Incident management in telecommunications networks generates large volumes of incident management tickets (IMTs), each containing heterogeneous and often unstructured text describing service outages, performance degradations, or security issues. Accurately categorizing these IMTs into multiple impact and cause labels is essential for rapid diagnosis and resolution. However, existing rule-based and standard language-model-based approaches struggle with noisy data, overlapping categories, and limited contextual understanding. To address these challenges, we propose two complementary solutions for automated multi-label classification of IMTs. To mitigate the effects of noisy data and overlapping categories, the first solution employs an encoder-based language model (i.e., Bidirectional Encoder Representations from Transformers (BERT)) with a relevance-guided feature selection strategy that focuses on semantically meaningful attributes. To improve contextual understanding and label consistency, the second solution leverages a decoder-based large language model (i.e., Phi-3.5) enhanced with retrieval-augmented generation (RAG) and a novel probabilistic re-ranking mechanism to refine label predictions. Experimental results show that our encoder-only model achieves an F1 score of 79.20%, while our RAG-enhanced decoder model achieves 94.98%, outperforming traditional machine learning models and BERT baselines by 23.59% and 29% on average, respectively. These findings demonstrate that combining fine-tuned language models with intelligent retrieval and re-ranking significantly improves classification accuracy in incident management systems.

As 5G and beyond mobile networks evolve, their increasing complexity necessitates advanced, automated, and datadriven fault diagnosis methods. While traditional data-driven methods falter with modern network complexities, Transformer models have proven highly effective for fault diagnosis through their efficient processing of sequential and time-series data. However, these Transformer-based methods demand substantial labeled data, which is costly to obtain. To address the lack of labeled data, we propose a novel active learning (AL) approach designed for Transformer-based fault diagnosis, tailored to the time-series nature of network data. AL reduces the need for extensive labeled datasets by iteratively selecting the most informative samples for labeling. Our AL method exploits the interpretability of Transformers, using their attention weights to create dependency graphs that represent processing patterns of data points. By formulating a one-class novelty detection problem on these graphs, we identify whether an unlabeled sample is processed differently from labeled ones in the previous training cycle and designate novel samples for expert annotation. Extensive experiments on real-world datasets show that our AL method achieves higher F1-scores than state-of-the-art AL algorithms with 50% fewer labeled samples and surpasses existing methods by up to 150% in identifying samples related to unseen fault types.

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 Special Issue contains a set of remarkable papers covering various recent research advances towards resilient Communication Networks for an hyper-connected World. Papers are organized into five categories: (i) Resilient Architectures for Next-Generation Networks, (ii) Edge, IoT, and Cyber-Physical Systems, (iii) Vehicular, Mobile, and Aerial Networks, (iv) Optical, Hybrid, and Satellite-based Resilient Communications, and (v) Security, Trust, and Resilience in Services and Applications. The editorial begins with an overview of the field and proceeds with a summary of the twenty-two papers included in this Special Issue.

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.

Quantum Key Distribution (QKD) is a foundational technology for future secure communications, and several QKD networks have been already deployed and tested around the world using optical fibers. However, these networks cannot scale in size due to the inefficiency of fiber QKD networks with increasing distances, making satellite networks a major candidate for long-distance QKD networks. In satellite QKD networks, satellites and ground stations can act as trusted relays, distributing keys between satellite-ground station pairs to serve requests among ground stations. Satellite QKD networks face fundamental challenges due the time-varying nature of the connection between ground stations and satellites, caused by both the satellite’s orbital movement and fluctuating atmospheric attenuation. Thus, it is necessary to design novel schemes to dynamically allocate resources for satellite QKD networks that adapt to evolving network conditions in different time intervals. In this work, we investigate the problem of resource allocation in satellite QKD networks taking into account the changing key generation rates, calculated according to evolving weather conditions and satellite visibility. We first model the achievable key rate of connections between satellite and ground stations under different weather conditions, which is used as an input for optimization. We formulate a Mixed-Integer Linear Programming (MILP) model to allocate resources in satellite QKD networks, which decides both link assignments (i.e., deciding which ground to connect to for satellites) and the appropriate routing path for the trusted relay. In addition, the MILP models multiple timeslots and considers keys stored in the quantum key pool (QKP), allowing keys generated during low-load periods to be used later during high-load periods. Moreover, we propose to decide the link configuration with heuristic algorithms and then utilize ILP to decide the appropriate routing path for the trusted relay, which significantly reduces the execution time. The numerical results show that incorporating link configuration within the ILP achieves up to 20% more total served keys compared to heuristicbased baseline approaches, but with an execution time up to 700x longer.

Quantum Key Distribution (QKD) can distribute keys with guaranteed security but remains susceptible to key exchange interruption due to physical-layer threats, such as high-power jamming attacks. To address this challenge, we first introduce a novel metric, namely Maximum Number of Affected Requests (maxNAR), to quantify the worst-case impact of a single physical-layer attack, and then we investigate a new problem of Routing and Wavelength Assignment with Minimal Attack Radius (RWA-MAR). We formulate the problem using an Integer Linear Programming (ILP) model and propose a scalable heuristic to efficiently minimize maxNAR. Our approach incorporates key caching through Quantum Key Pools (QKPs) to enhance resilience and optimize resource utilization. Moreover, we model the impact of different QKD network architectures, employing Optical Bypass (OB) for optical switching of quantum channels and Trusted Relay (TR) for secure key forwarding. Moreover, a tunable parameter is designed in the heuristic to guide the preference for OB or TR, offering enhanced adaptability and dynamic control in diverse network scenarios. Simulation results show our method significantly outperforms the baseline in terms of security and scalability.

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.

Machine Learning (ML) models are increasingly employed for critical network management tasks such as traffic prediction, anomaly detection, root cause analysis, and resource allocation. A major issue in the reliability of these models is data drift, which refers to the discrepancy between training data (source domain) and test/operational data (target domain) caused by changes in network conditions and configurations. Domain adaptation, which aims to develop robust models that can generalize well across different but related domains, is a promising solution to the data drift issue. However, existing domain adaptation methods often fall short in few-shot scenarios, where target domain data is limited due to high data collection costs or restricted operational network access. Furthermore, the existing methods require the network management ML models to be frequently retrained or fine-tuned over time to adapt to the changes in data distributions, leading to high operational costs. To address these limitations, we propose a novel, model-agnostic domain adaptation approach specifically designed for few-shot scenarios and network data. In our approach, network management ML models are trained exclusively on source domain data with all the input features included, while a two-step method aligns test data samples from the target domain with the source domain during inference. The first step employs our proposed causal-inference-based feature separation (FS) method, which introduces a novel perspective by treating domain shift as soft interventions (interventions that adjust the probability distribution of features rather than making absolute changes) on a set of specific features. FS effectively separates domain-variant and domain-invariant features directly in the input space, even with limited target training data. In the second step of our approach, we propose a Generative Adversarial Network (GAN)-based reconstruction method, trained exclusively on source data, to reconstruct the domain-variant features given the domain-invariant features. During inference, the GAN model maps the domain-variant features of the target domain samples to the source domain distribution, allowing the use of domain-variant features without causing cross-domain performance degradation. Since our approach trains the network management ML models exclusively on source domain data, it eliminates the need for retraining or fine-tuning these models as network conditions or data distributions evolve over time, significantly reducing costs and operational overhead. Comprehensive evaluations on two public 5G network datasets demonstrate an average 52% improvement in mitigating data drift compared to state-of-the-art methods in terms of F1-score.

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%.

Geo-distributed machine learning (GDML) can facilitate collaborative learning among geographically-dispersed data centers to meet the demands of distributed and privacy-preserving training for large-scale distributed Internet of Things applications. Unfortunately, the efficiency of distributed training tasks heavily depends on synchronized communication between multiple distributed models over bandwidth-limited wide area networks (WANs). The fine-grained Optical Transport Network (fgOTN), thanks to its adjustable bandwidth connections, represents more flexible transmission and has the ability for accurate synchronization across GDML tasks in WANs. However, flexible bandwidth assignment and complex interdependencies among tasks pose significant challenges to resource allocation for GDML in fgOTN. Specifically, flexible bandwidth assignment exacerbates resource competition among task flows, leading to decreased learning efficiency. This paper provides novel resource allocation solutions for GDML in fgOTN. We first formulate this problem as a linear programming aimed at maximizing the completion ratio of GDML tasks. Subsequently, we propose an innovative resource allocation algorithm based on genetic algorithm (GARA) for GDML in fgOTN. GARA considers both task completion and bandwidth adjustment through population generation based on prior knowledge and adaptive mutation based on completion ratio. Simulation analysis demonstrates that GARA effectively prioritizes resource allocation for high-priority tasks to alleviate resource competition, achieving the highest task completion ratio while avoiding excessive network reconfiguration.

The networking industry is offering new services leveraging recent technological advances in connectivity, storage, and computing such as mobile communications and edge computing. In this regard, extended reality, a term encompassing virtual reality, augmented reality, and mixed reality, can provide unprecedented user experience and pioneering service opportunities such as: live concerts, sports, and other events; interactive gaming and entertainment; immersive education, training, and demos. These services require high-bandwidth, low-latency, and reliable connections, and are supported by next-generation ultra-reliable and low-latency communications in the vision of 6G mobile communication systems. In this work, we devise a novel scheme, called backup from different data centers with multicast and adaptive bandwidth provisioning, to admit reliable, low-latency, and high-bandwidth extended reality live streams in next-generation networks. We consider network services where contents are non-cacheable and investigate how backup services can be offered by different data centers with multicast and adaptive bandwidth provisioning. Our proposed service-provisioning scheme provides protection not only against link failures in the physical network but also against computing and storage failures in data centers. We develop scalable algorithms for the service-provisioning scheme and evaluate their performance on various complex network instances in a dynamic environment. Numerical results show that, compared to conventional service-provisioning schemes such as those seeking backup services from the same data center, our proposed service-provisioning scheme efficiently utilizes network resources, ensures higher reliability, and guarantees low latency; hence, it is highly suitable for extended reality live streams.

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.

The integration of Network Functions Virtualization (NFV) systems into mobile edge and core networks has heightened the need for effective anomaly detection and localization methods. The complexity of NFV demands robust mechanisms for network resilience, security, and performance. Machine Learning approaches have demonstrated promising solutions in crafting adaptive and efficient mechanisms for detecting and localizing potential anomalies within NFV systems. Particularly, Unsupervised Learning (UL) methods have garnered significant attention for their potential to detect anomalies without the need for labeled data. However, UL methods are susceptible to even minor levels of anomalous samples in the training data, termed contamination, which can severely compromise their performance. This paper proposes a novel approach using the Noisy-Student technique for anomaly detection. It addresses data contamination by combining a density-estimation teacher model for pseudo-labeling with a weakly-supervised student model based on a Masked Autoencoder trained on the pseudo-labeled data. For anomaly localization, we introduce a heuristic tailored for our anomaly detection model and two Explainable Artificial Intelligence (XAI)-based approaches applicable to any detection model. Extensive experiments on three NFV datasets demonstrate superior performance, with up to a 20% improvement in anomaly detection and up to a 22% improvement in localization, in terms of F1-score.

