description of a service as a composition of NFs and their interconnecting virtual links, enables tuning of the service's logical structure. A major challenge with respect to the management of these environments is the placement of service requests in the physical infrastructure, comprising NFs that can only be instantiated on particular hosts, due to latency, legislation, or hardware-related constraints. These location constraints are known to complicate the resource allocation. This paper proposes two service placement algorithms with better coordination between the composition and embedding phases. We formulate the combined service embedding and chain composition problem as an integer linear program and then, propose a fast heuristic based on greedy chain selection that iteratively adds virtual NF chains to the composition and embeds them at the same time. Simulation experiments show that the proposed cross-layer optimization can significantly increase the acceptance ratio and reduce the embedding cost.

Rate adaptation in 802.11 WLANs has received a lot of attention from the research community, with most of the proposals aiming at maximising throughput based on network conditions. Considering energy consumption, an implicit assumption is that optimality in throughput implies optimality in energy efficiency, but this assumption has been recently put into question. In this paper, we address via analysis and experimentation the relation between throughput performance and energy efficiency in multi-rate 802.11 scenarios. We demonstrate the trade-off between these performance figures, confirming that they may not be simultaneously optimised, and analyse their sensitivity towards the energy consumption parameters of the device. Our results provide the means to design novel rate adaptation schemes that takes energy consumption into account.

Increasing the density of access points is one of the most effective mechanisms to cope with the growing traffic demand in wireless networks. To prevent energy wastage at low loads, a resource-on-demand (RoD) scheme is required to opportunistically (de)activate access points as network traffic varies. While previous publications have analytically modeled these schemes in the past, they have assumed that resources are immediately available when activated, an assumption that leads to inaccurate results and might result in inappropriate configurations of the RoD scheme. In this paper, we analyze a general RoD scenario with N access points and non-zero start-up times. We first present an exact analytical model that accurately predicts performance but has a high computational complexity, and then derive a simplified analysis that sacrifices some accuracy in exchange for a much lower computational cost. To illustrate the practicality of this model, we present the design of a simple configuration algorithm for RoD. Simulation results confirm the validity of the analyses, and the effectiveness of the configuration algorithm.