Novel networking paradigms based on programmability and softwarization are deeply transforming the architecture and end-to-end management of next-generation mobile networks. This demonstration provides a step beyond in this transformation process by showcasing the full cloud-native deployment of a disaggregated and distributed 5 G mobile network from core to Radio Access Network (RAN) using open-source software. The distinguishing features of the proposed approach are that the orchestration process includes the use of a cloud-based Distributed Unit (DU) communicating through O-RAN Split 7.2 with a Radio Unit (RU) performing over-the-air transmission, the end-to-end configuration of slices with on-demand activation of core network function components, and the dynamic configuration of transport network flows to differently route the traffic of active slices.

Novel networking paradigms based on cloudification and open interfaces are making mobile networks more agile to adapt and meet the requirements of emerging use cases. Based on an automated end-to-end cloud-native open-source deployment of a 5G mobile network with over-the-air capabilities, this demonstration features the inclusion of a cloud-native monitoring system to follow the radio access network (RAN) performance evolution. By just defining the number of gNBs, this monitoring system automatically configures independent data sources and dashboards representing the evolution of received information while such gNBs are progressively created on-demand in a distributed environment.

The architecture and management of nextgeneration mobile networks are integrating novel networking paradigms to support challenging scenarios. This paper presents the design, implementation, and evaluation of a fully cloud-native, end-to-end mobile network integrating O-RAN functionality. Built on top of open-source software, our proposed framework develops a set of cloud-native deployment artefacts-such as Helm Charts and Open Source MANO packages-also released as open-source. This framework supports highly disaggregated and distributed deployments of key network functions, including the$\mathbf{5 G}$core, gNodeB (with decoupled Centralised and Distributed Unit entities), near-RT RIC, and xApps, enabling agile, portable, and flexible instantiations. Experiments show that the full cloudnative mobile network can be instantiated in less than five minutes, achieving a throughput comparable to bare-metal setups. This work highlights significant improvements in deployment efficiency and adaptability, contributing towards automated and autonomous cloud-native Beyond 5G/6G networks.

This tutorial deals with the integration of data engineering with network management and orchestration in telecommunication networks. It provides participants with a comprehensive insight into the use of data engineering to improve the efficiency and performance of telecommunication systems, especially through the use of Artificial Intelligence (AI)/ Machine Learning (ML) technologies in network infrastructures. Practical applications are also demonstrated using relevant case studies to illustrate the implementation of these concepts.

Integrating edge computing with 5G/6G, bolstered by standards such as ETSI Multi-access Edge Computing (MEC), becomes indispensable, particularly in addressing end-to-end latency-sensitive vertical applications. Thus, it is imperative to delve into infrastructure and network considerations, including the relocation of server workloads and optimization of user data planes. Notably, 3GPP facilitates third-party vertical providers’ access to user equipment (UE) metrics and data plane capacities through the Network Exposure Function (NEF). This paper showcases a demonstration that focuses on the seamless coordination of cloud-native workload relocation and user plane function (UPF) selection within the 5G network. Here, the interaction between a vertical provider and the 5G core enables informed decisions aimed at ensuring minimal latency by triggering UPF selection and relocation of containerized servers to the cloud or the edge, illustrating the anticipated trend of increased automation within 6G networks.

Novel networking paradigms, such as virtualization, are transforming mobile networks to support challenging use cases and scenarios. This demonstration presents an on-demand, distributed, and fully cloud-native deployment of a 5G mobile network (including core and RAN) with over-the-air capabilities using open-source software. This deployment strategy provides the flexibility and dynamicity required to manage and orchestrate forthcoming B5G/6G networks, thereby empowering mobile networks to adapt and scale effectively.

Novel networking paradigms are enabling the introduction of innovative vertical use cases and new business relations in the B5G/6G mobile ecosystem. For instance, a use case may require the coordination of domains owned by different operators to provide service continuity and keep offering a vertical network service (NS) in similar conditions after a cross-border situation. This demonstration presents a procedure to perform a proactive service relocation in such a multi-administrative domain scenario considering an automotive use case. This demonstration proposes a cloud-native solution combining multiple enablers to manage the life-cycle of virtualised automotive NSs. During run-time, and upon registration, an AI/ML-based enabler decides proactively on the service relocation moment based on the collected vehicle’s positions and triggers an Integration Fabric enabler following ETSI ZSM guidelines to start a new instance of such automotive NS at the associated ETSI NFV management and orchestration stack present in the neighbouring administrative domain.