The Edge-Cloud Continuum represents a paradigm shift in distributed computing, seamlessly integrating resources from cloud data centers to edge devices. However, orchestrating services across this heterogeneous landscape poses significant challenges, as it requires finding a delicate balance between different (and competing) objectives, including service acceptance probability, offered Quality-of-Service, and network energy consumption. To address this challenge, we propose leveraging Multi-Objective Reinforcement Learning (MORL) to approximate the full Pareto Front of service orchestration policies. In contrast to conventional solutions based on single-objective RL, a MORL approach allows a network operator to inspect all possible “optimal” trade-offs, and then decide a posteriori on the orchestration policy that best satisfies the system’s operational requirements. Specifically, we first conduct an extensive measurement study to accurately model the energy consumption of heterogeneous edge devices and servers under various workloads, alongside the resource consumption of popular cloud services. Then, we develop a set-based MORL policy for service orchestration that can adapt to arbitrary network topologies without the need for retraining. Illustrative numerical results against selected heuristics show that our MORL policy outperforms baselines by 30% on average over a broad set of objective preferences, and generalizes to network topologies up to 5x larger than training.

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.

Multi-band transmission is a promising technical direction for spectrum and capacity expansion of existing optical networks. Due to the increase in the number of usable wavelengths in multi-band optical networks, the complexity of resource allocation problems becomes a major concern. Moreover, the transmission performance, spectrum width, and cost constraint across optical bands may be heterogeneous. Assuming a worst-case transmission margin in U, L, and C-bands, this paper investigates the problem of throughput maximization in multi-band optical networks, including the optimization of route, wavelength, and band assignment. We propose a low-complexity decomposition approach based on Column Generation (CG) to address the scalability issue faced by traditional methodologies. We numerically compare the results obtained by our CG-based approach to an integer linear programming model, confirming the near-optimal network throughput. Our results also demonstrate the scalability of the CG-based approach when the number of wavelengths increases, with the computation time in the magnitude order of 10 s for cases varying from 75 to 1200 wavelength channels per link in a 14-node network. Code of this publication is available at github.com/cchen000/CG-Multi-Band.

Mobile edge computing (MEC) and millimeter-wave (mmWave) communication are promising techniques for future cellular networks. MEC enables latency-critical tasks offloading at the network edge, while mmWave provides an abundant spectrum for gigabit-per-second data transmission. Dense deployment of remote radio units (RRUs) is necessary due to high mmWave signal path loss, and hence limiting the deployment cost becomes a prime network design factor. Our work considers that RRUs are deployed to provide mmWave access and to offload computation requests to edge servers (ESs) via fronthaul links. We propose an edge network (EN) deployment problem by jointly optimizing the mmWave access and fronthaul networks. Converged fiber and in-band mmWave techniques are utilized for flexible fronthaul links deployment and cost reduction. The deployed EN is expected to fulfill coverage, reliability and latency requirements of ultra-reliable low-latency (uRLLC) services. We formulate the optimization problem as an integer linear program (ILP) and propose a multi-objective evolutionary algorithm to solve the problem. The numerical results demonstrate that our proposed algorithm can achieve close-to-optimal solutions compared with the ILP formulation. We also comparatively evaluate the deployment costs under different EN settings and show that our algorithm provides up to 20.3% cost savings compared to non-converged solutions.

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.

Deep Reinforcement Learning (DRL) is being investigated as a competitive alternative to traditional techniques for solving network optimization problems. A promising research direction lies in enhancing traditional optimization algorithms by offloading low-level decisions to a DRL agent. In this study, we consider how to effectively employ DRL to improve the performance of Local Search algorithms, i.e., algorithms that, starting from a candidate solution, explore the solution space by iteratively applying local changes (i.e., moves), yielding the best solution found in the process. We propose a Local Search algorithm based on lightweight Deep Reinforcement Learning (DeepLS) that, given a neighborhood, queries a DRL agent for choosing a move, with the goal of achieving the best objective value in the long term. Our DRL agent, based on permutation-equivariant neural networks, is composed by less than a hundred parameters, requiring only up to ten minutes of training and can evaluate problem instances of arbitrary size, generalizing to networks and traffic distributions unseen during training. We evaluate DeepLS on two illustrative NP-Hard network routing problems, namely OSPF Weight Setting and Routing and Wavelength Assignment, training on a single small network only and evaluating on instances 2x-10x larger than training. Experimental results show that DeepLS outperforms existing DRL-based approaches from literature and attains competitive results with state-of-the-art metaheuristics, with computing times up to 8x smaller than the strongest algorithmic baselines.

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.

Changes are an integral part of the day-to-day operation of large telecommunications networks as they allow to keep pace with technological advancements, meet growing network demands, ensure scalability, enhance security, improve service quality, and meet customer expectations. Changing configurations, installing devices, and migrating traffic are some examples of these changes. These changes are documented by opening tickets through a ticket management system. Automation in the ticket management system is now becoming highly desirable to manage the large number of submitted tickets. An automated ticket management system supports the management of a ticket by automating several parts of a ticket's lifecycle. In this context, ticket classification problem consists in assigning an appropriate label to a ticket to be utilized in the later stages of the ticket management cycle. In this paper, we use a collection of network-change tickets from a real network operator to solve a ticket classification problem. We observe that the network-change ticket dataset is highly skewed in the number of tickets for different possible classes. We address this challenge of classification in a highly imbalanced dataset by proposing two token-prioritization strategies along with other components. We compare three variations of our proposed approach with three methods from the literature and show that the variations of the proposed approach outperform existing methods by up to 7% in terms of F1 score.

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.

Progressive network recovery is the problem arising when a network is subject to massive failures and the network operator has to identify the optimal sequence of repair actions to maximize carried traffic. As initial deployments of quantum-key-distribution (QKD) over optical networks start appearing in several locations worldwide, in this paper, for the first time, we model and solve the Progressive QKD Network recovery (PQNR) problem in QKD networks to accelerate the QKD network recovery after massive failures. Specifically, we formulate an Integer Linear Programming (ILP) model to model the achievable key rate for different QKD network architectures (w/o trusted relay and optical bypass). Due to the scalability issue of ILP, we also propose a scalable heuristic to solve the PQNR problem for large topologies. Our numerical results show that joint utilization of optical bypass and trusted node technologies leads to significant improvement in performance and that our heuristic solution has an acceptable optimality gap compared with ILP while reducing time.

We consider the problem of forecasting the Quality-of-Transmission (QoT) of deployed lightpaths in a Wavelength Division Multiplexing (WDM) optical network. QoT forecasting plays a determinant role in network management and planning, as it allows network operators to proactively plan maintenance or detect anomalies in a lightpath. To this end, we leverage Bayesian Recurrent Neural Networks for learning uncertainty-aware probabilistic QoT forecasts, i.e., for modelling a probability distribution of the QoT over a time horizon. We evaluate our proposed approach on the open-source Microsoft Wide Area Network (WAN) optical backbone dataset. Our illustrative numerical results show that our approach not only outperforms state-of-the-art models from literature, but also predicts intervals providing near-optimal empirical coverage. As such, we demonstrate that uncertainty-aware probabilistic modelling enables the application of QoT forecasting in risk-sensitive application scenarios.

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.

We consider the problem of designing and training a neural network-based orchestrator for fog computing service deployment. Our goal is to train an orchestrator able to optimize diversified and competing QoS requirements, such as blocking probability and service delay, while potentially supporting thousands of fog nodes. To cope with said challenges, we implement our neural orchestrator as a Deep Set (DS) network operating on sets of fog nodes, and we leverage Deep Reinforcement Learning (DRL) with invalid action masking to find an optimal trade-off between competing objectives. Illustrative numerical results show that our Deep Set-based policy generalizes well to problem sizes (i.e., in terms of numbers of fog nodes) up to two orders of magnitude larger than the ones seen during the training phase, outperforming both greedy heuristics and traditional Multi-Layer Perceptron (MLP)-based DRL. In addition, inference times of our DS-based policy are up to an order of magnitude faster than an MLP, allowing for excellent scalability and near real-time online decision-making.

We make a case for in-network Continual Learning as a solution for seamless adaptation to evolving network conditions without forgetting past experiences. We propose implementing Active Learning-based selective data filtering in the data plane, allowing for data-efficient continual updates. We explore relevant challenges and propose future research directions.

Edge computing provides computing capability at close-user proximity to reduce service latency for end users. To improve the efficiency of edge computing infrastructures, geographically-distributed edge datacenters can co-work with each other and with cloud datacenters, forming a new paradigm referred to as cooperative edge-cloud computing. In this context, applications typically run on a virtual machine (VM) that can be replicated at multiple sites, and thus user traffic can be served at all the sites where corresponding VMs reside. For the performance of many applications, latency is a critical parameter. In this work, taking applications’ latencies as the primary constraint, we model the problem of “VM placement and workload assignment” as a mixed integer linear program and develop heuristic algorithms accordingly. The goal is to minimize the consumption of information technology (IT) infrastructures for placing VMs in cooperative edge-cloud computing, while meeting the heterogeneous latency demands of different applications. Some preliminary results indicate that edge datacenter's resource efficiency can be optimized by proper cross-site VM placement and workload re-direction.

5G transport networks will support dynamic services with diverse requirements through network slicing. Elastic Optical Networks (EONs) facilitate transport network slicing by flexible spectrum allocation and tuning of transmission configurations. A major challenge in supporting dynamic services is the lack of priori knowledge of future slice requests. As a consequence, slice embedding can become sub-optimal over time, leading to spectrum fragmentation and skewed utilization. This in turn can block future slice requests, impacting operator revenue. To address this issue, operators can periodically re-optimize slice embedding for reducing fragmentation. In this paper, we address this problem of re-optimizing network slice embedding on EONs for minimizing fragmentation. The problem is solved in its splittable version, which significantly increases problem complexity, but also offers more opportunities for a larger set of re-configuration actions. We employ simulated annealing for systematically exploring the large solution space. We also propose a greedy algorithm to address the practical constraint of limiting the number of re-configuration steps. Moreover, we present a novel method based on Deep Reinforcement Learning (DRL) for determining when performing re-configuration is most effective. Our extensive simulations demonstrate that the greedy algorithm yields a solution very close to that obtained using simulated annealing while requiring orders of magnitude lesser re-configuration actions. Finally, we show that by applying the greedy algorithm periodically on the network according to the DRL-based time selection algorithm, a significant improvement in the total number of accepted slice requests can be achieved with only performing a limited number of re-configuration operations.

With the adoption of edge computing, several data centers are available within the footprint of an optical metro network, and contents are replicated in multiple locations. Such a wide content replication offers a unique opportunity to provide better services to users, especially for content-based services, e.g., video delivery. Thus, a service-provisioning scheme can embrace this opportunity to optimize network resource utilization, improve reliability, and achieve lower latency. In this study, we propose a reliable service-provisioning scheme that selects the optimal subset of data centers hosting the desired content and inversely multiplexes a content request over multiple link-disjoint paths. We formulate an integer linear program and develop heuristics for the problem, and use them to solve various complex and realistic network instances. Numerical data show that, compared to conventional service-provisioning schemes such as multipath routing from a single data center or dedicated-path protection, our proposed scheme efficiently utilizes network resources, improves reliability, and reduces latency; hence, it is suitable for the above-mentioned services.

