Software-Defined Networking (SDN) enables flexible and programmable control over network behavior through the deployment of multiple control applications. However, when these applications operate simultaneously, each pursuing different and potentially conflicting objectives, unexpected interactions may arise, leading to policy violations, performance degradation, or inefficient resource usage. This paper presents a Digital Twin (DT)-based framework for the early detection of such application-level conflicts. The proposed framework is lightweight, modular, and designed to be seamlessly integrated into real SDN controllers. It includes multiple DT models capturing different network aspects, including end-to-end delay, link congestion, reliability, and carbon emissions. A case study in a smart factory scenario demonstrates the framework’s ability to identify conflicts arising from coexisting applications with heterogeneous goals. The solution is validated through both simulation and proof-of-concept implementation tested in an emulated environment using Mininet. The performance evaluation shows that three out of four DT models achieve a precision above 90%, while the minimum recall across all models exceeds 84%. Moreover, the proof of concept confirms that what-if analyses can be executed in a few milliseconds, enabling timely and proactive conflict detection. These results demonstrate that the framework can accurately detect conflicts and deliver feedback fast enough to support timely network adaptation.

Next-generation networks rely on the network softwarization paradigm to enable faster and more cost-effective deployment of telecommunications services. The ETSI MANO framework plays a critical role in orchestrating these networks, yet it faces challenges such as the hidden state problem, arising from the NFVO's lack of holistic visibility into the internal state of NFVI-PoPs, which can lead to the choice of sub-optimal allocation schemes. This work introduces a novel approach to address the hidden state problem by integrating the Digital Twin (DT) paradigm into the MANO architecture. The proposed DT is a model-based solution employing neural networks to predict orchestration costs and estimate prediction errors, enabling the NFVO to make informed orchestration decisions through what-if analyses while preserving scalability and administrative independence. Performance evaluation demonstrates the DT's ability to mimic the behavior of an NFVI-PoP with high precision, i.e., in 84% of the cases, it returns a prediction that is 5% close to the actual value. Furthermore, the DT-aided NFVO achieves orchestration performance equivalent to approaches that assume full knowledge of the actual allocation costs, while overcoming in the 43% of cases traditional benchmark policies.

Next-generation applications, marked by their critical nature, need to cope with stringent Quality of Service (QoS) requirements, such as low response time and high throughput. Moreover, the increasing number of devices connected to the Internet and the need to provide a consistent network infrastructure to serve the applications requested by users, open the tradeoff of jointly considering the QoS improvement for such applications and the reduction in the energy consumption of the infrastructure. To address this challenge, this paper proposes ELTO (Energy-Load Trade-Off), a system designed for the joint optimization of energy efficiency and traffic load balancing during the transition from IP networks to Software-Defined Networks (SDN). Leveraging SDN and Network Function Virtualization (NFV) paradigms, ELTO introduces an Integer Linear Programming multi-objective formulation, and a Genetic Algorithm heuristic to tackle the optimization problem in large-scale scenarios. ELTO encompasses a holistic approach to network configuration, including network equipment status and routing, to strike a balance between network traffic load balancing and energy efficiency. Results over realistic topologies show the effectiveness of the proposed solution, outperforming other state-of-the-art approaches, being able to switch off nearly half of the links in the network while also reducing the Maximum Link Utilization.

One of the key enabling solutions to in-orbit extract information from Earth Observation images is given by deep learning techniques. However, the accuracy of these algorithms is strictly related to the availability of large datasets of satellite images for training purposes. Limitations on the available transmission bandwidth in the orbital context may prevent the possibility to downlink all acquired images to a node where centralized training happens. For this reason, we propose a communication strategy to support a completely distributed learning (DL) technique to train a deep learning model in-orbit, by leveraging the fact that satellites may form a network thanks to the potential availability of Inter-Satellite Links (ISLs) within and between orbital planes. Our proposal is different from a FL approach since we provide for each satellite to receive all the information needed to calculate an updated global model by itself, without leaning on a central parameter server. Numerical results show that DL outperforms FL in number of learning rounds completed in the unit time, allowing for reaching validation accuracy convergence in a shorter time.

One of the key enabling solutions to in-orbit extract information from Earth Observation images is given by deep learning techniques. However, the accuracy of these algorithms is strictly related to the availability of large datasets of satellite images for training purposes. Limitations on the available transmission bandwidth in the orbital context may prevent the possibility to downlink all acquired images to a node where centralized training happens. Instead, Federated Learning (FL) could be fruitfully leveraged in this scenario, since it provides for each satellite to train a local model only with its own dataset, and then to share its trained model with a central server, which receives models trained by the different satellites and aggregates them into a new global model being eventually shared with all the satellites, and this repeats until convergence is reached. However, because communication with a node acting as a central parameter server may be still limited by short visibility time, the described process may need a long time because of limited communication windows, negatively impacting the time needed to reach model convergence. For this reason, we propose a communication strategy to support a completely distributed learning technique to train a deep learning model in-orbit, by leveraging the fact that satellites may form a network thanks to the potential availability of Inter-Satellite Links (ISLs) within and between orbital planes. Our proposal is different from a FL approach since we provide for each satellite to receive all the information needed to calculate an updated global model by itself, without leaning on a central parameter server. Numerical results show that distributed learning outperforms FL in number of learning rounds completed in the unit time, allowing for reaching validation accuracy convergence in a shorter time, as it has been verified on a land coverage classification task based on the EuroSAT dataset.

