Recent advancements in Hollow Core Fibers (HCF) production are paving the way toward new ground-breaking opportunities of HCF for 6G-and-beyond applications. While Standard Single-Mode Fibers (SSMF) have been the go-to solution in optical communications for the past 50 years, HCF is expected to be a turning point in how next-generation optical networks are planned and designed. Compared to SSMF, in which the optical signal is transmitted in a silica core, in HCF, the optical signal is transmitted in a hollow, i.e., air, core, significantly reducing latency (by 30%), while also decreasing attenuation (as low as 0.11 dB/km) and non-linearities. In this study, we investigate the optimal placement of HCF in latency-constrained optical networks to minimize the number of edge Data Centers (edgeDCs), while also ensuring physical-layer validation. Given the optimized placement of HCF and edgeDCs, we minimize the overall network cost in terms of transponders (TXPs) and Wavelength Selective Switches (WSSes) by optimizing the type, number, and transmission mode of TXPs, and the type and number of WSSes. We develop a Mixed Integer Nonlinear Programming (MINLP) model and a Genetic Algorithm (GA) to solve these problems. We validate the GA against the MINLP model in four synthetically generated topologies and perform extensive numerical evaluations in a realistic 25-node metro aggregation topology and a 22-node national topology. We show that by upgrading 25% of the links to HCF, we can significantly reduce the number of edgeDCs by up to 40%, while also reducing network equipment cost by up to 38%, compared to an SSMF-only network.

This demonstration showcases the autonomous creation of optical lightpaths across two geographically optical network testbeds, using TeraFlowSDN (TFS) as a high-level intent-based orchestrator for optical service provisioning. Unlike existing approaches that create lightpaths independently within a single domain, our system highlights a hierarchical control model in which a centralized TFS instance coordinates two heterogeneous domain controllers: a vendor-specific controller at Politecnico di Milano (Italy) and a local TFS instance at Sant’Anna School of Advanced Studies (Italy). Southbound adapters enable the translation of high-level service intents into device-specific configurations, making it possible to integrate different controllers and vendors without modifying the underlying infrastructure. The live demo demonstrates automated provisioning of optical lightpaths triggered via a user-friendly graphical interface. The process includes endpoint discovery, transceiver selection, and lightpath establishment, all performed autonomously across multiple domains to support a video streaming service. This work demonstrates the novelty of hierarchical cross-domain orchestration, showing how TFS can unify multivendor environments under a single platform with minimal configuration overhead. This lays the groundwork for future developments in automated service provisioning, closed-loop control, and scalable cross-domain networking.

This work proposes a pipeline for Software Defined Networking (SDN) that enables natural-language-based flow rule configuration using large language models (LLMs). The system addresses two key challenges: 1) the ambiguity and incompleteness of natural language inputs, and 2) the difficulty of reliably translating them into deployable SDN configurations. To this end, the pipeline integrates: i) an intent recognition module that refines user prompts via iterative clarification, and ii) a retrybased correction mechanism that handles failed configurations by regenerating and resubmitting corrected versions. These components are combined with intermediate YAML generation, documentation-based enrichment, and final translation into OpenFlow-compliant JSON for Ryu controllers. The pipeline is evaluated on flow rule deployment tasks of varying complexity, achieving an accuracy up to 96.7%, while maintaining costefficiency with an estimated API cost of only 0.08 per 100 configurations and remaining model model-agnostic.

Machine Learning (ML) has proven to be a promising solution to provide novel scalable and efficient fault management solutions in modern 5G-and-beyond communication networks. In the context of microwave networks, ML-based solutions have received significant attention. However, current solutions can only be applied to monolithic scenarios in which a single entity (e.g., an operator) manages the entire network. As current network architectures move towards disaggregated communication platforms in which multiple operators and vendors collaborate to achieve cost-efficient and reliable network management, new ML-based approaches for fault management must tackle the challenges of sharing business-critical information due to potential conflicts of interest. In this study, we explore the application of Federated Learning in disaggregated microwave networks for failure-cause identification using a real microwave hardware failure dataset. In particular, we investigate the application of two Vertical Federated Learning (VFL), namely using Split Neural Networks (SplitNNs) and Federated Learning based on Gradient Boosting Decision Trees (FedTree), on different multi-vendor deployment scenarios, and we compare them to a centralized scenario where data is managed by a single entity. Our experimental results show that VFL-based scenarios can achieve F1-Scores consistently within at most a 1% gap with respect to a centralized scenario, regardless of the deployment strategies or model types, while also ensuring minimal leakage of sensitive-data.

Optical metro networks interconnect access networks to core networks and must support traffic ranging from aggregation of low-rate end-user requests to high-rate inter-datacenter transfers. To effectively support traffic volumes consisting of heterogeneous flows at extremely different bit-rate, optical metro networks must jointly support coherent (100/200Gbps) and non-coherent (10Gbps) transmission technologies. When deploying these networks, network operators prioritize seeking solutions that consider both scalability and equipment cost minimization. In metro optical networks, different technologies can enable cost savings: at Optical Transport Network (OTN) layer, traffic grooming can be used to reduce equipment cost, while, at Wavelength Division Multiplexing (WDM) layer, filterless optical switching nodes , based on purely passive components, can be used to avoid expensive Wavelength Selective Switches deployment (WSS), and optimized Optical Amplifiers (OA) placement can decrease significantly required amplifiers cost. Joint deployment of these technologies can facilitate significant cost savings, but requires coordination in form of multi-layer optimization, across OTN and WDM network layers to minimize overall equipment cost (from amplifiers at WDM layer, to OTN boards at OTN layer). In this paper, we propose a novel single-step Genetic Algorithm (GA) to jointly optimize OTN-layer equipment cost (OTN boards) and WDM-layer equipment (mainly OAs) cost. We propose two sequential GA approaches, named two-step and three-step. Numerical results, obtained using real network topologies and traffic matrices provided by our industrial collaborators, show that our proposed GA-based approaches can save costs up to 58% compared to real-world baseline solutions, and that single-step approach outperforms two- and three-step cases up to 10%.