Effective and real-time management of data planes becomes of paramount importance to support the ever-increasing challenging use cases and scenarios foreseen in 5G and beyond. In this demonstration, we present a comprehensive showcase of a cloud-native open-source 5G core deployment, realised as a disaggregated non-public network (NPN) spanning multiple locations. Ranging from cloud to edge points of presence, the demonstration establishes an end-to-end experimental platform with over-the-air transmission capabilities, specifically highlighting the on-demand creation and deletion of User-Plane Functions (UPFs). This dynamic deployment approach harnesses the advantages offered by edge locations, empowering the mobile network to adapt and scale as per its specific requirements. Furthermore, our demo elucidates how to use Open Source MANO (OSM) for easing the management operations of network administrators.

The automation and flexibility introduced in the management and orchestration of B5G/6G mobile networks are allowing the creation of innovative vertical use cases considering the coordination of multiple provider domains owned by different operators and/or service providers. The first step to enable this automation is the onboarding of network service artefacts prior to enabling its instantiation in such challenging scenarios. This demonstration focuses on the automatic onboarding operation in a multi-administrative domain scenario. We propose a cloud-native solution deployed at each administrative domain made of an artefact registry management system coupled with an Integration Fabric element following ETSI ZSM guidelines. These elements synchronize between them autonomously and with the associated ETSI NFV management and orchestration stacks ensuring a consistent catalogue of vertical services at the different administrative domains.

The expected complexity of shared 5G/6G cloud and network infrastructures requires a functional management and orchestration (MANO) architecture to automatically provision distinct vertical services. These are mapped as generic network services (NSes) specifying their computing and networking needs: Virtual Network Functions (VNFs) and Virtual Links. We focus on a hierarchical MANO implementation where a Resource Layer orchestrator handles the configuration of the physical infrastructure and exposes an abstract view of it to the upper-layer Service Orchestrator. Two different abstraction philosophies are adopted, namely the Infrastructure Abstraction, which pre-calculates the resource allocations before advertising them, and the Connection Service Abstraction, which exposes potential connectivity services without an actual resource allocation. The resulting abstracted infrastructure becomes the input of the Service Orchestrator to make (placement) decisions fulfilling the NS demands. Thus, a novel placement algorithm is devised which, unlike previous works, can handle NSes with arbitrary VNF topology demands. The performance of the MANO functions is experimentally evaluated by dynamically creating/terminating heterogeneous NSes in terms of: NS blocking, blocked bandwidth/VNF ratio, bandwidth occupancy, and VNF distribution per cloud site. From the results, Connection Service Abstraction does attain a more efficient use of resources and in general, performs better than the Infrastructure Abstraction approach.

Slices are deployed to continuously fulfill requirements from vertical industries. Thus, the management of service level agreements (SLAs) is fundamental. Composite network services (NSs), made up of multiple nested NSs, can be exploited to build slices. Generally, slices may span multiple domains and the different nested NSs may be deployed by different providers, in what is referred to as federated NS. Previous work focused on the architectural design and workflows for the functional deployment of federated services without considering the continuous SLA management. Herein, we cover this gap by presenting a complete workflow, the required interface extensions, and the operation profiling over an experimental testbed to automatically scale nested NSs deployed in a federated domain. Evaluation results show that such scale out/in operations can be performed in tens of seconds (39/31 s, respectively), where the allocation/release of underlying resources account for most of the profiled scaling time (more than 80%).

Heterogeneity is one relevant characteristic of next generation mobile networks. This term embraces not only different kind of infrastructure resources or transmission technologies (e.g., networking vs computing, wireless vs optical), but it also applies to different ways of implementing the network functions (NFs) composing the network services (NSs). This allows the definition of hybrid NSs combining different kind of components, such as physical, virtual, and cloud-native NFs. This demonstration shows the enhancements introduced in the 5Growth platform to manage physical NFs, hence increasing the capabilities of this platform to cope with more heterogeneous hybrid NSs in multi-site scenarios. In particular, we show the deployment of an NS constituted by physical, virtual, and cloud-native NFs implementing an end-to-end service covering access, mobile core and application functionalities.

