Communication networks are the key enabling technology for our digital society. In order to sustain their critical services in the future, communication networks need to flexibly accommodate new requirements and changing contexts due to emerging diverse applications. In contrast to traditional networking technologies, software-oriented networking concepts, such as software-defined networking (SDN) and network function virtualization (NFV), provide ample opportunities for highly flexible network operations, enabling fast and simple adaptation of network resources and flows. This paper identifies the opportunities and challenges of adaptable softwarized networks and introduces a conceptual framework for adaptations in softwarized networks. We first explain how softwarized networks contribute to network adaptability through the functional primitives observation, composition, and control. We review the wide range of options for fine-granular observations as well as fine-granular composition and control provided by SDN and NFV. The multitude of fine-granular ``tuning knobs'' in adaptable softwarized networks complicates the decision making, which is the main focus of this paper. We propose to enhance the functional primitives observation, composition, and control with data-driven decision making, e.g., machine learning modules, resulting in deep observation, composition, and control. The data-driven decision making modules can learn and react to changes in the environment, e.g., new flow demands, so as to support meaningful decision making for adaptation in softwarized networks. Finally, we make the case for employing the concept of empowerment to realize truly ``self-driving'' networks.

In the context of the 5G ecosystem, the integration between the terrestrial and satellite networks is envisioned as a potential approach to further enhance the network capabilities. In light of this integration, the satellite community is revisiting its role in the next generation 5G networks. Emerging technologies such as Software-Defined Networking (SDN) which rely on programmable and reconfigurable concepts, are foreseen to play a major role in this regard. Therefore, an interesting research topic is the introduction of management architecture solutions for future satellite networks driven by means of SDN. This anticipates the separation of the data layer from the control layer of the traditional satellite networks, where the control logic is placed on programmable SDN controllers within traditional satellite devices. While a centralized control layer promises delay reductions, it introduces additional overheads due to reconfiguration and migration costs. In this paper, we propose a method to quantify the overhead imposed on the network by the aforementioned parameters while investigating the use-case scenario of an SDN-enabled satellite space segment. We make use of an optimal controller placement and satellite-to-controller assignment which minimizes the average flow setup time with respect to varying traffic demands. Furthermore, we provide insights on the network performance with respect to the migration and reconfiguration cost for our proposed SDN-enabled architecture. Finally, we compare our proposed space segment SDN-enabled architecture with alternative solutions in the state-of-the-art given the aforementioned performance metrics.

Service function chains (SFCs) are an ordered set of virtual network functions (VNFs) which can realize a specific network service. Enabled by virtualization technologies, these VNFs are hosted on physical machines (PMs), and interconnected by network switches. In today networks, these resources are usually under-utilized and/or over-provisioned, resulting in power-inefficient deployments. To improve power-efficiency, SFCs should be deployed utilizing the minimum number of PMs and network equipment, which are not concomitant. Considering the existing PM and switch power consumption models and their resource constraints, we formulate the power-aware and delay-constrained joint VNF placement and routing (PD-VPR) problem as an Integer Linear Program (ILP). Due to the NP-completeness of the problem, we propose Holu , a fast heuristic framework that efficiently solves the PD-VPR problem in an online manner. Specifically, Holu decomposes the PD-VPR into two sub-problems and solve them sequentially: i) a VNF placement problem that consists of mapping the VNFs to PMs using a centrality-based ranking method, and ii) a routing problem that efficiently splits the delay budget between consecutive VNFs of the SFC, and finds a Delay-Constrained Least-Cost (DCLC) shortest-path through the selected PMs (hosting VNFs) using the Lagrange Relaxation based Aggregated Cost (LARAC) algorithm. Our simulation results indicate that Holu outperforms the state-of-the-art algorithms in terms of total power consumption and acceptance rate by 24.7% and 31%, respectively.

With the rapid growth of user traffic, service innovation, and the persistent necessity to reduce costs, today's mobile operators are faced with several challenges. In networking, two concepts have emerged aiming at cost reduction, increase of network scalability and deployment flexibility, namely Network Functions Virtualization (NFV) and Software Defined Networking (SDN). NFV mitigates the dependency on hardware, where mobile network functions are deployed as software virtual network functions on commodity servers at cloud infrastructure, i.e., data centers. SDN provides a programmable and flexible network control by decoupling the mobile network functions into control plane and data plane functions. The design of the next generation mobile network (5G) requires new planning and dimensioning models to achieve a cost optimal design that supports a wide range of traffic demands. We propose three optimization models that aim at minimizing the network load cost as well as data center resources cost by finding the optimal placement of the data centers as well the SDN and NFV mobile network functions. The optimization solutions demonstrate the trade-offs between the different data center deployments, i.e., centralized or distributed, and the different cost factors, i.e., optimal network load cost or data center resources cost. We propose a Pareto optimal multi-objective model that achieves a balance between network and data center cost. Additionally, we use prior inference, based on the solutions of the single objectives, to pre-select data center locations for the multi-objective model that results in reducing the optimization complexity and achieves savings in run time while keeping a minimal optimality gap.

The first release of 5G technology is being rolled out worldwide. In parallel, 3GPP is constantly adding new features to upcoming releases covering well-known use cases. This raises the questions i.) when will 6G be introduced?, ii.) how can 6G be motivated for the stakeholders, and iii.) what are the 6G use cases? In this work, we present the perspective of these stakeholders, namely the network operators, manufacturers, and verticals, identifying potential 5G shortcomings and the remaining 6G solution space. We will highlight the Metaverse as the enabler for 6G addressing omnipresent daily challenges and the upcoming energy problem.

Industrial networks require real-time guarantees for the flows they carry. That is, flows have hard end-to-end delay requirements that have to be deterministically guaranteed. While proprietary extensions of Ethernet have provided solutions, these often require expensive forwarding devices. The rise of software-defined networking (SDN) opens the door to the design of centralized traffic engineering frameworks for providing such real-time guarantees. As part of such a framework, a network model is needed for the computation of worst-case delays and for access control. In this paper, we propose two network models based on network calculus theory for providing deterministic services (DetServ). While our first model, the multi-hop model (MHM), assigns a rate and a buffer budget to each queue in the network, our second model, the threshold-based model (TBM), simply fixes a maximum delay for each queue. Via a packet-level simulation, we confirm that the delay bounds guaranteed by both models are never exceeded and that no packet loss occurs. We further show that the TBM provides more flexibility with respect to the characteristics of the flows to be embedded and that it has the potential of accepting more flows in a given network. Finally, we show that the runtime cost for this increase in flexibility stays reasonable for online request processing in industrial scenarios.