To accommodate ever-growing traffic, network operators are actively deploying high-degree reconfigurable optical add/drop multiplexers (ROADMs) to build large-capacity optical networks. High-degree ROADM-based optical networks have multiple parallel fibers between ROADM nodes, requiring the adoption of ROADM nodes with a large number of inter-/intra-node components. However, this large number of inter-/intra-node optical components in high-degree ROADM networks increases the likelihood of multiple failures simultaneously, and calls for novel methods for accurate localization of multiple failed components. To the best of our knowledge, this is the first study investigating the problem of multi-failure localization for high-degree ROADM-based optical networks. To solve this problem, we first provide a description of the failures affecting both inter-/intra-node components, and we consider different deployments of optical power monitors (OPMs) to obtain information (i.e., optical power) to be used for automated multi-failure localization. Then, as our main and original contribution, we propose a novel method based on a rules-informed neural network (RINN) for multi-failure localization, which incorporates the benefits of both rules-based reasoning and artificial neural networks (ANN). Through extensive simulations and experimental demonstrations, we show that our proposed RINN algorithm can achieve up to around 20% higher localization accuracy compared to baseline algorithms, incurring only around 4.14 ms of average inference time.

Network slicing, a key technology for 6G communications, allows diverse services to coexist on a shared physical infrastructure by allocating different resources to virtual networks (VNs, or equivalently, “network slices”) mapped over the shared infrastructure. However, it presents challenges in terms of failure survivability, as the failure of one physical element can lead to the failure of multiple VNs mapped to it, making survivability of ultra-reliable services against multiple failures a crucial research topic. In this study, we investigate the Survivable Virtual Network Mapping (SVNM) problem, focusing on double-link failures. SVNM against double-link failures can be guaranteed by enforcing appropriate SVNM constraints (e.g., any double-link failure cannot disconnect any virtual node from other virtual nodes in the same VN), but this approach requires excessive redundant capacity deployment. To address this issue, we propose a novel technique called SVNM with Inter-VN Capacity Sharing (SINC), which allows capacity sharing across different VNs to improve survivability against double-link failures with efficient spare capacity utilization. Since SINC may fail to reconnect some VNs due to insufficient spare capacity, we propose combining it with a spare slice (a VN fully dedicated to enhancing survivability) to create an advanced version, SINC+, which improves survivability by reconnecting VNs with additional spare capacity. We then formulate both SINC and SINC+ through Integer Linear Programming (ILP) models, which can provide optimal solutions. Moreover, to address the computational limitations of the ILP formulation, we developed scalable heuristic algorithms applicable to both SINC and SINC+ with a small optimality gap. Our numerical results show that VN availability using SINC improves by up to 9.48% over SVNM, with the same total link resource consumption (TLRC). Furthermore, SINC+ ensures VN survivability against all potential double-link failures, and the additional TLRC can be reduced to less than 1% in presence of a high number of VNs or large nodal connectivity, underscoring the sustainability of our proposed solutions.

Machine Learning (ML) models have found numerous applications in the automation of complex network management tasks. More recently, thanks to the introduction of new solutions for data-plane programmability (as the P4 programming language), it has become possible for programmable switches to execute ML-models directly in the data plane, with the great advantage that decisions can now be taken at packet-rate, without the involvement of the control plane. Existing works have shown that tree-based ML models, such as Random Forest, can be implemented on P4 switches, despite strict constraints on the available computational and memory resources. However, the (common, and practical) case when multiple models must be concurrently implemented to perform different tasks is still under-investigated. Assigning separate, dedicated resources (i.e., stages in the packet-processing pipeline) to each model can be very inefficient. In this study we propose a new resource-efficient data-plane implementation of multiple concurrent tree-models that share input features. We focus on the problems of DDoS-attack detection and application-traffic identification and demonstrate high accuracy in both problems while saving up to 40% of the required processing stages.

Machine Learning (ML) for failure management in optical networks has recently gained noteworthy attention. Even though real field-collected data is crucial for ML-based failure management, it is challenging to access data in emerging disaggregated optical networks, where multi-vendor equipment co-exist, and the end-to-end network management requires coordination between operators that manage different network segments. Due to data confidentiality issues, network operators tend not to share business-critical data, which sets a barrier to utilizing ML-based approaches. To overcome this issue, we propose a Vertical Federated Learning (VFL) approach based on Split-Neural-Network (SplitNN) for failure localization. We consider different deployment scenarios for ML-based solutions in a collaborative and privacy-preserving manner. Our experiments show that, depending on the VFL client and server model architectures, the proposed approaches provide very similar accuracy compared to a baseline scenario of a single operator managing the whole network (differences are mostly within $1 \%$ of accuracy), while minimizing the exposure of risk-sensitive data.