5G marks the beginning of a revolution in the mobile network ecosystem, transitioning to a network of services to satisfy the demands of new telecom players, the vertical industries. This revolution implies a redesign of the overall mobile network architecture to provide it with the automation and programmability capabilities to handle the envisioned heterogeneity, flexibility, and dynamicity. Software Defined Networking (SDN), Network Function Virtualization (NFV), and Network slicing are key enabling concepts to provide such capabilities. They are complementary, but they are still in its infancy and the synergies between them must be exploited to realise the mentioned vision. This thesis contributes to its development and integration in next generation mobile networks by designing an end-to-end (E2E) network service orchestration architecture. This proposed architecture, which is aligned with some guidelines and specifications provided by main standardization bodies, goes beyond current management and orchestration (MANO) platforms to fulfill network service lifetime requirements in heterogeneous multi-technology network infrastructures shared by concurrent instances of diverse network services. Other relevant contribution of this work is the multi-administrative domain capabilities of the proposed architecture, going beyond high-level considerations done in existing literature that prevent its effective realisation. The resulting architecture, workflows, and interfaces are validated and assessed through practical implementations in experimental infrastructures with network service definitions close to real vertical use cases (e.g., automotive and eHealth), which help bridging the gap between network providers and vertical industries stakeholders.

One of the main tasks of new-generation cellular networks is the support of the wide range of virtual services that may be requested by vertical industries, while fulfilling their diverse performance requirements. Such task is made even more challenging by the time-varying service and traffic demands, and the need for a fully-automated network orchestration and management to reduce the service operational costs incurred by the network provider. In this paper, we address these issues by proposing a softwarized 5G network architecture that realizes the concept of ML-as-a-Service (MLaaS) in a flexible and efficient manner. The designed MLaaS platform can provide the different entities of a MANO architecture with already-trained ML models, ready to be used for decision making. In particular, we show how our MLaaS platform enables the development of two ML-driven algorithms for, respectively, network slice subnet sharing and run-time service scaling. The proposed approach and solutions are implemented and validated through an experimental testbed in the case of three different services in the automotive domain, while their performance is assessed through simulation in a large-scale, real-world scenario. In-testbed validation shows that the use of the MLaaS platform within the designed architecture and the ML-driven decision-making processes entail a very limited time overhead, while simulation results highlight remarkable savings in operational costs, e.g., up to 40% reduction in CPU consumption and up to 30% reduction in the OPEX.

Blockchain technologies are gradually being deployed in a variety of industries, including telecommunications. In this paper, due to the strict governance of telecommunication infrastructure, we propose a permissioned distributed ledger (PDL)-based blockchain supported architecture for a network management and orchestration platform. The work focuses on creating a trusted environment for both Cloud Service Providers (CSPs) and Mobile Network Operators (MNOs) for managing the lifecycle of network services (e.g., instantiation, scaling, termination, etc.) in a multi-cloud environment. We also validate our proposed approach with an experimental scenario using the Quorum blockchain network (BCN) to measure various performance metrics (e.g., number of transactions and blocks, time to write, and transactions per second) of different service orchestrator (SO)-related instantiation metrics. Our evaluation results show that the values for the service instantiation time and the corresponding BCN metrics can be completely different, suggesting that some logs arrive very quickly and generate a high transaction load, while others take longer and generate a low number of transactions. As a solution, at the end of the paper, we also provide some recommendations for appropriate optimizations during transfer of SO-related logs to BCNs and some observed challenges.