The processing capacity available on satellites within Low Earth Orbit constellations allows for the implementation of data processing on-board of satellites. Furthermore, by endowing satellites with Inter-Satellite Links, it is possible to obtain a network within the constellation, enabling data routing and allowing processing to take place on any satellite of the constellation, even different from the task originating one. Edge computing solutions for Earth Observation (EO) applications have been proposed in satellite environments, where the data processing is accomplished by either the satellite running the observation task (Always First) or by a datacenter connected to a ground station (Always Ground). We propose a solution in which any satellite of the constellation may process the EO task and its choice is performed so as to minimize the sum of the memory, processing and transmission costs. We propose and evaluate the effectiveness of a heuristic that jointly performs the following operations: i) the choice of the element (satellite, datacenter) performing the task processing; ii) the choice of the routing path on which the task or its processing result is routed from the observation satellite to the datacenter. The proposed solution is compared to the Always First and Always Ground benchmark solutions and both the cost advantages and the potential delivery delay reduction are evaluated.

Nowadays, inter domain routing optimization is performed based on the so called “Tweak and Pray” approach, which consists in performing changes in the configuration of the BGP protocol without knowing in advance the consequences of such a modification. This is due to the lack of cooperation among Network Operators in the configuration of the BGP to optimize the inter domain routing. Inefficiency in the resource usage, network anomalies and outages are common consequences of wrong configuration changes performed by Network Operators in an attempt to improve the performance of their infrastructures. In this paper we propose a novel framework based on the Digital Twin technology to enable the execution of “what-if” analysis in the context of Traffic Engineering performed by tuning BGP parameters. Such a paradigm shift will allow Network Operator to be aware of the effects of BGP configuration changes before their actual execution. A proof of concept related to the balancing of inbound traffic in an Autonomous System network, based on the use of the AS Path Prepending technique, is realized to validate the feasibility of the proposed approach.

By endowing satellites in Earth Observation (EO) constellations with Orbital Edge Computing (OEC) capability, i.e, with the ability of processing acquired images directly on-board, it is surely possible to use bandwidth more effectively, since only the actual useful information extracted from the image is transferred on ground. However, OEC can be fruitfully leveraged also to minimize the energy consumption on ground stations due to image processing. In fact, in EO satellites energy is allocated in advance by endowing them with appropriate solar panels and batteries, and this amount of energy is always generated, even when it is not necessary. On the contrary, energy on ground station is closely related to its demand. For this reason, we propose and investigate a strategy to allocate processing and bandwidth resources in OEC-endowed EO satellite network to minimize energy consumption on ground stations due to on-ground processing. By taking into account the energy budget on Sentine1-2, having a 26% of available energy being unused, we show how with our approach it is possible to obtain a substantial reduction in energy consumption on ground stations even by using the 1% of the total energy budget when appropriate computational capacity is available on board, while this consumption can even drop to zero by increasing the energy dedicated to OEC operations to the 15% of the energy budget.

The next step in Earth Observation (EO) constellations will be leveraging Inter-Satellite Links (ISLs) to form a network where information generated by the EO application can be transmitted, in such a way that, by endowing spacecrafts with processing capacity, observation data may be processed directly in orbit by any satellite of the constellation. However, since bandwidth and on-board processing capacity are valuable resources, strategies to appropriately routing the information and deciding on which node it has to be processed shall be defined. In this work, we formalize and solve an optimal bandwidth and computing resource allocation problem in Low Earth Orbit (LEO) satellite constellation for EO applications. In order to deal with the complexity of the proposed optimization problem, we also present two heuristics requiring different computational effort. In the proposed problem formalization, processing can happen on any node of the network (i.e., either on the data source satellite, on any other satellite of the constellation or on ground station). After having validated the proposed heuristics by comparing their results to the optimization problem ones, we apply them to a real orbital scenario, showing their ability to reduce both total cost and data delivery delay to ground with respect to state-of-the-art solutions.

Network Black Holes (BHs) are logical failures that create a service disruption for a subset of traffic flows, generally due to device misconfiguration. Detection of a BH is a hard task due to its specific nature: the infrastructure is up and the disconnection affects a limited number of flows. An example of BH is the one caused by the failure of the Path MTU Discovery procedure in IPv6. The Segment Routing (SR) Architecture is an overlay infrastructure that provides source routing support by exploiting the connectivity service offered by the underlay IPv6 (SRv6). Thus, SR inherits the problems related to BHs affecting IPv6. In SR this problem is even more stressed due to the encapsulation mechanism that is required to enforce the segment lists on packets. Even worse, existing active probing based tools to detect network BHs for IPv6 are not suitable in SR. In this paper we investigate the problem of detecting SR Black Holes in SR domains. As first, we provide an experimental demonstration of the creation of an SR Black Holes. Then we show that existing tools based on active probing are not suitable to detect SR BHs. Then, a passive framework named Segment Routing Black Holes Detection (SR-BHD) is introduced. SR-BHD makes use of specific traffic counters available in SR capable nodes to verify the validity of the flow conservation principle on each network element. Experimental evaluation carried out through simulation and emulation shows the effectiveness of SR-BHD in detecting the presence of SR BHs.