Optical metro networks currently support various traffic demands with different bit-rates, ranging from low values, e.g., 1 Gbps and 10 Gbps, to high values, e.g., 100 Gbps and 200 Gbps. These traffic demands can be served through coexistence of non-coherent transmission technology (mostly 10 Gbps) or by coherent high-rate technology (100 Gbps and above), characterized by different transmission requirements (e.g., in terms of Signal-to-Noise Ratio (SNR)). To achieve a low-cost metro architecture, various technical directions can be followed: (i) traffic grooming can be employed to decrease the number of line transmission interfaces (at the cost of increased Optical-Transport-Network (OTN) grooming boards), (ii) filterless nodes can reduce the node cost and power consumption by replacing costly Wavelength Selective Switches (WSS) with passive splitters and combiners, and (iii) amplifiers placement can be optimized, benefiting from short distances in metro areas. In this paper, we observe, for the first time to the best of our knowledge, that traffic grooming and amplifier placement are interdependent problems if we aim to achieve overall network cost minimization. Therefore, we propose and compare two cost-effective cross-layer optimization approaches that jointly consider the optical and OTN layers. Precisely, we propose two Quality-of-Transmission (QoT) aware approaches that optimize deployment cost of OTN grooming boards and interfaces in OTN layer while guaranteeing SNR and power on receiver of lightpaths as QoT metrics by considering placement of optical amplifiers along fibers in optical layer. The results indicate that our proposed approaches can save up to 40% compared to real-world baseline solutions.

We investigate classifying hardware failures in microwave networks via Machine Learning (ML). Although MLbased approaches excel in this task, they usually provide only hard failure predictions without guarantees on their reliability, i.e., on the probability of correct classification. Generally, accumulating data for longer time horizons increases the model’s predictive accuracy. Therefore, in real-world applications, a trade-off arises between two contrasting objectives: i) ensuring high reliability for each classified observation, and ii) collecting the minimal amount of data to provide a reliable prediction. To address this problem, we formulate hardware failure-cause identification as an As-Soon-As-Possible (ASAP) selective classification problem where data streams are sequentially provided to an ML classifier, which outputs a prediction as soon as the probability of correct classification exceeds a user-specified threshold. To this end, we leverage Inductive and Cross Venn-Abers Predictors to transform heuristic probability estimates from any ML model into rigorous predictive probabilities. Numerical results on a real-world dataset show that our ASAP framework reduces the time-to-predict by 8x compared to the state-of-the-art, while ensuring a selective classification accuracy greater than 95%. The dataset utilized in this study is publicly available, aiming to facilitate future investigations in failure management for microwave networks.

We consider the problem of classifying hardware failures in microwave networks given a collection of alarms using Machine Learning (ML). While ML models have been shown to work extremely well on similar tasks, an ML model is, at most, as good as its training data. In microwave networks, building a good-quality dataset is significantly harder than training a good classifier: annotating data is a costly and time-consuming procedure. We, therefore, shift the perspective from a Model-Centric approach, i.e., how to train the best ML model from a given dataset, to a Data-Centric approach, i.e., how to make the best use of the data at our disposal. To this end, we explore two orthogonal Data-Centric approaches for hardware failure identification in microwave networks. At training time, we leverage synthetic data generation with Conditional Variational Autoencoders to cope with extreme data imbalance and ensure fair performance in all failure classes. At inference time, we leverage Batch Uncertainty-based Active Learning to guide the data annotation procedure of multiple concurrent domain-expert labelers and achieve the best possible classification performance with the smallest possible training dataset. Illustrative experimental results on a real-world dataset show that our Data-Centric approaches allow for training top-performing models with ~4.5x less annotated data, while improving the classifier’s F1-Score by ~2.5% in a condition of extreme data scarcity. Finally, for the first time to the best of our knowledge, we make our dataset (curated by microwave industry experts) publicly available, aiming to foster research in data-driven failure management.

Quantum Key Distribution (QKD) is currently being explored as a solution to the threats posed to current cryptographic protocols by the evolution of quantum computers and algorithms. However, single-photon quantum signals used for QKD permit to achieve key rates strongly limited by link performance (e.g., loss and noise) and propagation distance, especially in multi-node QKD networks, making it necessary to design a scheme to efficiently and timely distribute keys to the various nodes. In this work, we introduce the new problem of joint Routing, Channel, Key-rate and Time-slot Assignment (RCKTA), which is addressed with four different network settings, i.e., allowing or not the use of optical bypass (OB) and trusted relay (TR). We first prove the NP-hardness of the RCKTA problem for all network settings and formulate it using a Mixed Integer Linear Programming (MILP) model that combines both quantum channels and quantum key pool (QKP) to provide an optimized solution in terms of number of accepted key rate requests and key storing rate. To deal with problem complexity, we also propose a heuristic algorithm based on an auxiliary graph, and show that it is able to obtain near-optimal solutions in polynomial time. Results show that allowing OB and TR achieves an acceptance ratio of 39% and 14% higher than that of OB and TR, respectively. Remarkably, these acceptance ratios are obtained with up to 46% less QKD modules (transceivers) compared to TR and only few (less than 1 per path) additional QKD modules than OB.

Quantum networks, with applications like Quantum Key Distribution (QKD), are gaining significant attention. However, their implementation faces challenges due to low entanglement generation success rates and quantum decoherence. Recent quantum technology advancements have extended entanglement memory lifetimes to one minute, termed cutoff, opening new opportunities for entanglement routing. We propose the Adaptive Entanglement Routing (AER) algorithm, which optimizes resource utilization to improve the success probability of serving entanglement and ultimately reduce the time needed for entanglement establishment. AER includes two phases: 1) determine redundant paths based on load and 2) utilize shared entanglements for entanglement swapping. Moreover, we design the highest-success-path (HSP) algorithm to maximize the success probability of entanglement routing with limited quantum memory. These innovative routing algorithms significantly reduce entanglement request failures, resulting in up to 70% reduction in average waiting times.