This Special Section features research contributions in the area of robust and reliable networks of the future. Modern network infrastructures must support a growing demand for intensive data processing and high-speed communication, that has led, in the last decade, to a constant evolution towards convergence of networking and computing infrastructures. This convergence was made possible by the introduction of network function virtualization and by the emergence of the Software-Defined Networking (SDN) paradigm, and has enabled new forms of cloud and edge computing to cope with the strict requirements of new services and applications, as those in the realm of the Internet of Things (IoT).

Flexible optical networks (FONs) are being adopted to accommodate the increasingly heterogeneous traffic in today’s Internet. However, in presence of high traffic load, not all offered traffic can be satisfied at all time. As carried traffic load brings revenues to operators, traffic blocking due to limited spectrum resource leads to revenue losses. In this study, given a set of traffic requests to be provisioned, we consider the problem of maximizing operator’s revenue, subject to limited spectrum resource and physical layer impairments (PLIs), namely amplified spontaneous emission noise (ASE), self-channel interference (SCI), cross-channel interference (XCI), and node crosstalk. In FONs, adaptive modulation, multiple FEC, and the tuning of power spectrum density (PSD) can be effectively employed to mitigate the impact of PLIs. Hence, in our study, we propose a universal bandwidth-related impairment evaluation model based on channel bandwidth, which allows a performance analysis for different PSD, FEC and modulations. Leveraging this PLI model and a piecewise linear fitting function, we succeed to formulate the revenue maximization problem as a mixed integer linear program. Then, to solve the problem on larger network instances, a fast two-phase heuristic algorithm is also proposed, which is shown to be near-optimal for revenue maximization. Through simulations, we demonstrate that using adaptive modulation enables to significantly increase revenues in the scenario of high signal-to-noise ratio (SNR), where the revenue can even be doubled for high traffic load, while using multiple FECs is more profitable for scenarios with low SNR.

Following the emergence of Elastic Optical Networks (EONs), Machine Learning (ML) has been intensively investigated as a promising methodology to address complex network management tasks, including, e.g., Quality of Transmission (QoT) estimation, fault management, and automatic adjustment of transmission parameters. Though several ML-based solutions for specific tasks have been proposed, how to integrate the outcome of such ML approaches inside Routing and Spectrum Assignment (RSA) models (which address the fundamental planning problem in EONs) is still an open research problem. In this study, we propose a dual-stage iterative RSA optimization framework that incorporates the QoT estimations provided by a ML regressor, used to define lightpaths’ reach constraints, into a Mixed Integer Linear Programming (MILP) formulation. The first stage minimizes the overall spectrum occupation, whereas the second stage maximizes the minimum inter-channel spacing between neighbor channels, without increasing the overall spectrum occupation obtained in the previous stage. During the second stage, additional interference constraints are generated, and these constraints are then added to the MILP at the next iteration round to exclude those lightpaths combinations that would exhibit unacceptable QoT. Our illustrative numerical results on realistic EON instances show that the proposed ML-assisted framework achieves spectrum occupation savings up to 52.4% (around 33% on average) in comparison to a traditional MILP-based RSA framework that uses conservative reach constraints based on margined analytical models.

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.

Cooperation among datacenter providers (DCPs) and network carriers is necessary to support today's ubiquitous cloud services. However, such cooperation can be constrained by limited visibility as confidential information, such as network topology, resource availability, etc., may not be disclosed among these entities due to regulatory policies. We study a DCP-carrier cooperation-based service restoration scheme during a disaster with the aid of a third-party mediator, namely a Provider Neutral Exchange (PNE). We propose a novel resource-driven demand-matching strategy to restore DCP services. When multiple DCPs compete for network resources (due to post-disaster resource crunch), resource balancing by PNE can achieve fair and efficient service restoration. To allow flexibility in demand-resource matching, DCPs generate multiple sets of connection requests and define varying priorities and bandwidth degradations for each request. Carriers evaluate the DCP requests and provide feedback (e.g., whether a request can be satisfied or not) based on their available resources. We present an eight-phase DCP-carrier cooperation framework, with each phase employing individual sub-tasks carried out by DCPs, carriers, and PNE. Results under different disaster scenarios show that our strategy significantly improves DCP service restoration, incurring less restoration time.

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.

Due to the scarcity of labeled faulty data, Unsupervised Learning (UL) methods have gained great traction for anomaly detection and localization in Network Functions Virtualization (NFV) systems. In a UL approach, training is performed on only normal data for learning normal data patterns, and deviation from the norm is considered as an anomaly. However, it has been shown that even small percentages of anomalous samples in the training data (referred to as contamination) can significantly degrade the performance of UL methods. To address this issue, we propose an anomaly-detection approach based on the Noisy-Student technique, which was originally introduced for leveraging unlabeled datasets in computer-vision classification problems. Our approach not only provides robustness against training-data contamination, but also can leverage this contamination to improve anomaly-detection accuracy. Moreover, after an anomaly is detected, localization of the anomalous virtualized network functions in an unsupervised manner is a challenging task in the absence of labeled data. For anomaly localization in NFV systems, we propose to exploit existing local AI-explainability methods to achieve a high localization performance and propose our own novel AI-explainability method, specifically designed for the anomaly-localization problem in NFV, to improve the performance further. We perform a comprehensive experimental analysis on two datasets collected on different NFV testbeds and show that our proposed solutions outperform the existing methods by up to 22% in anomaly detection and up to 19% in anomaly localization in terms of F1-score.

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.

Increased global traffic puts tough requirements not just on fiber communications links but on the entire network. This manifests itself in multiple ways, including how to optimize wavelength routing around the network, how to maximize the benefits arising from fine-control DSP with increasingly accurate real-time monitoring, and how to best deploy multiband or multiple fiber connectivity. This article will summarize research into all these areas to present a full picture of how future optical networks will play their role in supporting the continuing traffic demands of broadband, 5G, and associated applications.

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).

This Special Section (SI) features the latest research contributions regarding recent advances in the design and management of reliable communication networks. Communication networks are constantly increasing their complexity and scale to satisfy the requirements of network services. The current trend of convergence of networking and computing infrastructures (as in today’s cloud systems and softwarized networks) calls for novel advanced strategies and solutions to support reliable services, as the development of new data-driven solutions for reliable network automation and self-diagnostic tools to ensure resilient network management.

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.

5G transport networks will support dynamic services with diverse requirements through network slicing. Elastic Optical Networks (EONs) facilitate transport network slicing by flexible spectrum allocation and tuning of transmission configurations such as modulation format and forward error correction. A major challenge in supporting dynamic services is the lack of a priori knowledge of future slice requests. In consequence, slice embedding can become sub-optimal over time, leading to spectrum fragmentation and skewed utilization. This in turn can block future slice requests, impacting operator revenue. Therefore, operators need to periodically re-optimize slice embedding for reducing fragmentation. In this paper, we address this problem of re-optimizing network slice embedding on EONs for minimizing fragmentation. The problem is solved in its splittable version, which significantly increases problem complexity, but offers more opportunities for a larger set of re-configuration actions. We employ simulated annealing for systematically exploring the large solution space. We also propose a greedy algorithm to address the practical constraint to limit the number of re-configuration steps taken to reach a defragmentated state. Our extensive simulations demonstrate that the greedy algorithm yields a solution very close to that obtained using simulated annealing while requiring orders of magnitude lesser number of re-configuration actions.

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.

Network Function Virtualization (NFV) provides a major shift in the provisioning of telecommunication services by decoupling network functions from dedicated hardware devices. Such decoupling enables operational expenditure (OpEx) and capital expenditure (CapEx) reduction and allows to increase service agility. NFV relies on Virtualized Network Functions (VNFs) and, by placing VNFs on NFV-capable network nodes, and by chaining them in a specific order while guaranteeing a given end to end latency, Service Chains (SCs) are formed to provide a specific service. To achieve great flexibility in resource assignment in the network and decrease further OpEx, it is important to consider provisioning of SCs in a dynamic scenario in which traffic evolves in the network. In this study we observe that, when deploying a SC in a situation where SC requests arrive dynamically in the network, it is important to consider protection techniques to withstand failures of the network components supporting the SC. Different protection approaches can be followed to protect the SC against failures. We consider three different protection strategies, namely, Virtual-Node protection, Virtual-Link protection and End-to-End protection, which provide protection against single virtual node (hosting a VNF), single virtual link (connecting two consequent VNF of SC together) and single virtual node/virtual link failure for dynamic VNF placement. For each of them, we provide a heuristic approach for dynamic provisioning of the SC with protection. In our simulative numerical results over realistic network and SC settings, we compare the three strategies and show that End-to-End protection and Virtual-Node protection have both high blocking, however, End-to-End is able to satisfy the latency requirement of more SCs with respect to virtual node protection. Of the three protection strategies, Virtual-Link protection requires less network and computational resources and achieves lower SC latency violation.

This Special Issue contains a collection of outstanding papers covering several recent advances in optical networks for 5G communications and beyond. Papers are organized into four categories: network resource planning; optical access networks; optical fronthaul solutions; and autonomous and data-driven network management. In this introduction, a brief overview of the field is given, followed by a summary of the seventeen papers of this Special Issue, and a discussion of future directions in the field.

Network Function Virtualization (NFV) provides higher flexibility for network operators and reduces the complexity in network service deployment. Using NFV, Virtual Network Functions (VNF) can be located in various network nodes and chained together in a Service Function Chain (SFC) to provide a specific service. Consolidating multiple VNFs in a smaller number of locations would allow decreasing capital expenditures. However, excessive consolidation of VNFs might cause additional latency penalties due to processing-resource sharing, and this is undesirable, as SFCs are bounded by service-specific latency requirements. In this paper, we identify two different types of penalties (referred as “costs”) related to the processing-resource sharing among multiple VNFs: thecontext switching costsand theupscaling costs. Context switching costs arise when multiple CPU processes (e.g., supporting different VNFs) share the same CPU and thus repeated loading/saving of their context is required. Upscaling costs are incurred by VNFs requiring multi-core implementations, since they suffer a penalty due to the load-balancing needs among CPU cores. These costs affect how the chained VNFs are placed in the network to meet the performance requirement of the SFCs. We evaluate their impact while considering SFCs with different bandwidth and latency requirements in a scenario of VNF consolidation.

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.

This special section features the latest research contributions regarding the design and management of reliable networks. Reliability of communication infrastructure is a top priority for network operators. To ensure reliable network operation, new design and management techniques for reliable communications must be constantly devised to respond to the rapid network and service evolution. As a recent and relevant example, deployments of 5G communication networks will soon enter their second phase, during which the network infrastructure will require upgrades to support new Ultra-Reliable Low-Latency Communication (URLLC) services with availabilities of up to 6 nines to be guaranteed jointly with extremely low latencies. Even in the still preliminary vision of 6G communication networks, reliability is posed as one of the most critical requirements, as 6G networks will represent the communication platform of our future hyper-connected society, supporting essential services as smart mobility, e-health, and immersive environments with application in remote education and working, just to name a few. Similarly, disaster resiliency in communication networks is now attracting the attention of media, government and industry as never before (consider, e.g., the worldwide network traffic deluge to support remote working during the current Coronavirus pandemic). Luckily, several new technical directions can be leveraged to provide new solutions for network reliability as: increased network reconfigurability enabled by Software Defined Networking (SDN); integration/convergence of multiple technologies (optical, wireless satellite, datacenter networks); enhanced forms of data/service replication, supported by, e.g., edge computing; network slicing, used to carve highly-reliable logical partitions of network, computing and storage resources. These, and many others, technological transformations can be leveraged to enable next-generation high-reliability networks.