Network service federation in 5G/B5G networks enables service providers to extend service offering by collaborating with peering providers. Realizing this vision requires interoperability among providers towards end-to-end service orchestration across multiple administrative domains. Smart admission control is fundamental to make such extended offering profitable. Without prior knowledge of service requests, the admission controller (AC) either determines the domain to deploy each demand or rejects it to maximize the long-term average profit. In this paper, we first obtain the optimal AC policy by formulating the problem as a Markov decision process, which is solved through the policy iteration method. This provides the theoretical performance bound under the assumption of known arrival and departure rates of demands. Then, for practical solutions to be deployed in real systems, where the rates are not known, we apply the Q-Learning and R-Learning algorithms to approximate the optimal policy. The extensive simulation results show that learning approaches outperform the greedy policy and are capable of getting close to optimal performance. More specifically, R-learning always outperformed the rest of practical solutions and achieved an optimality gap of 3-5% independent of the system configuration, while Q-Learning showed lower performance and depended on discount factor tuning.

In 5G networks, heterogeneous vertical services with different requirements are rolled out over a common multi-technology infrastructure. A resource orchestrator entity automatically coordinates the operations and functions to support the service's lifecycle management (i.e., creation, update and termination). Moreover, it is essential that service needs are continuously assured even if transport network anomalies (e.g., link failures) occur. Herein, we present an implemented resource orchestrator architecture integrating monitoring capabilities to attain closed-loop operations for: i) gathering monitored information; ii) detecting transport network anomalies; and iii) triggering the required action (e.g., restoration) to keep the service continuity. When a link failure happens, several transport services may be disrupted requiring to be immediately restored. To this end, we propose a novel on-line restoration algorithm called as Global Concurrent Optimization (GCO). The GCO algorithm aims at attaining an enhanced restorability performance compared to a more traditional restoration algorithm (referred to as 1-by-1). Both algorithms are experimentally compared on top of the deployed resource orchestrator architecture. The evaluation is done upon both dynamic service arrival/departure and link failure generation using different performance metrics: the average restorability, the average network resource utilization, and the restoration computational time.

Next generation mobile networks base on the auto- mated, flexible, and dynamic orchestration of virtualised network services (NSs). These NSs are made up of Virtual Network Functions, which have hitherto mostly been implemented by means of virtual machines. The current trend is to include the use of containers, the so-called Cloud-native Network Functions, which may fit better the need of NS deployments embracing edge infrastructures having constrained resources. This ends up in the definition of NSs mixing both kinds of network functions (NFs) satisfying the needs of network operators and vertical industries and the characteristics of available infrastructures. We refer to the use of both kinds of NFs in a single NS as Hybrid NS. This demonstration presents the extensions done in the 5Growth management and orchestration platform to perform the deployment of such kind of NSs in a multi-site infrastructure, including the dynamic interconnection of the deployed NFs according to their nature.

Measuring several relevant metrics related to Network Function Virtualization (NFV) service lifecycle management in real time brings an enhanced monitoring of the operation of the network service orchestrator (SO) and the NFV infrastructure. In this demonstration, we integrate a complete data engineering pipeline in an operational management and orchestration stack (that of EU 5Growth project) to analyze lifecycle management metrics in real-time, in this case the network service instantiation time related metrics. In our demonstration, a data connection module instance continuously monitors the NFV SO log files and sends the log changes to the data ingestion layer, where log files are temporarily stored to be fetched by Apache Spark jobs. After utilizing Spark jobs to cleanse the log files and to obtain the service instantiation times, the metrics are sent back to the data ingestion layer to be transferred to the Elasticsearch (ELK) stack for data indexing and visualization purposes. Furthermore, the statistical information of network service instantiation (in total and its components) of studied metrics inside the network can also be profiled via a separate data analysis layer connected to the ELK stack.

Empowered bynetwork softwarization, 5G systems have become the key enabler to foster the digital transformation of the vertical industries by expanding the scope of traditional mobile networks and enriching the network service offerings. To make this a reality, we propose anautomationsolution for vertical services provisioning and hierarchical Service Level Agreement (SLA) management.Service scalingis one of the most essential operations to adapt the service deployments and resource allocations to ensure SLA fulfilment. Three different scaling levels are addressed in this work: application-, service- and resource-level. We have implemented our solution in a proof-of-concept of a virtualized mobile network platform, spanning over three geographically-distributed sites. To evaluate our solution, we leverage field tests, focusing onautomotive vertical servicescomprising a mission-critical application (collision-avoidance) and an entertainment one (video streaming). The results demonstrate the excellent performance of our solution, and its ability to automatically deploy vertical services and ensure their SLAs through different levels of service scaling.