Practical deployments of Machine-Learning(ML)-based solutions for failure management in optical networks often suffer from limited data availability, due to, especially, scarcity of labelled data describing different failure scenarios. Transfer Learning (TL) is regarded as a promising direction in cases of data scarcity, thanks to its ability to transfer knowledge from a Source Domain (SD) (e.g. SD could be a digital twin or a laboratory testbed) to a Target Domain (TD) (e.g., the infield network). In this paper, we focus on cross-lightpath and cross-task application of TL for failure localization and failure detection in optical networks. We found that, depending on the number of retrained parameters in the ML model, cross-lightpath TL for failure localization provides satisfactory accuracy (higher than 90%, in some cases) with limited amounts of TD data, and is also convenient in terms of TD retraining duration with respect to cases where TL is not used. Moreover, we found that cross-task failure detection/localization reaches up to 12% or 25% improvement in TD accuracy when considering failure localization and detection as TD task, respectively.

The continuous traffic growth experienced in telecom networks pushes network operators to constantly investigate new solutions to deploy scalable and cost-effective network architectures, especially in the metro segment. These solutions should also ensure backward compatibility with existing network architectures. A cost-effective technical solution for today's metro networks consists in optimizing the deployment cost of hierarchical traffic-grooming boards while considering a mix of coherent (typically 100 Gbps) and non-coherent (typically 10 Gbps) transmission technologies. In this study, we consider metro regional networks composed of interconnected filterless rings, and we investigate how to minimize the joint cost of stacked Optical Transport Network (OTN) traffic-grooming boards, coherent and non-coherent transponders and interfaces, and Dispersion Compensation Modules (DCM). We propose a novel optimization approach based on Genetic Algorithms to effectively solve the associated grooming problem and compare its performance to baseline strategies, showing that we can reach up to 79% cost savings in terms of the total cost of deployed equipment.

Quantum Key Distribution (QKD) is a recent technology for secure distribution of symmetric keys, which is currently being deployed to increase communications security against quantum attacks. However, the key rate achievable over a weak quantum signal is limited by the link performance (e.g., loss and noise) and propagation distance, especially in multi-node QKD networks, making it necessary to design a scheme to efficiently and timely distribute keys to the various nodes. In this work, we formulate, using a Mixed Integer Linear Programming (MILP) model, a novel Routing, Channel, and Key-rate Assignment (RCKA) problem for QKD with Quantum Key Pool (QKP), which exploits the opportunity of using trusted relays and optical bypass. Our formulation accounts for the possibility to build a quantum key distribution path that combines both quantum channels and trusted relays to increase the acceptance ratio of key rate requests. Leveraging different versions of the proposed MILP model, we evaluate several strategies exploiting different combinations of trusted relays and optical bypass for the RCKA problem. Results show how different trade-offs between security and resource-efficiency (expressed in terms of acceptance ratio of key rate requests vs. key storing rate in QKP) can be achieved when adopting trusted-relay and/or optical-bypass technologies. Trusted relays can provide a higher acceptance ratio when the number of QKD modules (transmitters or receivers) is sufficiently large, while optical bypass, which does not require the implementation of expensive trusted relays, is preferable when the number of QKD modules is a limiting factor.

Artificial Intelligence (AI) has demonstrated superhuman capabilities in solving a significant number of tasks, leading to widespread industrial adoption. For in-field network-management application, AI-based solutions, however, have often risen skepticism among practitioners as their internal reasoning is not exposed and their decisions cannot be easily explained, preventing humans from trusting and even understanding them. To address this shortcoming, a new area in AI, called Explainable AI (XAI), is attracting the attention of both academic and industrial researchers. XAI is concerned with explaining and interpreting the internal reasoning and the outcome of AI-based models to achieve more trustable and practical deployment. In this work, we investigate the application of XAI for network management, focusing on the problem of automated failure-cause identification in microwave networks. We first introduce the concept of XAI, highlighting its advantages in the context of network management, and we discuss in detail the concept behind Shapley Additive Explanations (SHAP), the XAI framework considered in our analysis. Then, we propose a framework for a XAI-assisted ML-based automated failure-cause identification in microwave networks, spanning model’s development and deployment phases. For the development phase, we show how to exploit SHAP for feature selection and how to leverage SHAP to inspect misclassified instances during model’s development process, and how to describe model’s global behavior based on SHAP’s global explanations. For the deployment phase, we propose a framework based on predictions uncertainty to detect possibly wrong predictions that will be inspected through XAI.

Filterless Optical Networks (FONs) (i.e., optical networks where switching nodes are solely based on passive splitters and combiners) enjoy features that are highly appreciated by network operators, such as their low cost and their energy efficiency, posing them as an alternative solution to filtered Wavelength-Switched Optical Networks (WSON) based on active switching nodes. Due to FONs’ specific design criteria (the network topology must be divided into link-disjoint filterless fiber trees to avoid laser loops), traditional network problems, such as survivable virtual network mapping, shall be revisited and tackled adopting novel solutions with respect to state-of-the-art filtered WSONs. In this paper, we investigate the problem of survivable virtual network mapping (SVNM) in FONs with the aim of evaluating the cost of survivability when adopting FON technology. We first model the problem as an Integer Linear Program to establish fiber trees and provide survivable mapping of virtual networks, while minimizing cost of additional network equipment and spectrum with respect to WSON. We then propose multiple heuristic and meta-heuristic approaches to tackle large problem instances. In our numerical evaluations, we consider three scenarios: FON, WSON, and FON with pre-established fiber trees. Results show that in FON, where SVNM is jointly optimized with fiber tree establishment, the investment in additional network equipment can be largely minimized, and even avoided in some cases. In contrast, in FON with pre-established trees, amount of additional network equipment needed to guarantee survivability is significant (up to 60% with respect to WSON).