Malicious jamming launched by smart jammers can attack legitimate transmissions, which has been regarded as one of the critical security challenges in wireless communications. With this focus, this paper considers the use of an intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against a smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated while considering quality of service (QoS) requirements of legitimate users. As the jamming model and jamming behavior are dynamic and unknown, a fuzzy win or learn fast-policy hill-climbing (WoLF-CPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy, where WoLF-CPHC is capable of quickly achieving the optimal policy without the knowledge of the jamming model, and fuzzy state aggregation can represent the uncertain environment states as aggregate states. Simulation results demonstrate that the proposed anti-jamming learning-based approach can efficiently improve both the IRS-assisted system rate and transmission protection level compared with existing solutions.

Network Function Virtualization (NFV) refers to the process of running network functions in virtualized IT infrastructures as softwarized Virtual Network Functions (VNFs). Several telecom service providers are currently benefiting from this concept, as it enables a faster introduction of new network services, thereby meeting changing requirements. Following a trend initially adopted by cloud service providers, telecom service providers are also adopting de-aggregation of the VNFs into microservices (μservices). However, a μservice-based architecture that can manage a large set of diverse and sensitive network functions requires new Artificial Intelligence (AI)-based methodologies to cope with the complexity of the μservice-based NFV paradigm. This paper focuses on the use of explainable AI (XAI) for gradually migrating towards a μservices-based architecture in NFV. The paper first establishes the need for XAI to transform the NFV architecture to a μservice-based architecture and then describes some of our research objectives. Afterwards, our preliminary approach and long-term visions are provided.

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.

Malicious jamming launched by smart jammer, which attacks legitimate transmissions has been regarded as one of the critical security challenges in wireless communications. Thus, this paper exploits intelligent reflecting surface (IRS) to enhance anti-jamming communication performance and mitigate jamming interference by adjusting the surface reflecting elements at the IRS. Aiming to enhance the communication performance against smart jammer, an optimization problem for jointly optimizing power allocation at the base station (BS) and reflecting beamforming at the IRS is formulated. As the jamming model and jamming behavior are dynamic and unknown, a win or learn fast policy hill-climbing (WoLFCPHC) learning approach is proposed to jointly optimize the anti-jamming power allocation and reflecting beamforming strategy without the knowledge of the jamming model. Simulation results demonstrate that the proposed anti-jamming based-learning approach can efficiently improve both the the IRS-assisted system rate and transmission protection level compared with existing solutions.

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.

Modern network services are constantly increasing their requirements in terms of bandwidth, latency and cost efficiency. To satisfy these requirements, the concept of network slicing has been introduced in the context of next-generation 5G networks. However, to successfully provision resources to slices, a complex optimization problem must be addressed to allocate resources over a cloud network, i.e., a distributed computing infrastructure interconnected through high-capacity network links. In this study, we propose two new latency and energy-aware optimization models for provisioning 5G slices in cloud networks comprising both distributed computing and network resources. The proposed approaches differ from other existing solutions since we conduct our studies with respect to the end-to-end latency. Relevant models of latency and energy consumption are proposed based on a comprehensive review of the state-of-the-art. To effectively solve those optimization problems, a configurable heuristic is also proposed and investigated over different network topologies. Performance of the proposed heuristic is compared against near-optimal solutions. Moreover, we assess the importance of matching between resource provisioning algorithms and architectural assumptions related to 5G network slices and a proper problem modeling.

Following successful adoption of cloud computing, many service providers (SPs) are now using high-performance Virtual Machines (VMs) located in large datacenters owned by public cloud infrastructure providers to deploy their virtual network functions (VNFs). Since using these VMs has a cost depending on utilization time, a complex problem of VNF placement and scheduling (VPS) must be addressed to achieve satisfactory network performance (e.g., latency) while minimizing the cost paid to lease VMs. In this study, a cost-efficient VPS scheme (CE-VPS) is proposed to address the VPS problem in public cloud networks considering dynamic requests of ordered sequences of VNFs. Our CE-VPS scheme goes beyond existing solutions as it models some important practical aspects such as an additional latency incurred by booting a VM and installing a VNF instance. Also, CE-VPS considers that VNFs can be multi-threaded or single-threaded, and that their throughput as a function of allocated computing resources must be modeled differently. CE-VPS is formulated as a mixed inter linear program (MILP) and also as an efficient heuristic algorithm. CE-VPS achieves lower cost and latency than conventional Best-Availability and Cost-Efficient Proactive VNF Placement schemes, and a better trade-off between resource consumption and latency performance than a conventional Low-Latency scheme.

The problem of optimally deploying a virtual network onto a substrate physical network is referred to as Virtual Network Embedding (VNE). In general, this embedding is requested by a customer to an Internet Service Provider (ISP), which performs the VNE over its physical telecom network. In several situations, the physical substrate infrastructure is composed of multiple independent ISPs. In this scenario, ISPs are concerned about exposing to a third-party entity (e.g., the customer) sensitive infrastructural details that are needed to perform an effective embedding. Following a common privacy-preserving approach, known as Limited Information Disclosure (LID), the embedding may be performed by the customer based on a limited and abstracted view of the multi-domain infrastructure that ISPs accept to expose. With this approach, embedding is sub-optimal (e.g., embedding cost is not minimized) in comparison with the case where all information is available, i.e., Full Information Disclosure (FID). In this work, we propose a Reinforcement-Learning-based algorithm able to process data that the customer and ISPs cipher under the Shamir Secret Sharing (SSS) scheme. This approach guarantees total privacy to both the customer and the ISPs (e.g., details about a virtual function are only revealed to the ISP in charge of hosting it) and achieves comparable embedding cost of an existing FID heuristic, as observed from extensive simulations. The main drawback of our algorithm is the high overhead of data that ISPs and the customer need to exchange with each other to execute it. Hence, we also explore the trade-off between embedding cost and data overhead resulting from the reduction of operations done by the RL. In general, intermediate embedding costs between the FID and LID heuristics can be obtained at a significant reduction of data overhead, while not sacrificing any privacy guarantees.

Large-scale disasters affecting both network and datacenter (DC) infrastructures can cause severe disruptions in cloud-based services. During post-disaster recovery, repairs are usually carried out in stages in a progressive manner due to limited repair resource availability. The order in which network elements and DCs are repaired can significantly impact users' reachability to important contents/services. We investigate joint progressive network and DC recovery in which network recovery and DC recovery are conducted in a coordinated manner such that users have access to the maximum possible amount of contents/services at each repair stage. We first solve the optimization problem of joint progressive recovery to find the optimal sequence of network element and DC repairs with the objective to maximize cumulative weighted content reachability in the network. We then propose a scalable heuristic for scheduling the sequential repair of network nodes/links and DCs. Our model assumes that, at each repair stage, one network node with adjacent links and one DC can be fully repaired; however, full recovery may not be guaranteed due to limited resource availability. Hence, we also propose a “resource-aware” approach (with two resource-allocation strategies, namely “selective allocation” and “adaptive allocation”), which considers both full and partial recovery of elements based on available resources at each stage. We show that, compared to disjoint progressive recovery approach, in which network recovery and DC recovery plans are independent, our joint progressive recovery approach provides significantly higher per-stage content reachability in the network.

Network Function Virtualization (NFV) enables Network Operators (NOs) to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) running on commercial off-the-shelf servers are easy to deploy, update, monitor, and manage. Such virtualized services are often deployed as Service Chains (SCs), which require in-sequence placement of computing and memory resources as well as routing of traffic flows. Due to the ongoing migration towards cloudification of networks, the concept of auto-scaling which originated in Cloud Computing, is now receiving attention from networks professionals too. Prior studies on auto-scaling use measured load to dynamically react to traffic changes. Moreover, they often focus on only one of the resources (e.g., compute only, or network capacity only). In this study, we consider three different resource types: compute, memory, and network bandwidth. In prior studies, NO takes auto-scaling decisions, assuming tenants are always willing to auto-scale, and Quality of Service (QoS) requirements are homogeneous. Our study proposes a negotiation-game-based auto-scaling method where tenants and NO both engage in the auto-scaling decision, based on their willingness to participate, heterogeneous QoS requirements, and financial gain (e.g., cost savings). In addition, we propose a proactive Machine Learning (ML) based prediction method to perform SC auto-scaling in dynamic traffic scenario. Numerical examples show that our proposed SC auto-scaling methods powered by ML present a win-win situation for both NO and tenants (in terms of cost savings).

5G network slicing allows partitioning of network resources to meet stringent end-to-end service requirements across multiple network segments, from access to transport. These requirements are shaping technical evolution in each of these segments. In particular, the transport segment is currently evolving in the direction of elastic optical networks (EONs), a new generation of optical networks supporting a flexible optical-spectrum grid and novel elastic transponder capabilities. In this paper, we focus on the reliability of 5G transport-network slices in EON. Specifically, we consider the problem of slicing 5G transport networks,i.e., establishing virtual networks on 5G transport, while providing dedicated protection. As dedicated protection requires a large amount of backup resources, our proposed solution incorporates two techniques to reduce backup resources: (i) bandwidth squeezing,i.e., providing a reduced protection bandwidth than the original request; and (ii) survivable multi-path provisioning. We leverage the capability of EONs to fine tune spectrum allocation and adapt modulation format and forward error correction for allocating spectrum resources. Our numerical evaluation over realistic network topologies quantifies the spectrum savings achieved by employing EON over traditional fixed-grid optical networks, and provides new insights on the impact of bandwidth squeezing and multi-path provisioning on spectrum utilization. One key takeaway from our evaluation is that multi-path provisioning can guarantee up to 40% of the bandwidth requested by a VN during failures by provisioning only 10% additional spectrum resources. This also caused VN blocking ratio for BSR up to 40% to remain very close to that of the no-backup case.

In 5G networks, slicing allows partitioning of network resources to meet stringent end-to-end service requirements across multiple network segments, from access to transport. These requirements are shaping technical evolution in each of these segments. In particular, the transport segment is currently evolving in the direction of the so-called elastic optical networks (EONs), a new generation of optical networks supporting a flexible optical-spectrum grid and novel elastic transponder capabilities. In this paper, we focus on the reliability of 5G transport-network slices in EON. Specifically, we consider the problem of slicing 5G transport networks, i.e., establishing virtual networks on 5G transport, while providing dedicated protection. As dedicated protection requires a large amount of backup resources, our proposed solution incorporates two techniques to reduce backup resources: (i) bandwidth squeezing, i.e., providing a reduced protection bandwidth with respect to the original request; and (ii) survivable multi-path provisioning. We leverage the capability of EONs to fine tune spectrum allocation and adapt modulation format and Forward Error Correction (FEC) for allocating rightsize spectrum resources to network slices. Our numerical evaluation over realistic case-study network topologies quantifies the spectrum savings achieved by employing EON over traditional fixed-grid optical networks, and provides new insights on the impact of bandwidth squeezing and multi-path provisioning on spectrum utilization.