In this paper we introduce QLR, a per-router control agent that aims at reducing the occupancy of the local buffers by performing re-routing operations. The Segment Routing architecture is exploited to manage the uncoordinated selection of re-routing performed by different nodes, thus avoiding the creation of routing loops, while the Extensible In-band Processing is used to allow the network nodes to have a detailed and updated view of the wide network status. Data and control plane programmability are considered to define a prototype implementation of QLR that allows for the execution of a preliminary performance evaluation and proof-of-concept. From the conducted experiments has emerged that QLR can effectively reduce the maximum queue occupancy and end-to-end delay up to 43% and 63%, respectively.

The increasing number of mobile devices and the enhanced user experience they require have a strong impact on mobile network development, since they result in an increased channel capacity demand to be obtained with a limited site densification. An interesting approach to face this challenge can be found in the combination of two technology enabling solutions: Cloud or Centralized RAN (C-RAN) and Advanced Antenna Systems (AAS). In this paper, we discuss the advantages given by these solutions, with a special focus on how Artificial Intelligence (AI)-based algorithms can improve their combination in terms of functional split and antenna mapping, stated as an optimization problem. In particular, AI can be beneficial in three main areas, such as the actual solution of the optimization problem, the tuning of parameters used in classical heuristic algorithms aiming at solving the optimization problem, and, finally, the traffic and resource allocation prediction at the base of proactive reconfiguration frameworks.

The Network Programming model of SRv6 allows the creation of network programs that can be enforced over traffic flows entering a Segment Routing (SR) domain. A network program is a list of instructions that must be applied on a packet traversing the SR domain. Instructions, also known as behaviors, currently available in SRv6 are divided into two main categories: i) topological (e.g., send the packet over the shortest path), and ii) service based (e.g., duplicate the packet). In this paper we introduce a new behavior for the SRv6 Network Programming model, named maximize Throughput (max_T). This function allows to steer an incoming traffic flow toward the egress node over the path that currently guarantees the highest throughput for the flow. The proposed max_T behavior has been implemented over programmable switches, and its effectiveness in improving the performance experienced by flows asking for its application is evaluated through experiments performed over an emulated environment. The preliminary result shows that a 23% reduction of the transfer time for a file over the SR domain is achieved when the max_T behavior is used.

Service Function Chaining (SFC) is an enabling technology to provide end-to-end service differentiation according to specific user requirements. Although emerging technologies such as Software-Defined Networking (SDN) and Network Function Virtualization (NFV) are perfect enablers for SFC, hardware limitation of Ternary-Content Addressable Memories (TCAMs) can be an obstacle when handling a large variability of SFC requests, derived from the increasing number of users, and the heterogeneity of applications and Quality of Service (QoS) requirements. This article introduces and investigates the problem of TCAM size limitation on the classification procedure of SFC requests in SDN-based SFC environments. To overcome this limitation, the classification of incoming SFC requests is proposed to be offloaded to transient nodes when the occupation of the ingress node flow table is close to its maximum. An Integer Linear Programming (ILP) formulation is provided to formalize the Chain Request Classification Offloading (CRCO) problem, that consists in maximizing the number of SFC requests that can be served. Furthermore, a heuristic algorithm is presented to solve the CRCO problem in feasible time. The performance evaluation carried out over two real topologies, shows that the proposed offloading strategy can greatly increase the number of accepted requests without significantly affecting the network QoS.

Network function virtualization foresees the virtualization of service functions and their execution on virtual machines. Any service is represented by a service function chain (SFC) that is a set of VNFs to be executed according to a given order. The running of VNFs needs the instantiation of VNF Instances (VNFIs) that in general are software modules executed on virtual machines. The virtualization challenges include: 1) where to instantiate VNFIs; ii) how many resources to allocate to each VNFI; iii) how to route SFC requests to the appropriate VNFIs in the right sequence; and iv) when and how to migrate VNFIs in response to changes to SFC request intensity and location. We develop an approach that uses three algorithms that are used back-to-back resulting in VNFI placement, SFC routing, and VNFI migration in response to changing workload. The objective is to first minimize the rejection of SFC bandwidth and second to consolidate VNFIs in as few servers as possible so as to reduce the energy consumed. The proposed consolidation algorithm is based on a migration policy of VNFIs that considers the revenue loss due to QoS degradation that a user suffers due to information loss occurring during the migrations. The objective is to minimize the total cost given by the energy consumption and the revenue loss due to QoS degradation. We evaluate our suite of algorithms on a test network and show performance gains that can be achieved over using other alternative naive algorithms.