In presence of multiple failures affecting their network infrastructure, operators are faced with the Progressive Network Recovery (PNR) problem, i.e., deciding the best sequence of repairs during recovery. With incoming deployments of 5G networks, PNR must evolve to incorporate new recovery opportunities offered by network slicing. In this study, we introduce the new problem of Progressive Slice Recovery (PSR), which is addressed with eight different strategies, i.e., allowing or not to change slice embedding during the recovery, and/or by enforcing different versions of slice connectivity (i.e., network vs. content connectivity). We propose a comprehensive PSR scheme, which can be applied to all recovery strategies and achieves fast recovery of slices. We first prove the PSR’s NP-hardness and design an integer linear programming (ILP) model, which can obtain the best recovery sequence and is extensible for all the recovery strategies. Then, to address scalability issues of the ILP model, we devise an efficient two-phases progressive slice recovery (2-phase PSR) meta-heuristic algorithm, small optimality gap, consisting of two main steps: i) determination of recovery sequence, achieved through a linear-programming relaxation that works in polynomial time; and ii) slice-embedding recovery, for which we design an auxiliary-graph-based column generation to re-embed failed slice nodes/links to working substrate elements within a given number of actions. Numerical results compare the different strategies and validate that amount of recovered slices can be improved up to 50% if operators decide to reconfigure only few slice nodes and guarantee content connectivity.

Network Slicing is one of the key enabling technologies in 5G networks, as it allows the same network infrastructure to host numerous services, characterized by different Quality of Service (QoS) requirements. Network slicing provides greater flexibility when assigning resources to virtual networks (VNs, or, equivalently, “network slices”), allowing to meet very diverse service requirements. However, network slicing also brings numerous challenges in terms of management of network resources. Among these, service reliability is one of the most important, especially in light of the rising importance of ultra-reliable services in 5G. In this study, we investigate the Survivable Virtual Network Mapping (SVNM) problem focusing on double-link failures. SVNM against double-link failures can be guaranteed enforcing appropriate SVNM constraints, but this approach requires excessive redundant capacity. Capacity sharing represents a more capacity-efficient solution to ensure survivability against double-link failures. Hence, we propose a new SVNM strategy that allows capacity sharing across different virtual networks in case of double-link failure. To evaluate benefits of the proposed technique we categorize six different SVNM scenarios (with and without capacity sharing, jointly applied with SVNM or not) and formalize them through Integer Linear Programming (ILP) models. Results show that the proposed technique for SVNM with capacity sharing enables availability gains (up to about 29%) over traditional SVNM against single-link failures and significant capacity savings (up to about 50%) over SVNM against double-link failures. The advantages are more significant for increasing number of virtual networks.

Enabling Dedicated Path Protection (DPP) in Filter-less Optical Networks (FONs) poses specific design challenges, as FONs require dividing the network topology in non-overlapping fiber trees, and lightpaths cannot cross from one tree to another unless additional devices are installed. In this study, we consider the possibility to deploy three type of devices, namely I nter-Tree Transceivers (ITTs), Wavelength Blockers (WBs) and Colored Passive Filters (CPFs) to achieve DPP in FON, and we compare the three resulting DPP strategies, called P-ITT, P- WB and P- WBC. More specifically, we formulate three Integer Linear Programming (ILP) models for DPP in FON with the objective to minimize additional device cost and minimize total wavelength consumption. Numerical results over two realistic topologies show that P-WBC achieves cost savings up to 33% in comparison to P-WB and up to 97% in comparison to P-ITT. However, even if it is the costliest approach, P-ITT ensures up to 7 % savings in wavelength consumption and up to 23 % savings in resource overbuild compared to P- WB and P- WBC, making it a possible candidate in spectrum-scarce deployments.

With the risk of natural disaster occurrence rising globally, the interest in innovative disaster resilience techniques is greatly increasing. In particular, Data Center (DC) operators are investigating techniques to avoid data-loss and service downtime in case of disaster occurrence. In cloud DC networks, DCs host Virtual Machines (VM) that support cloud services. A VM can be migrated, i.e., transferred, across DCs without service disruption, using a technique known as “online VM migration”. In this article, we investigate how to schedule online VMs migrations in an alerted disaster scenario (i.e., for those disasters, such as tsunami and hurricanes, that grant an alert time to DC operators) where VMs are migrated from a risky DC, i.e., a DC at risk to be affected by a disaster, to a DC in safe locations, within a deadline set by the alert time of the incoming disaster. We propose a multi-objective Integer Linear Programming (ILP) model and heuristic algorithms for efficient online VMs migration to maximize number of VMs migrated, minimize service downtime and minimize network resource occupation. The proposed approaches perform scheduling, destination DC selection and assign route and bandwidth to VM migrations. Compared to baseline approaches, our proposed algorithms eliminate service downtime in exchange of an acceptable additional network resource occupation. Results also give insights on how to calculate the minimum amount of time required to evacuate all VMs with no service downtime. Moreover, since the proposed approaches exhibit different execution times, we design an ‘alert-aware VM evacuation’ tool to intelligently select the most suitable approach based on the number and size of VMs, alert time and available network capacity.