Next-generation optical metro-access networks are expected to support end-to-end virtual network slices for critical 5G services. However, disasters affecting physical infrastructures upon which network slices are mapped can cause significant disruption in these services. Operators can deploy recovery units or trucks to restore services based on slice requirements. In this study, we investigate the problem of slice-aware service restoration in metro-access networks with specialized recovery trucks to restore services after a disaster failure. We model the problem based on classical vehicle-routing problem to find optimal routes for recovery trucks to failure sites to provide temporary backup service until the network components are repaired. Our proposed slice-aware service-restoration approach is formulated as a mixed integer linear program with the objective to minimize penalty of service disruption across different network slices. We compare our slice-aware approach with a slice-unaware approach and show that our proposed approach can achieve significant reduction in service-disruption penalty.

Cloud-Fog Radio Access Networks (CF-RAN) were proposed as an alternative network architecture to alleviate the high fronthaul capacity requested in traditional Cloud RAN (CRAN) by moving some BaseBand Units (BBUs) from the cloud nodes to fog nodes closer to users. However, when BBU processing is moved into fog nodes, OPEX and CAPEX will increase, and the cost and energy savings introduced by CRAN will also reduce. Moreover, mobile traffic fluctuations may lead to an unbalanced resource utilization and energy- inefficient operation in fog nodes. To address this problem, processing functions in fog nodes could be activated and deactivated in function of network traffic and BBUs placed on fog nodes could be migrated to cloud nodes when network traffic is low. In this paper, we propose an Integer Linear Programming (ILP) formulation to address this dynamic resource allocation problem. By means of Network Functions Virtualization (NFV), virtualized BBUs (vBBUs) can be dynamically allocated and deallocated in fog nodes. Furthermore, considering the availability of cloud nodes and the optical fronthaul, vBBUs can be migrated from fog nodes to cloud nodes in order to balance processing loads and save energy. Compared to a baseline incremental algorithm without vBBU migration, our proposal reduces blocking probability in 89% and achieves power savings of 38%, while providing a very small rate of service interruption due to vBBUs migration.

Content Providers (CPs) typically encrypt the content sent over the telecom network to improve security and privacy of their final users, as well as to protect business-critical information (e.g., contents' popularity). Due to this encryption, Internet Service Providers (ISPs) can not easily apply caching strategies that require the inspection of traffic traversing their networks to select the most popular contents. The most common approach to solve the conflict between privacy and caching consists in allowing a CP to manage the caches (e.g., by storing and delivering the contents) directly from inside the area of the ISP. However, in this way ISPs lose the legitimate control on a portion of traffic traversing their networks. An alternative approach is enabled by recently-proposed architectural solutions that allow a CP to encrypt the contents and associate pseudonyms to them, and the ISP to count the occurrences of such identifiers to infer popularity-related information without inspecting the original contents. However, we observe that ISPs can still obtain valuable information about contents' popularity that may threaten CPs' privacy. In this paper, we formalize a strategy of association between pseudonyms and contents that effectively improves privacy but leads to a degradation of caching performance. We formally define privacy in this context and study the trade-off between caching and privacy considering differerent metrics, such as the hit-rate and the retrieval latency. The results, obtained by means of simulations over both real and synthetic data, show that privacy can be significantly improved while accepting a minor impact on the hit-rate of caching and suggest the applicability of the considered architecture in a real scenario of content delivery.

The distribution of video contents generated by Content Providers (CPs) significantly contributes to increase the congestion within the networks of Internet Service Providers (ISPs). To alleviate this problem, CPs can serve a portion of their catalogues to the end users directly from servers (i.e., the caches) located inside the ISP network. Users served from caches perceive an increased QoS (e.g., average retrieval latency is reduced) and, for this reason, caching can be considered a form of traffic prioritization. Hence, since the storage of caches is limited, its subdivision among several CPs may lead to discrimination. A static subdivision that assignes to each CP the same portion of storage is a neutral but ineffective appraoch, because it does not consider the different popularities of the CPs' contents. A more effective strategy consists in dividing the cache among the CPs proportionally to the popularity of their contents. However, CPs consider this information sensitive and are reluctant to disclose it. In this work, we propose a protocol based on Shamir Secret Sharing (SSS) scheme that allows the ISP to calculate the portion of cache storage that a CP is entitled to receive while guaranteeing network neutrality and resource efficiency, but without violating its privacy. The protocol is executed by the ISP, the CPs and a Regulator Authority (RA) that guarantees the actual enforcement of a fair subdivision of the cache storage and the preservation of privacy. We perform extensive simulations and prove that our approach leads to higher hit-rates (i.e., percentage of requests served by the cache) with respect to the static one. The advantages are particularly significant when the cache storage is limited.

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.

Elastic Optical Network (EON) virtualization has recently emerged as an enabling technology for 5G network slicing. A fundamental problem in EON slicing (known as Virtual Network Embedding (VNE)) is how to efficiently map a virtual network (VN) on a substrate EON characterized by elastic transponders and flexible grid. Since a number of 5G services will have strict latency requirements, the VNE problem in EONs must be solved while guaranteeing latency targets. In existing literature, latency has always been modeled as a constraint applied on the virtual links of the VN. In contrast, we argue in favor of an alternate modeling that constrains the latency of virtual paths. Constraining latency over virtual paths (vs. over virtual links) poses additional modeling and algorithmic challenges to the VNE problem, but allows us to capture end-to-end service requirements. In this paper, we first model latency in an EON by identifying the different factors that contribute to it. We formulate the VNE problem with latency guarantees as an Integer Linear Program (ILP) and propose a heuristic solution that can scale to large problem instances. We evaluated our proposed solutions using real network topologies and realistic transmission configurations under different scenarios and observed that, for a given VN request, latency constraints can be guaranteed by accepting a modest increase in network resource utilization. Latency constraints instead showed a higher impact on VN blocking ratio in dynamic scenarios.

Machine Learning (ML) is under intense investigation in optical networks as it promises to lead to automation of a variety of management tasks, as amplifier gain equalization, fault recognition, Quality of Transmission (QoT) estimation, and many others. Though several studies focus on each of these specific tasks, the integration of ML-based estimations inside Routing and Spectrum Assignment (RSA) is still largely unexplored.This paper moves towards such integration. We develop a framework that leverages the probabilistic outputs of a ML-based QoT estimator to define the reach constraints in an Integer Linear Programming (ILP) formulation for RSA in an elastic optical network. In this integrated procedure, the RSA problem is solved iteratively by updating the reach constraints based on the outcome of a QoT estimator, to exclude lightpaths with unacceptable QoT. In our numerical evaluation, the proposed integrated method achieves savings in spectrum occupation up to 30% (around 20% on average) compared to traditional ILP-based RSA approaches with reach constraints based on margined analytical models.

Network operators must continuously scale the capacity of their optical backbone networks to keep apace with the proliferation of bandwidth-intensive applications. Today’s optical networks are designed to carry large traffic aggregates with coarse-grained resource allocation, and are not adequate for maximizing utilization of the expensive optical substrate. Elastic Optical Network (EON) is an emerging technology that facilitates flexible allocation of fiber spectrum by leveraging finer-grained channel spacing, tunable modulation formats and Forward Error Correction (FEC) overheads, and baud-rate assignment, to right size spectrum allocation to customer needs. Virtual Network Embedding (VNE) over EON has been a recent topic of interest due to its importance for 5G network slicing. However, the problem has not yet been addressed while simultaneously considering the full flexibility offered by an EON. In this paper, we present an optimization model that solves the VNE problem over EON when lightpath configurations can be chosen among a large (and practical) set of combinations of paths, modulation formats, FEC overheads and baud rates. The VNE over EON problem is solved in its splittable version, which significantly increases problem complexity, but is much more likely to return a feasible solution. Given the intractability of the optimal solution, we propose a heuristic to solve larger problem instances. Key results from extensive simulations are: (i) a fully-flexible VNE can save up to 60% spectrum resources compared to that where no flexibility is exploited, and (ii) solutions of our heuristic fall in more than 90% of the cases, within 5% of the optimal solution, while executing several orders of magnitude faster.

Multicore fibers (MCFs) are one of the main technological enablers for space-division multiplexing. In principle, MCFs could scale the fiber capacity by a factor equal to the number of cores, but in practice such increase is hindered by transmission impairments due to the inter-core crosstalk between adjacent lit cores. The entity of such crosstalk depends on the number of cores and on their disposition within the fiber cladding, and also on the baud rate and modulation format used for transmission. As first MCF applications are expected over point-to-point systems, in this paper we concentrate on the resource allocation over a single link. Specifically, we study the Baud rate, Modulation format, Core and Spectrum Assignment problem in a multicore flexi-grid link, considering distance-adaptive reaches for different baud rates, modulation formats, and crosstalk impairments. We show that the problem is NP-hard and provide two integer linear programs, as well as heuristic approaches to solve it over large/practical traffic instances. Our problem formulations incorporate modeling of the exact inter-core crosstalk contributions depending on the number of lit neighbor cores. Numerical results are provided in a high-spatial-efficiency 19-core fiber considering different transmission impairment conditions.

Transient traffic spikes are becoming a crucial challenge for network operators from both user-experience and network-maintenance perspectives. Different from long-term traffic growth, the bursty nature of short-term traffic fluctuations makes it difficult to be provisioned effectively. Luckily, next-generation elastic optical networks (EONs) provide an economical way to deal with such short-term traffic fluctuations. In this paper, we go beyond conventional network reconfiguration approaches by proposing the novel lightpath-splitting scheme in EONs. In lightpath splitting, we introduce the concept of SplitPoints to describe how lightpath splitting is performed. Lightpaths traversing multiple nodes in the optical layer can be split into shorter ones by SplitPoints to serve more traffic demands by raising signal modulation levels of lightpaths accordingly. We formulate the problem into a mathematical optimization model and linearize it into an integer linear program (ILP). We solve the optimization model on a small network instance and design scalable heuristic algorithms based on greedy and simulated annealing approaches. Numerical results show the tradeoff between throughput gain and negative impacts like traffic interruptions. Especially, by selecting SplitPoints wisely, operators can achieve almost twice as much throughput as conventional schemes without lightpath splitting.

Content delivery involves multiple entities, such as Content Providers (CPs) and Internet Service Providers (ISPs). To better serve its users, the CP may deploy resources (e.g.,caches) as close as possible to them. In this work, we consider the deployment of Virtual Servers (VSs) to stream live videos owned by the CP in the network of the ISP. An efficient deployment requires the knowledge of both the users' position and requests. However, the CP knows what users request but not their exact position, while the ISP has knowledge of users' locations but not of their requests (due to content encryption). To guarantee users' privacy, the ISP and the CP cannot exchange these information with each other. In this paper, we make the two parties cooperate by employing a secure multiparty computation protocol which does not require the two parties to reveal the aforementioned information. This protocol allows the ISP to obtain the number of requests for a specific live-video content issued from a given area at the cost of a negligible overhead. Knowing this information, the ISP efficiently deploys the VSs with the aim of minimizing the number of hops crossed by the live videos to reach their viewers. We assess the average number of hops saved when the geographic distribution of requests is known and we conclude that it is relevant. Then, we investigate scenarios in which the privacy of both the CP and the ISP can be violated, and we propose several countermeasures. In particular, the parties can distort their data when executing the protocol, which results in a trade-off between performance and privacy. We conclude that the fulfillment of stringent privacy requirements comes at significant performance loss.

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.