5G networks aim to provide orchestration of services across multiple administrative domains through the concept of federation. In this paper, we are exploring the federation feature of a platform for 5G transport network of vertical services. Then we formulate the decision problem that directly impacts the revenue of 5G administrative domains, and we propose as solution a Q-learning algorithm. The simulation results show near optimum profit maximization and a well-trained Q-learning algorithm can outperform the intuitive “greedy” approach in a realistic scenario.

The composition of NFV-network services (NSs) helps 5G networks to provide the required flexibility and dynamicity to satisfy the needs of vertical industries. The mentioned capabilities are required not only at instantiation time but also during operation time to maintain the corresponding service level agreements in the presence of changing network conditions. In this demonstration, we present the capabilities of the 5Growth platform to handle the scaling of composite NSs under different situations. In particular, we show the scaling of a composite NS implying the update of the required deployed nested NS components and its interconnections but also how the scaling of a shared single NS entails updates on its associated composite NSs.

5G networks require flexibility, automation and programmability to satisfy the requirements of verticals industries. 5G-TRANSFORMER project proposes an SDN/NFV based network platform to enable this vision. Among its features, this platform allows the end-to-end deployment of parts of a network service (NS) in multiple administrative domains, which is known as network service federation (NSF). This feature increases network flexibility, opening the door to new business models. This paper complements our previous work by providing a detailed description of the 5G-TRANSFORMER NSF workflow, its interface and a profiling of the operations involved in the deployment of an NS between multiple administrative domains in a real experimental setup. Experimental results reveal i) that a federated NS can be deployed in the order of few minutes (less than 5 minutes), in line with the 5G target of reducing service setup to minutes, and ii) the impact of the NSF procedure in the deployment time is reduced when compared with the deployment of the same NS in a single administrative domain.

5G mobile networks need flexilibity, dynamicity and programmability to satisfy the needs of vertical industries. In such context, network services can be designed as integral units to be dynamically grouped among them to create tailored complex composite services or to allow the combination of services in a context of network slicing. In this demonstration, we present the service composition capabilities of the 5G-TRANSFORMER platform. In particular, we will show the instantiation of a composite network service using a previously instantiated service. When terminating the composite network service, the initial instantiated network service resumes its operation without disruption.

Future 5G networks will rely on heterogeneous deployment of micro eNodeBs, and is expected that wireless transport interfaces, deployed together with each eNodeB, will provide high-speed backhaul connectivity through the possibility to create an ad-hoc wireless mesh between the deployed micro eNodeBs. Therefore, the backhaul routing strategy will play a fundamental role in exploiting the backhaul resources. In this context, we aim to investigate the advantages and limitations of qualitatively different backhaul routing strategies as UEs download TCP and UDP content. In this paper, we compare a backpressure-based strategy that takes per-packet or per-flow basis (BP-MR) and traditional single path routing (OLSR). Ns-3 simulations show that the per-packet BP-MR variant enhances UDP performance by circumventing congestion but degrades TCP because of reordering. The per-flow BP-MR variant minimizes retransmissions by limiting the excessive reordering, and outperforms OLSR performance for both UDP backhaul traffic and TCP traffic.

The future 5G transport networks are envisioned to support a variety of vertical services through network slicing and efficient orchestration over multiple administrative domains. In this paper, we propose an orchestrator architecture to support vertical services to meet their diverse resource and service requirements. We then present a system model for resource orchestration of transport networks as well as low-complexity algorithms that aim at minimizing service deployment cost and/or service latency. Importantly, the proposed model can work with any level of abstractions exposed by the underlying network or the federated domains depending on their representation of resources.

Future dense mobile networks will imply much higher costs both in access and backhaul. This paper analyzes the effect on wireless mesh backhaul routing performance when energy saving policies are present at the radio access network (RAN). We consider an heterogeneous two-tier network where small cells (SC) with energy harvesting capabilities extend the capacity of the macro base stations (MBS), and can autonomously switch on-off in order to increase the energy efficiency of the network based on a Q-learning (QL) algorithm. Instead of calculating new routes for each SCs activation pattern, we propose to agnostically adapt to the RAN traffic demands using a non-route-based backpressure routing policy for the wireless mesh backhaul to even the network resource usage amongst SCs. We used the ns-3 simulator to integrate the different mobile network segments: RAN, wireless mesh backhaul, and evolved packet core (EPC). Simulation results show an achieved reduction of the %37% of the RAN energy consumption while satisfying traffic demands with an improvement of up to a factor of 10 of delay performance in the backhaul during peak hours.