Automated failure-cause identification in communication networks allows operators to reduce service unavailability. Once the most likely failure root-cause is identified, appropriate countermeasures can be effectively put in place (e.g., by choosing an in-field intervention vs. a remote equipment reconfiguration). In this article, we describe a successful application of Machine Learning (ML) for automatic failure identification in microwave networks based on the real-field data. On microwave links, different heterogeneous causes (e.g., adverse atmospheric conditions, or obstacles) lead to service unavailability and produce not easily-distinguishable degradation effects on the transmission parameters. Hence, failure identification is traditionally accomplished by domain experts via direct inspection of transmission-parameter logs. As a first contribution, we identify six categories of failure causes in microwave networks and show that supervised ML enables very accurate failure identification, hence significantly simplifying failure troubleshooting. Comparing various ML algorithms, we find that up to 93% classification accuracy is obtained using real-field labeled datasets with 2513 points. One main hindrance to the application of supervised learning is that, in real network deployments, limited amount of labeled data is available for training, as manual labeling is performed by domain experts based on their knowledge and experience. On the other hand, collecting unlabeled data is relatively simple as network management systems retrieve large amounts of unlabeled information automatically. As a second contribution, we investigate an automated labeling procedure, based on autoencoders-like Artificial Neural Networks, to combine the knowledge of the few manually-labeled data with large unlabeled data. Results show that our data augmentation based on autoencoders can slightly improve failure-cause identification only when Artificial Neural Networks or Support Vector Machines are used, while accuracy slightly decreases when adopting Random Forest.

In Filterless Optical Networks (FONs), passive splitters and combiners replace the more complex and more expensive Reconfigurable Optical Add-Drop Multiplexers (ROADMs) based on Wavelength Selective Switch (WSS) in network nodes. The utilization of passive switching elements in network nodes requires to subdivide the network topology into loop-free edge-disjoint fiber trees, consequently constraining physical paths between nodes. In this work, we investigate Virtual Network Mapping (VNM) and Virtual Network Embedding (VNE) with virtual link protection in the context of FONs. To ensure virtual link protection, we propose a strategy based on the placement of additional network equipment to allow mapping virtual links over two fiber trees. We model the problem as an Integer Linear Programming (ILP) formulation with the objective of minimizing network cost in terms of additional network equipment and overall wavelength consumption. Main results show that, when solving VNE, placement of virtual nodes and link mapping can be optimized to avert excessive wavelength consumption, while VNM drains much more network's capacity, since virtual nodes' locations are pre-determined. In addition, results show that, when guaranteeing protection of virtual links in FONs, the gain of VNE is further maximized with respect to VNM. Moreover, results show that solving VNM with virtual link protection requires additional network equipment, severely penalizing network cost.

While Software Defined Networking (SDN) provides well-known advantages in terms of network automation, flexibility and resources utilization, it has been observed that SDN controllers may represent critical points of failure for the entire network infrastructure, especially when they are targeted by malicious cyber attacks such as Distributed Denial of Service (DDoS). To address this issue, in this paper we exploit stateful data planes, as enabled by P4 programming language, where switches maintain persistent memory of handled packets to perform attack detection directly at the data plane, with only marginal involvement of the SDN controllers. As machine learning (ML) is recognized as primary anomaly detection methodology, we perform DDoS attack detection using a MLbased classification and compare different ML algorithms in terms of classification accuracy and train/test duration. Moreover, we combine ML and P4-enab1ed stateful data planes to design a real-time DDoS attack detection module, which we evaluate in terms of latency required for the detection. Three real-time scenarios are considered, where P4-enab1ed switches elaborate the received packets in different ways, namely, packet mirroring, header mirroring, and P4-metadata extraction. Numerical results show significant latency reduction when P4 is adopted.

In view of incoming 5G mobile communication, network operators must upgrade their network capacity while capping capital and operational expenditures. Filterless Optical Networks (FONs) are emerging as a cost-effective technology as they eliminate costly active switching elements, the Reconfigurable Optical Add-Drop multiplexers (ROADMs) based on Wavelength Selective Switch (WSS), by replacing them with passive devices as optical power splitters/combiners. However, eliminating active switching and filtering components enforces signal broadcast on all the outputs of the passive splitters, resulting in the transmission of optical signals over unintended links and hence in higher spectrum occupation with respect to wavelength-switched optical networks (WSONs) based on active devices. To mitigate spectrum waste, FONs can be augmented by deploying programmable optical switches, which increase network flexibility as they allow re-configuration of fiber trees established in FONs to accommodate demands. This filterless network is referred to as Programmable FON (P-FON). In this paper, we propose a traffic-adaptive heuristic algorithm, namely Adapt P-FON, for the re-configuration of programmable optical switches in FONs. The algorithm performs routing and spectrum assignment for traffic demands and also provides optimized configuration of programmable optical switches such that the overall spectrum utilization in the network is minimized. We evaluate the advantages of P-FONs, in terms of spectrum utilization and equipment cost, against FONs and WSON scenarios. Results show that P-FONs have significant advantages in terms of spectrum utilization in comparison to FONs (up to 60%), and, at the same time, cost savings (up to 90%), considering cost of splitters, WSSs and programmable switches, in comparison to WSON.