Virtualization of network functions (as virtual routers, virtual firewalls, etc.) enables network owners to efficiently respond to the increasing dynamicity of network services. Virtual Network Functions (VNFs) are easy to deploy, update, monitor, and manage. The number of VNF instances, similar to generic computing resources in cloud, can be easily scaled based on load. Auto-scaling (of resources without human intervention) has been investigated in academia and industry. Prior studies on auto-scaling use measured network traffic load to dynamically react to traffic changes. In this study, we propose a proactive Machine Learning (ML) based approach to perform auto-scaling of VNFs in response to dynamic traffic changes. Our proposed ML classifier learns from past VNF scaling decisions and seasonal/spatial behavior of network traffic load to generate scaling decisions ahead of time. Compared to existing approaches for ML-based auto- scaling, our study explores how the properties (e.g., start-up time) of underlying virtualization technology impacts QoS and cost savings. We consider four different virtualization technologies: Xen and KVM, based on hypervisor virtualization, and Docker and LXC, based on container virtualization. Our results show promising accuracy of the ML classifier. We also demonstrate using realistic traffic load traces and optical backbone network that our ML method improves QoS and saves significant cost for network owners as well as leasers.

Elastic Optical Networks (EONs) represent a new approach for dealing with the enormous traffic demand in core networks as they can offer bandwidth granularities closer to those requested by the user and hence improve spectral utilization. In current literature there is a lack of dynamic strategies for service degradation which is a possible measure to address problems related to network congestion and consists in reducing the amount of resources provided. Since services of different classes can be requested, we propose in this paper an online strategy for service degradation using proportional Quality of Service (QoS). Our proposed strategy aims at minimizing the number of blocked requests due to lack of resources while provides throughput and delay guarantees for provisioned lightpaths. Thus, in order to quantify the impact of the degradation on the lightpaths we modeled source-destination pairs in an EON as a queuing system working under the Generalized Processor Sharing (GPS) service discipline with admission control of Leaky Bucket policy. The obtained results show that the proposed algorithm can reduce the blocking probability and give network operators more control between different degraded service classes.

Network function virtualization (NFV) aims to simplify service deployment using virtual network functions (VNFs). Service deployment involves the placement of VNFs and in-sequence routing of traffic flows through VNFs comprising a service chain (SC). The joint VNF placement and traffic routing is called SC mapping. In a wide-area network (WAN), where several traffic flows, generated by many distributed node pairs, require the same SC; a single instance (or occurrence) of that SC might not be enough. SC mapping with multiple SC instances for same SC is a very complex problem, since sequential traversal of VNFs has to be maintained while accounting for traffic flows in various directions. This paper is the first to deal with the problem of SC mapping with multiple SC instances to minimize network resource consumption. We propose an integer linear program (ILP), a column-generation-based ILP (CG-ILP), and a two-phase column-generation-based model (2PhMod) to solve this problem. ILP does not scale to large networks and CG-ILP scalability is limited by quadratic constraints. So, to get results over large network topologies within reasonable computational times, we propose 2PhMod. Using such an approach, we observe that an appropriate choice of only a small set of SC instances leads to a solution very close to minimum bandwidth consumption. Furthermore, this approach also helps us to analyze effects of number of VNF replicas and number of NFV nodes on bandwidth consumption when deploying these minimum number of SC instances.

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.

Physical resources are highly virtualized in todays datacenter-based cloud-computing networks. Servers, for example, are virtualized as Virtual Machines (VMs). Through abstraction of physical resources, server virtualization enables migration of VMs over the interconnecting network. VM migration can be used for load balancing, energy conservation, disaster protection, etc. Migration of a VM involves iterative memory copy and network re-configuration. Memory states are transferred in multiple phases to keep the VM alive during the migration process, with a small downtime for switchover. Significant network resources are consumed during this process. Migration also results in undesirable performance impacts. Suboptimal network bandwidth assignment, inaccurate pre-copy iterations, and high end-to-end network delay in wide-area networks (WAN) can exacerbate the performance degradation. In this study, we devise strategies to find suitable bandwidth and pre-copy iteration count to optimize different performance metrics of VM migration over a WAN. First, we formulate models to measure network resource consumption, migration duration, and migration downtime. Then, we propose a strategy to determine appropriate migration bandwidth and number of pre-copy iterations, and perform numerical experiments in multiple cloud environments with large number of migration requests. Results show that our approach consumes less network resources when compared with maximum and minimum-bandwidth provisioning strategies while using an order of magnitude less bandwidth than maximum-bandwidth strategy. It also achieves significantly lower migration duration than minimum-bandwidth scheme.

Traditionally, networks operate at a small fraction of their capacities; however, recent technologies, such as software-defined networking, may let operators run their networks harder (i.e., at higher utilization levels). Higher utilization can increase the network operator's revenue, but this gain comes at a cost: daily traffic fluctuations and failures might occasionally overload the network. We call such situations Resource Crunch. Dealing with Resource Crunch requires certain types of flexibility in the system. We focus on scenarios with flexible bandwidth requirements, e.g., some connections can tolerate reducing their bandwidth allocation. This may free capacity to provision new requests that would otherwise be blocked. For that, the network operator needs to make an informed decision, since reducing the bandwidth of a high-paying connection to allocate a low-value connection is not sensible. We propose a strategy to decide whether or not to provision a request (and which other connections to degrade) focusing on maximizing profits during Resource Crunch. To address this problem, we use an abstraction of the network state, called a connection adjacency graph (CAG). We propose an algorithm, called PROVISIONER, which integrates our CAG solution with an efficient linear program (LP). We compare our method to existing greedy approaches and to LP-only solutions, and show that our method outperforms them during Resource Crunch.

Decoupling control and data planes in a software-defined network (SDN) has its advantages along with its challenges. Especially, resilient communication between elements in the data plane (switches) and in the control plane (controllers) is key to SDN's success as disruption of this communication after a failure can severely affect data-plane functions. After a failure, simultaneous recovery of all switch-controller communication paths (control paths) may not be possible, and multiple recovery stages may be required. Since restoration of disrupted data paths depends on the recovery of disrupted control paths feeding control information to switches, the performance of control-path recovery seriously affects data-path recovery performance. The assignment of controller to switches and the routing of controller-switch control paths are what determines the control-plane recovery performance, and hence should be performed in conjunction with a recovery plan after failures. This study proposes an algorithm for recovery-aware switch-controller assignment and routing (RASCAR), which enables fast data-path recovery after a set of failures (e.g., single point of failures and disasters). We formulate the problem as an integer linear program and propose an efficient heuristic algorithm to solve large problem instances. Our illustrative numerical studies show that RASCAR significantly reduces the data-path restoration times after any failure with a minor increase in resource consumption of control paths.

Network Function Virtualization (NFV) aims to simplify deployment of network services by running Virtual Network Functions (VNFs) on commercial off-the-shelf servers. Service deployment involves placement of VNFs and in-sequence routing of traffic flows through VNFs comprising a Service Chain (SC). The joint VNF placement and traffic routing is usually referred as SC mapping. In a Wide Area Network (WAN), a situation may arise where several traffic flows, generated by many distributed node pairs, require the same SC, one single instance (or occurrence) of that SC might not be enough. SC mapping with multiple SC instances for the same SC turns out to be a very complex problem, since the sequential traversal of VNFs has to be maintained while accounting for traffic flows in various directions. Our study is the first to deal with SC mapping with multiple SC instances to minimize network resource consumption. Exact mathematical modeling of this problem results in a quadratic formulation. We propose a two-phase column-generation-based model and solution in order to get results over large network topologies within reasonable computational times. Using such an approach, we observe that an appropriate choice of only a small set of SC instances can lead to solution very close to the minimum bandwidth consumption.

In the context of future Cloud Radio Access Networks (CRAN), optical networks will play an important role to provide the required transport capacity between cell-sites and processing pools, especially for future 5G scenarios. For instance, using CPRI fronthaul technologies a single antenna element can generate data up to 24.3Gbps even with current configurations of radio transmissions, and it is expected to generate up to Tbps with the advance of technology. So, the transport segment of a 5G network needs to be accurately planned to accommodate all the generated traffic. In this work, we propose the use of a Passive Optical Network (PON) jointly with the emergent paradigms of Fog Computing and Network Function Virtualization (NFV) to energy-efficiently support the high traffic transported in emergent mobile networks in an hybrid architecture called Cloud/Fog RAN (CF-RAN) that allows local and remote baseband processing. We introduce an Integer Linear Programming (ILP) model to schedule the processing of CPRI demands among the processing nodes of the network and turn on or off processing functions on demand. Our approach is able to accommodate demands on the nodes of the network in the most energy efficient way. We compare our results with CRAN and distributed architectures (DRAN) and show that an energy efficient planning can achieve considerable gains in power consumption.

Mobile Fronthaul (MF) is defined as the connection between Remote Radio Head (RRH) and Baseband Processing Unit (BBU) in a Cloud Radio Access Network (C-RAN). Dedicated MF connections between RRH and BBU would be very costly. Thus, Time Division Multiplexing Ethernet Passive Optical Network (TDM EPON) is a promising solution to reduce cost as it can enable multiplexing gain. Note that, even though mobile users transmit intermittently, in the upstream channel RRH is sampling radio signal all the time, limiting the achievable multiplexing gain. Our study enhances the conventional TDM EPON architecture by introducing traffic classification, sifting of useless data to avoid the transmission of unnecessary EPON frames, and hence increasing the multiplexing gain in the upstream channel. We also propose a Hybrid Bandwidth Allocation (HBA) scheme to exploit the traffic usefulness classification information. Simulation results show significant improvements in terms of load and number of connected RRHs that can be supported by same EPON, while keeping the end-to-end delay under 100 μs.

Centralized Radio Access Network (C-RAN) has been recently proposed to increase network capacity, reduce energy consumption, and improve scalability. However, C-RAN requires an extensive modification to the current infrastructure, which results in a considerable deployment cost. In this paper, we conduct a techno-economic study to evaluate the migration cost of C-RAN, and we propose a methodology for cost and energy efficient C-RAN deployment. We exploit the concept of total cost of ownership, defined as the sum of capital and operational expenditures. We formulate a Digital Unit (DU) pool placement optimization problem as Mixed Integer Linear Programming (MILP), which minimizes the total cost of ownership. We compare the total cost of ownership of C-RAN to that of the existing infrastructure, under different deployment scenarios such as greenfield and brownfield deployment of fiber and DU pool, and different cell sizes. The results show that the optical infrastructure plays a determinant role in the migration cost of C-RAN. If greenfield fiber is assumed, the migration cost cannot be compensated in a reasonable amount of time. If brownfield fiber is assumed, the migration cost is considerably reduced, and a more feasible C-RAN deployment is achieved.

Today's communication networks require special redundancy to overcome severe multiple element failures. These severe failures can isolate entire sub-components of terrestrial networks - e.g., possible aftermath of natural disasters and threats such as a High-Altitude Electromagnetic Pulse (HEMP). An integrated use of all communication systems available is important to help lessen the impact over distressed areas. This work investigates the use of aerial platforms with well-defined trajectories (such as LEO satellites) to evacuate data from systems within the affected regions. We propose an algorithm capable of generating an evacuation plan for data located in terrestrial isolated systems, such as Data Centers, through the satellite network, towards final destinations in the main network. Our method works whether the satellite network is damaged or not. The evacuation plan is a node-to-node transmission schedule that maximizes the amount of evacuated data. Post-disaster scenarios are used to analyze how our method performs under different impact sizes and satellite network configurations. The results show that the proposed algorithm produces node-to-node transmission schedules that maximize evacuated data while maintaining fairness among disconnected components.