After a disaster, it is unlikely to expect current terrestrial infrastructures to provide the coverage and the Quality of Service required by emergency operators. Currently, PPDR operators go towards a portable LTE infrastructure that provides an access network to field operators, backhauled by a wireless mesh network that can route traffic from and towards Internet through a satellite gateway. We firstly propose a new per-flow strategy for a backpressure-based routing protocol, BP-MR, and then in the aforementioned environment we analyse the original BP-MR per-packet, the proposed per-flow, and OLSR as routing protocol for the mesh network coupled with three TCP congestion control (Cubic, Hybla, Vegas) performing a typical HTTP transfer. We analysed the throughput, the latency, and the scalability over the number of the flows/operators. An extensive simulation set allows to conclude that employing BP-MR per-flow in the mesh network coupled with TCP Vegas for performing the end-to-end transfer improves the performance for PPDR professionals.

The deployment of dense networks of Small Cells (SC) is one of the key components of 5G mobile networks and will pose several challenges to the backhaul network. Software-Defined Networking (SDN) is a key technique to cope with the increased management complexity of such heterogeneous deployments, helping in the task of achieving a system-wide network management, which includes the 5G mobile wireless backhaul. Therefore, it is fundamental to devise the tools for the evaluation of 5G backhauling SDN architectures. This paper presents WiseHAUL: an SDN-empowered Wireless Small Cell Backhaul testbed. We present the design considerations of WiseHAUL, deployed in the form of an hypercube mesh that combines heterogeneous wireless technologies with the final goal of conducting SDN research and experimentation. Preliminary experimental results serve to identify the main research challenges faced by canonical SDN management techniques in an all-wireless backhaul deployment, hence acting as key drivers for further redesign and testbed development towards hybrid SDN models.

The backhaul of upcoming dense 5G Small Cell (SC) networks needs a full redesign, but there is no clear consensus on how to deploy such infrastructure. A low-cost backhaul solution is that provided by a wireless mesh network. Additionally, Software Defined Networking (SDN) is being considered as an alternative to distributed approaches to lower network infrastructure costs while enabling programmability and flexibility mostly for wired networks. This paper evolves the canonical SDN model by presenting a service-based hybrid SDN (hSDN) model that alleviates the problems caused by the unreliability of the in-band control channel formed by a wireless mesh backhaul between the SDN controller and the SCs. At the infrastructure level, we propose a wireless mesh backhaul combining sub-6GHz and millimeter wave links with long range microwave links. This architecture pursues the coexistence of network services located on top of a centralized SDN controller, with distributed network services, such as routing. To show the robustness of our proposed model, we compare a service-based hSDN model and a canonical SDN model under perfect control plane communication channel. In our service-based hSDN model, we further compare two distributed routing schemes used as fallback control plane mechanisms when the SDN controller is unreachable. Simulation results with ns-3 show improvements of up to 1.5x and 6x in terms of throughput and latency, respectively.

This paper presents Backpressure for sparse small cell deployments (BS), a transport network layer solution aimed to carry backhaul traffic under sparse topologies. BS leverages queue backlog and geographic information to take forwarding decisions on a per-packet basis, without incurring into excessive path lengths and queueing latencies. Simulations were carried out in ns-3 under diverse traffic loads. Our scheme shows similar performance to that of an idealized zero-overhead shortest path routing protocol (ISPA) under light traffic conditions, while outperforming a state of the art geographic routing scheme (i.e., GPSR). Under more severe traffic conditions, BS shows better performance than ISPA and GPSR. In particular, BS attains a reduction of up to 85% in latency regarding GPSR. Overall, this shows how a (quasi-)stateless low overhead scheme can scale to be able to serve the realistic backhaul topologies of interest in future massive small cell deployments.