Optical metro networks are currently evolving in response to the new requirements of emerging 5G services. Network Function Virtualization (NFV) is being leveraged as a platform to dynamically provision these services on top of Virtual Network Functions (VNFs), and central offices in metro areas are being upgraded to host processing units that can host the needed to provision services with stringent-latency and high-bandwidth requirements closer to users (i.e., edge computing). By concatenating these VNFs in a specific order and route traffic among them, operators generate a so-called “Service Chain“(SC). Considering the fact that, new 5G services have bandwidth requirements typically with sub-wavelength granularity, traffic grooming is required to achieve efficient network resources utilization. Since grooming affects the end-to-end latency of provisioned services, we investigate how to perform latency-aware traffic grooming, and we propose an algorithm for dynamic SC provisioning, that considers the latency requirements of each SC to decide about if grooming shall be allowed at intermediate network nodes. Our proposed algorithm tries to minimize the blocked bandwidth as well as number of nodes to host VNFs in the network (NFV-nodes) considering the nodes computational capacity, links bandwidth and end-to-end latency constraints. Results obtained from numerical evaluation show that, our algorithm is able to reduce the number of NFV-nodes up to 50%, while keeping amount of blocked bandwidth below a specific threshold.

Recently, machine learning (ML) has attracted the attention of both researchers and practitioners to address several issues in the optical networking field. This trend has been mainly driven by the huge amount of available data (i.e., signal quality indicators, network alarms, etc.) and to the large number of optimization parameters which feature current optical networks (such as, modulation format, lightpath routes, transport wavelength, etc.). In this paper, we leverage the techniques from the ML discipline to efficiently accomplish the routing and wavelength assignment (RWA) for an input traffic matrix in an optical WDM network. Numerical results show that near-optimal RWA can be obtained with our approach, while reducing computational time up to 93% in comparison to a traditional optimization approach based on integer linear programming. Moreover, to further demonstrate the effectiveness of our approach, we deployed the ML classifier into an ONOS-based software defined optical network laboratory testbed, where we evaluate the performance of the overall RWA process in terms of computational time.

Traffic demand in fixed and mobile networks is increasing rapidly, driven especially by the growing adoption of Video-on-Demand (VoD) services, which are responsible for roughly 70% of today's Internet's traffic. Network operators must continuously explore new architectural solutions to satisfy increasing traffic at minimum cost. A promising solution consists in deploying caches at the network edge such that VoD requests can be terminated locally. The dimensioning of edge network nodes in terms of storage capacity as well as their placement in the network must be optimized, to reduce costs, improve quality of service, and utilize network resources efficiently. In this paper, we aim to find the optimal deployment of caches, which minimizes overall network resource occupation for VoD service, across the various levels of a hierarchical optical metro network, in terms of the number of caches, their location and dimension (i.e., storage capacity). We develop a discrete-event simulator for dynamic VoD provisioning to measure the performance of different cache deployment strategies in terms of overall network resource occupation and blocking probability. We prove that deploying all the available storage capacity in nearest cache locations does not guarantee the minimal resource occupation. In fact, to minimize resource occupation given a fixed budget in terms of storage capacity, storage capacity must be distributed strategically among caches at different layers of the metro network based on the characteristics of the service, e.g., VoD content catalog popularity distribution.

The fifth generation (5G) of mobile communications will target unprecedented network performance and quality of service for end users. Among the various aspects which will be addressed in 5G, advanced cell coordination is deemed as crucial to maximize network throughput. In particular, in this paper, we refer to coordinated multipoint (CoMP) techniques that allow coordinating groups of cells (i.e., clusters) through a coordination controller, namely, a radio controller coordinator (RCC), to enhance the mobile network throughput by reducing interference. We focus on the placement of RCCs in the metro optical network and on its impact on the performance of cell coordination. We provide strategies to perform an optimized placement of such controllers in metro optical networks in order to maximize network throughput via cell coordination. Several CoMP techniques have been designed, whose throughput gain is affected by various factors, e.g., gain increases with the cluster size, while it decreases for larger latencies between the RCC and cells. As current metro networks are characterized by a hierarchical architecture with different levels of central offices, the choice of where to place controllers to maximize throughput gain can be optimized according to several factors, i.e., network geographical dimension, cells density, and available technology. In addition, selection of the most appropriate CoMP technique to be used in each cluster is not trivial, as the gain provided by the various techniques is differently affected by cluster size and latency between the RCC and cells. Our results show that under certain conditions optimized placement provides up to around 10% higher coordination gain with respect to fixed controller placement. Moreover, when adopting fronthaul technology, the coordination gain provided by an optimized controller placement may increase up to 20% in comparison to fixed placement.

For the incoming 5G mobile communications, enhanced cell coordination is deemed as crucial to maximize network throughput. In this paper we focus on the impact that the placement of coordination controllers in the metro optical network has on the performance of cell coordination, and we aim at verifying how throughput maximization can be achieved through optimized controller placement. Specifically, we refer to Coordinated Multipoint (CoMP), a protocol that allows to coordinate cell clusters through a Radio Controller Coordinator (RCC) to enhance the mobile-network throughput by reducing interference. Several CoMP techniques have been designed, whose throughput gain is affected differently by various factors. E.g., gains increases with the size of the cluster, while it decreases for larger latencies between RCC and cells. Current metro/aggregation networks are characterized by a hierarchical architecture with different levels of central offices, so the decision of where to place RCC in order to maximize CoMP throughput gain is not trivial and depends on several factors, such as geographical dimension of the network, cluster size and latency experienced before reaching the RCC. Our results show that under certain conditions optimized placement provides benefits with respect to fixed placement of controllers.