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.

As more organizations rapidly adopt cloud services, energy consumption in data centers (DCs) is increasing such that today Information and Communication Technology (ICT) has become a major consumer of energy. A large portion of ICT energy consumption is used to power servers running in DCs and the network they use to communicate. In this study, we consider that, often, energy cost at a particular DC is related to the electricity price regulated by Independent System Operators / Regional Transmission Organizations (ISOs/RTOs). As these prices vary in time and depend on the geographical locations of the DCs, recent studies have shown that the spatio-temporal variations of electricity price can be exploited to reduce electricity cost. While most prior works consider a quasi-static scenario with known workload patterns, our study proposes a dynamic workload-aware algorithm that exploits the spatio-temporal variations of electricity costs with the goal to minimize the energy cost in ICT. Our algorithm uses dynamic request rerouting and live virtual machine (VM) migration to move workloads to DCs with lower electricity cost. We consider VM migration cost (including electricity cost at optical backbone network nodes), bandwidth constraints for migration, VM consolidation, constraints from Service Level Agreement (SLA), and administrative overhead of VM migration. Our simulation studies show that the proposed algorithm reduces operational cost and improves energy efficiency of data centers significantly.

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

5G Radio Access Networks (RANs) are supposed to increase their capacity by 1000x to handle growing number of connected devices and increasing data rates. The concept of cloud-RAN (CRAN) has been recently proposed to decouple digital units (DUs) and radio units (RUs) of base stations (BSs), and centralize DUs into central offices. CRAN can ease the implementation of advanced radio coordination techniques, e.g., Coordinated Multi-Point (CoMP) Transmission/Reception, to enhance its system throughput. However, separating DUs and RUs, and implementing CoMP in CRAN require low-latency and high-bandwidth connectivity links, called "fronthaul". Today, consensus has not yet been achieved on how BSs, fronthaul, and central offices will be orchestrated to enhance the system throughput. In this study, we present a CRAN over Passive Optical Network (PON) architecture called virtualized-CRAN (V-CRAN). V-CRAN leverages the concept of virtualized PON (VPON) that can dynamically associate any RU to any DU so that several RUs can be coordinated by the same DU, and the concept of virtualized BS (V-BS) that can jointly transmit common signals from multiple RUs to a user. We propose a novel mathematical model based on constraint programming for joint allocation of radio, optical network, and baseband processing resources to enhance RAN throughput, and we solve it by optimally forming VPONs and V-BSs. Comprehensive simulations show that V-CRAN can enhance the system throughput and the efficiency of resource utilization.

In emerging cellular systems, optical fronthaul is expected to play a major role to support many control operations, e.g., Coordinated Multipoint (CoMP). CoMP is a promising technique for interference mitigation as it can transform interfing signals into joint transmission (reception) in which signals from adjacent cell sites are simultaneously transmitted (received) to (from) mobile terminals. But the exchange of information required by CoMP demands high flexibility and capacity. This paper proposes a new architecture for supporting CoMP operations in emerging cellular systems. It is based on a time-and-wavelength-division-multiplexed passive optical network (TWDM-PON) fronthaul, using virtualized base stations and a cloud radio access network (C-RAN) architecture. We also propose techniques to distribute the load on controllers to minimize the coordination delay. Results show that, for a typical setting, our methods can save up to 37% on the time required to distribute channel state information among multiple base stations.

In network virtualization, when a disaster hits a physical network infrastructure, it is likely to break multiple virtual network connections. So, after a disaster occurs, the network operator has to schedule multiple teams of repairmen to fix the failed components, by considering that these elements may be geographically dispersed. An effective schedule is very important as different schedules may result in very different amounts of time needed to restore a failure. In this study, we introduce the multiple traveling repairmen problem (MTRP) for post-disaster resilience, i.e., to reduce the impact of a disaster. Re-provisioning of failed virtual links is also considered. We first formally state the problem, where our objective is to find an optimal schedule for multiple teams of repairmen to restore the failed components in physical network, maximizing the traffic in restored virtual network and with minimum damage cost. Then, we propose a greedy (GR) and a simulated annealing (SA) algorithm, and we measure the damage caused by a disaster in terms of disconnected virtual networks (DVN), failed virtual links (FVL), and failed physical links (FPL). Numerical result shows that both proposed algorithms can make good schedules for multiple repairmen teams, and SA leads to significantly lower damage in terms of DVN, FVL, and FPL than GR.

With the rapid growth of content-based network services, there is increasing interest in reducing the emissions associated with brown-energy consumption in Content Delivery Networks (CDNs). At the same time, content needs to be placed in safe Data Center (DC) locations, which are unlikely to be hit by disasters. Further risk reduction is achieved using content replication which provides inter-DC content redundancy. Unfortunately, there is contention between the objectives of brown-energy minimization and risk reduction since replicating content increases brown-energy consumption. To address these contradictory issues, we leverage the concept of content fragmentation used inside DCs and propose an inter-DC Content Fragmentation (CF) scheme which aims to achieve brown-energy saving by reducing storage overhead while maintaining low risk compared to basic replication schemes. We also propose a Green and Low-Risk Content Placement approach (GR-CP) to address the tradeoff between brown-energy consumption and disaster risk. Both CF and replication schemes are implemented in GR-CP and evaluated over a range of content popularity and redundancy levels. Our results show that CF outperforms replication scheme except when content popularity is high and the risk constraint is stringent. When popularity and risk are both low, CF can save more brown energy by using low-redundancy fragmentation techniques.

In recent years, the increasing traffic demand in radio access networks (RANs) has led to considerable growth in the number of base stations (BSs), posing a serious scalability issue, including the energy consumption of BSs. Optical-access-enabled Cloud-RAN (CRAN) has been recently proposed as a next-generation access network. In CRAN, the digital unit (DU) of a conventional cell site is separated from the radio unit (RU) and moved to the “cloud” (DU cloud) for centralized signal processing and management. Each DU/RU pair exchanges bandwidth-intensive digitized baseband signals through an optical access network (fronthaul). Time-wavelength division multiplexing (TWDM) passive optical network (PON) is a promising fronthaul solution due to its low energy consumption and high capacity. In this paper, we propose and leverage the concept of a virtual base station (VBS), which is dynamically formed for each cell by assigning virtualized network resources, i.e., a virtualized fronthaul link connecting the DU and RU, and virtualized functional entities performing baseband processing in DU cloud. We formulate and solve the VBS formation (VF) optimization problem using an integer linear program (ILP). We propose novel energy-saving schemes exploiting VF for both the network planning stage and traffic engineering stage. Extensive simulations show that CRAN with our proposed VF schemes achieves significant energy savings compared to traditional RAN and CRAN without VF.

Cloud computing uses cloud networks (CNs) that integrate and virtualize computing servers and communication networks. In a CN, virtual machines (VMs) are interconnected through virtual networks (VNs) provisioned over a physical optical network. A disaster event is a serious threat to cloud computing infrastructure, not only for CN disconnections caused by multiple infrastructure failures, but by subsequent and unpredictable CN disconnections induced by cascading failures. Studies on disaster protection for CNs suggest large pre-provisioning of additional capacity before a possible disaster, with limited protection for later cascading failures. In this work, we propose an adaptive and cascading- failure-aware CN disaster recovery scheme that (re-)acts after the disaster, and uses risk modeling to reduce the capacity required for the recovery and minimize the post-disaster disconnection of CNs. Major power grid outages could cause cascading failures on cloud infrastructure operation. Thus, in this study, propagation patterns of power grid failures are used to estimate the location of cascading failures. Simulation results based on human-made disasters, e.g., weapon of mass destruction (WMD) attacks, show that our approach can lead to significant reduction in the risk of CN disconnections due to cascading failures, while reducing up to 50% of the capacity re-provisioning required for the recovery.

Communication networks, such as core optical networks, heavily depend on their physical infrastructure, and hence they are vulnerable to man-made disasters, such as Electromagnetic Pulse (EMP) or Weapons of Mass Destruction (WMD) attacks, as well as to natural disasters. Large-scale disasters may cause huge data loss and connectivity disruption in these networks. As our dependence on network services increases, the need for novel survivability methods to mitigate the effects of disasters on communication networks becomes a major concern. Software-Defined Networking (SDN), by centralizing control logic and separating it from physical equipment, facilitates network programmability and opens up new ways to design disaster-resilient networks. On the other hand, to fully exploit the potential of SDN, along with data-plane survivability, we also need to design the control plane to be resilient enough to survive network failures caused by disasters. Several distributed SDN controller architectures have been proposed to mitigate the risks of overload and failure, but they are optimized for limited faults without addressing the extent of large-scale disaster failures. For disaster resiliency of the control plane, we propose to design it as a virtual network, which can be solved using Virtual Network Mapping techniques. We select appropriate mapping of the controllers over the physical network such that the connectivity among the controllers (controller-to-controller) and between the switches to the controllers (switch-to-controllers) is not compromised by physical infrastructure failures caused by disasters. We formally model this disaster-aware control-plane design and mapping problem, and demonstrate a significant reduction in the disruption of controller-to-controller and switch-to-controller communication channels using our approach.

In recent years, the increasing traffic demand in radio access networks (RAN) has led to considerable growth of the number of base stations (BS), posing a serious scalability issue with respect to the energy consumption of BSs. Optical-access-enabled Cloud RAN (CRAN) has been recently proposed as a next-generation access network, where the digital unit (DU) of a conventional cell site is separated from the radio unit (RU), by an optical access network (fronthaul), and moved to the “cloud” (DU pool) for centralized signal processing and management. Time-Wavelength Division Multiplexing (TWDM) Passive Optical Network (PON) is a promising fronthaul solution due to its low energy consumption and high capacity. In this study, we propose the concept of Virtual Base Station (VBS), which is dynamically formed for each cell by assigning virtualized network resources, including i) a virtualized PON link connecting the DU and RU and ii) virtualized functional entities performing baseband processing in DU pool. We propose a novel energy-saving scheme exploiting VBS formation for CRAN and compare its performance with the optimal results of an Integer Linear Program for VBS formation optimization problem. Numerical evaluation shows that CRAN with VBS formation achieves significant energy savings compared to traditional RAN and CRAN without VBS formation.

Connections provisioned in a backbone network are usually protected. A “good” protection scheme can decrease the downtime experienced by a connection, which can reduce (or eliminate) penalties for the violation of the Service Level Agreement (SLA) between the network operator and its customer. Although “good” protection schemes can guarantee high availability to connections, they usually require high capacity (e.g., bandwidth). However, backbone networks usually have some excess capacity (EC) to accommodate traffic fluctuations and growth, and when there is enough EC, the high capacity requirement of protection schemes can be tolerated. However, under traffic growth, the network operator has to add more bandwidth to avoid capacity exhaustion, which increases upgrade costs. In this study, we show that, in case of connections supporting differentiated services, where connections' tolerable downtimes are diverse, efficient exploitation of EC can decrease both SLA violations and upgrade costs. We develop a novel EC management (ECM) approach that provides high-availability high-capacity protection schemes when EC is available, and reprovisions backup resources with multiple protection schemes so that SLAs are still respected, but network upgrade costs are kept under control. We formulate this problem as an integer linear program (ILP) and develop an efficient heuristic as the ILP is intractable for large problems. We present several alternatives of our ECM approach to show its compatibility with different protection-scheme combinations. Numerical examples are presented to illustrate how the proposed ECM technique finds a tradeoff between upgrade costs and penalties paid for SLA violations while reducing the total cost significantly.