Telecom operators worldwide are witnessing squeezed profit margins mainly due to hyper‐competition. Hence, new business models/strategies are needed to help operators reduce Operational and Capital Expenditures. In this context, the Network Function Virtualization (NFV) paradigm, which consists of running Virtual Instances of Network Functions (NFs) in Commercial‐Off‐The‐Shelf (COTS) hardware, represents a solid alternative. Virtual Network Functions (VNFs) are then concatenated together in a sequential order to form service chains (SCs) that provide specific Internet services. In this article, we study different approaches to provision SCs with resiliency against single‐link and single‐node failures. We propose three Integer Linear Programming (ILP) models to jointly solve the problem of VNF placement and traffic routing, while guaranteeing resiliency against single‐link and/or single‐node failures. Specifically, we focus on the trade‐off between the conflicting objectives of meeting SCs latency requirements and consolidating as many as possible VNFs in NFV‐capable nodes. We show that providing resiliency against both single‐link and single‐node failures comes at twice the amount of resources in terms of NFV‐capable nodes, and that for latency‐critical services providing resiliency against single‐node failures comes at the same cost with respect to resiliency against single‐link and single‐node failures. Finally, we discuss important insights about the deployment of bandwidth‐intensive SCs. © 2017 Wiley Periodicals, Inc. NETWORKS, Vol. 70(4), 373–387 2017

We study the problem of reducing power consumption in an Internet Service Provider (ISP) network by designing the content distribution infrastructure managed by the operator. We propose an algorithm to optimally decide where to cache the content inside the ISP network. We evaluate our solution over two case studies driven by operators feedback. Results show that the energy-efficient design of the content infrastructure brings substantial savings, both in terms of energy and in terms of bandwidth required at the peering point of the operator. Moreover, we study the impact of the content characteristics and the power consumption models. Finally, we derive some insights for the design of future energy-aware networks.

The continuous traffic growth keeps challenging operators' backbone networks, which nowadays are typically implemented through Wavelength Division Multiplexing (WDM) technology. Operators are always looking for new technical solutions to efficiently exploit the bandwidth provided by WDM fiber links, while avoiding undesired increases in network cost and energy consumption. The Time Driven Switching (TDS) technique promises to effectively addresses this issue by enabling the switching of “fractions” of wavelengths thanks to the time-coordination of all network elements. By doing so, connection requests can be efficiently “packed” into WDM links without necessarily converting optical signals into the electronic domain, i.e., traffic grooming can be accomplished directly in the optical domain. In this paper we propose new dynamic routing and scheduling strategies for TDS networks and investigate the performance of a TDS network in terms of blocking probability Pb. Due to the complexity of the joint routing and scheduling, we solve the problem by a two-step approach. Three different routing strategies are evaluated and we observe that Pbis substantially reduced (up to five orders of magnitude lower) when allowing connections to be routed over different wavelengths or different physical paths. We also note that using optical buffers at most reduces Pb10 times. Moreover we study the variation of the time-frame size switched by TDS nodes, which influences Pbup to two orders of magnitude.

Today's “green thrust” has been completely steering the evolution of our society. Therefore, since enhancements on telecommunication networks are expected to cope with the growth of traffic demand, energy consumption is becoming a crucial design metric. In this paper we focus on IP over Wavelength Division Multiplexing (IPoWDM) optical network architectures, where optical circuits (or lightpaths) interconnect IP routers. Various implementations of IPoWDM networks are possible, e.g., transparent, translucent or opaque each accounting different routing and switching constraints, in order to evolve from the classic IPoWDM approach, where only signal transmission is performed optically. Although it is a common belief that “more optical” architectures can generally guarantee a much lower total power consumption, we show that identifying the most energy-efficient IPoWDM architecture is not a trivial task. In fact we demonstrate that the most energy-efficient architecture depends on some network parameters such as number of nodes, link length and average distance between nodes. This work provide an analytical framework by which we draw guidelines to (a) assess under which conditions optical switching actually becomes more desirable than electronic switching and (b) evaluate which architecture, among those employing optical switching, is more energy-efficient under different parameter settings.

In this letter we focus on the construction of optical buffers exploiting a static priority scheduling policy. Optical Switches and fiber Delay Lines (SDLs) are used to build the proposed multiple input/output ports Optical Priority Multiplexers and Priority Switching Nodes, used for contention-resolution in Optical Packet Switched networks.

Reducing the Internet power consumption will become a challenging issue, since the Internet is expected to face a high growth in terms of traffic requirements. Simply scaling the network architecture, thus increasing its energy consumption, proportionally to this growth would not be a practical solution. Various energy-efficient approaches have been considered, typically consisting in a dual-layer network architecture through the introduction of an all-optical transport network layer, as relevant power savings can be reached thanks to optical technologies. Optical switching especially allows to significantly reduce the quantity of high power-requiring optical/electronic/optical conversions and electronic processing operations. A further improvement can be obtained with Time Driven Switching (TDS), a technique which allows to switch "fractions" of wavelengths directly in the optical domain exploiting the time-coordination of all network components. In this paper we show the benefits, in terms of power saving, provided by the TDS transport architecture, by performing a comprehensive overview of the different core network architectures and comparing the power consumption obtained in the different cases. With such an optical transport technology, power savings of more than 40% are demonstrated with respect to existing architectures.