Internet traffic is increasingly becoming a mediastreaming traffic. Especially, Video-on-Demand (VoD) services are pushing the demand for broadband connectivity to the Internet, and optical fiber technology is being deployed in the access network to keep up with such increasing demand. To provide a more scalable network architecture for video/content delivery, network operators are currently considering novel integrated metro/access networks which accommodate replicated video servers directly in their infrastructure. In such way, servers for VoD delivery are placed nearer to the end users, the core segment of the network is partially traffic offloaded, and the end users experience better performance in terms of QoS. In our work, we will evaluate the performance improvement of an integrated metro/access architecture for VoD delivery with replicated video servers considering different configurations in terms of number of replicated servers, meshing degree and adopted network technologies. We develop a network simulator in which replicas of video servers (called Metro Servers, or MSs) are deployed to meet the demand of VoD traffic. In the result section we compare the performance of the various configurations and discuss which are the minimum requirements to minimize blocking of the VoD requests.

Energy efficiency is expected to be a key design parameter for next-generation access/aggregation networks. Using a single network infrastructure to aggregate/backhaul both mobile and fixed network traffic, typically referred to as fixed/mobile convergence (FMC), seems a promising strategy to pursue energy efficiency. WDM networks are a prominent candidate to support next-generation FMC network architectures, as they provide huge capacity at relatively low costs and energy consumption. We consider a FMC WDM aggregation network in which the novel concept of “hotelling” of mobile baseband units (BBUs) is employed. The so-called BBU hotelling consists in separating BBUs from their cell sites and consolidating them in single locations, called hotels. As a result, the aggregation network will transport both IP (fixed and mobile) traffic and CPRI (fronthaul) traffic, the latter exchanged between each BBU and its cell site. In this paper, we propose an energy-efficient WDM aggregation network, and we formally define the BBU placement optimization problem, whose objective is to minimize the defined aggregation infrastructure power (AIP). We consider three different network architectures: Bypass, Opaque, and No-Hotel, which feature different placement of BBUs and routing of traffic. By modeling the power contributions of each active device, we study how and how much BBU consolidation, optical bypass, and active traffic aggregation influence the AIP, for all the three architectures. Our results show that, in our case study, the proposed architectures enable savings up to about 60%-65% in dense-urban/urban and about 40% in rural scenarios.

Cloud Computing (CC) services are rapidly catching on as an alternative to conventional office-based computing. As cloud computing adoption increases, the energy consumption of the network and of the computing resources that underpin the cloud is growing and causing the emission of enormous quantities of CO2. Research is now focusing on novel "low-carbon" cloud-computing solutions. Renewable energy sources are emerging as a promising solution both to achieve drastic reduction in CO2emissions and to cope with the growing power requirements of data centers. These infrastructures can be located near renewable energy plants and data can be effectively transferred to these locations via reconfigurable optical networks, based on the principle that data can be moved more efficiently than electricity. This paper focuses on how to dynamically route on-demand optical circuits that are established to transfer energy-intensive data processing towards data centers powered with renewable energy. Our main contribution consists in devising two routing algorithms for connections supporting CC services, aimed at minimizing the CO2emissions of data centers by following the current availability of renewable energies (e.g., coming from sun and wind). The trade-off with energy consumption for the transport equipments is considered. We also compare three different IP-over-WDM network architectures. The results show that relevant reductions, up to about 30% in CO2emissions can be achieved using our approaches compared to baseline shortest-path-based routing strategies, paying off only a marginal increase in terms of network blocking probability.

Traffic in optical backbone networks is increasing and becoming more heterogeneous with respect to bandwidth and QoS requirements due to the popularity of high-bandwidth services (such as cloud computing, e-science, telemedicine, etc.), which need to coexist with traditional services (HTTP, etc.). Mixed-line-rate (MLR) networks that support lightpaths of different rates such as 10, 40, 100 Gb/s, etc., are being studied to better support the heterogeneous traffic demands. Here, we study the important topic of degraded services in MLR networks, where a service can accept some degradation (i.e., reduction) in bandwidth in case of a failure in exchange for a lower cost, a concept called partial protection. Network operators may wish to support degraded services to optimize network resources and reduce cost. We propose using multipath routing to support degraded services in MLR networks, a problem that has not been studied before and is significantly more challenging than in single-line-rate (SLR) networks. We consider minimum-cost MLR network design (i.e., choosing which transponder rates to use at each node), considering the opportunity to exploit multipath routes to support degraded services. We propose a mixed-integer-linear-program (MILP) solution and a computationally efficient heuristic, and consider two partial-protection models. Our illustrative numerical results show that significant cost savings can be achieved due to partial protection versus full protection and is highly beneficial for network operators. We also note that multipath routing in MLR networks exploits volume discount of higher-line-rate transponders by cost-effectively grooming requests over appropriate line rates to maximize transponder reuse versus SLR.

In this comment, we present some remarks on the availability evaluation of overlap dedicated segment protection (o-DSP) method in Tornatore et al., 2010, ãAvailability Formulations for Segment Protection" . We show how to correctly apply the pivotal decomposition availability-evaluation method in directed graphs (which was claimed to inapplicable in ), such that it gives the same (exact) connection availability value as the generating function method proposed in the original paper.

Operational networks get gradually upgraded to avoid capacity exhaustion and create resources to handle traffic growth. These upgrades require installation of new physical resources (e.g., line cards) and might be costly to network operators. The additional capital expenditure (CapEx) to upgrade the network can be reduced or eliminated by relocating excess resources which were deployed into the network, but are unused (e.g., due to optimistic forecasts or excessive overprovisioning). We investigate a cost-effective scheme to relocate excess capacity in an optical WDM backbone network to (i) reduce or eliminate upgrade costs, (ii) avoid capacity exhaustion (by migrating resources from underutilized links to overutilized links), and (iii) improve network robustness (by migrating resources from unreliable links to reliable links). We propose a novel generic relocation scheme that network operators can use based on their objectives to determine how to relocate excess resources. We also provide a solution which maximizes various benefits (e.g., network robustness and capacity exhaustion avoidance) of network operators by using game theory for our relocation scheme. Our results show that network operator can save a lot of CapEx while upgrading/improving the network by using our relocation scheme.

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.

Energy consumption in telecommunication networks keeps growing rapidly, mainly due to emergence of new Cloud Computing (CC) services that need to be supported by large data centers that consume a huge amount of energy and, in turn, cause the emission of enormous quantity of CO2. Given the decreasing availability of fossil fuels and the raising concern about global warming, research is now focusing on novel “low-carbon” telecom solutions. E.g., based on today telecom technologies, data centers can be located near renewable energy plants and data can then be effectively transferred to these locations via reconfigurable optical networks, based on the principle that data can be moved more efficiently than electricity. This paper focuses on how to dynamically route on-demand optical circuits that are established to transfer energy-intensive data processing towards data centers powered with renewable energy. Our main contribution consists in devising two routing algorithms for connections supporting CC services, aimed at minimizing the CO2emissions of data centers by following the current availability of renewable energy (Sun and Wind). The trade-off with energy consumption for the transport equipments is also considered. The results show that relevant reductions, up to about 30% in CO2emissions can be achieved using our approaches compared to baseline shortest-path-based routing strategies, paying off only a marginal increase in terms of network blocking probability.

This study investigates the survivable traffic grooming problem for elastic optical networks with flexible grid employing new transmission technologies, e.g., orthogonal frequency-division multiplexing (OFDM). In such networks, the strict ITU-T wavelength grid is not followed. Instead, optical transponders are developed to be capable of properly tuning their rates. Equipping the network with gridless and elastic optical paths allows us to efficiently use the optical spectrum. In this paper, we propose a novel elastic shared path protection (ESPP), which does not only provide the traditional backup sharing of shared path protection (SPP) (i.e., the backup capacity of one optical path (i.e., lightpath) can be shared among multiple backup paths, provided that their corresponding working paths are link-disjoint), but also explores a new opportunity of sharing enabled by the tunability of the transponders: in fact, the backup spectrum can be shared between two adjacent lightpaths on a link, if their corresponding working paths are link-disjoint. The elasticity of the transponder enables the expansion and contraction of the lightpaths, so that at one time, the backup spectrum is used by only one of the adjacent lightpaths. Note that in traditional wavelength-division-multiplexing (WDM) networks, the lightpaths are fixed-grid, rigid-bandwidth, nor can they overlap each other. Our results show that ESPP is more spectrum efficient than traditional SPP.

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.

Excess capacity (EC) is the unused capacity in a network. We propose EC management techniques to improve network performance. Our techniques exploit the EC in two ways. First, a connection preprovisioning algorithm is used to reduce the connection setup time. Second, whenever possible, we use protection schemes that have higher availability and shorter protection switching time. Specifically, depending on the amount of EC available in the network, our proposed EC management techniques dynamically migrate connections between high-availability, high-backup-capacity protection schemes and low-availability, low-backup-capacity protection schemes. Thus, multiple protection schemes can coexist in the network. The four EC management techniques studied in this paper differ in two respects: when the connections are migrated from one protection scheme to another, and which connections are migrated. Specifically, Lazy techniques migrate connections only when necessary, whereas Proactive techniques migrate connections to free up capacity in advance. Partial Backup Reprovisioning (PBR) techniques try to migrate a minimal set of connections, whereas Global Backup Reprovisioning (GBR) techniques migrate all connections. We develop integer linear program (ILP) formulations and heuristic algorithms for the EC management techniques. We then present numerical examples to illustrate how the EC management techniques improve network performance by exploiting the EC in wavelength-division-multiplexing (WDM) mesh networks.

Any operational network has some excess capacity (EC) to avoid early exhaustion of resources. We propose to exploit the EC in optical WDM backbone networks to support efficient and survivable traffic grooming where connection requests are of sub-wavelength granularity and each provisioned request has to be protected from a single link failure. Our novel EC management techniques can improve network performance, at no additional cost to the operator, since excess capacity is normally unutilized. Our techniques exploit EC such that a connection can use a protection scheme which provides high reliability but may consume more resources when traffic is low, but it switches to another protection scheme which provides lower reliability but is resource efficient by reprovisioning backup resources. As a complement, we propose hold-p-lightpath scheme to exploit EC by preventing the termination of pre-established (but unused) resources. The backup reprovisioning problem is split it into three subproblems: when, how, and what to reprovision; and we propose our solutions for each subproblem. For the what to reprovision subproblem, we design three methods with different reliability and resource-efficiency performance. We compare our approaches with traditional protection schemes for typical daily fluctuating traffic, and show that significant improvements in performance and cost can be achieved.

Today's access networks are increasingly shaped by the services that they provide to the end users. In a hybrid wireless-optical broadband access network (WOBAN), to access any service, connection requests require multi-hop communications over the wireless mesh network (WMN) and the Passive Optical Network (PON), and subsequently over the Internet to some server in the service provider's domain. To improve the delivery of such services over WOBAN, we can design a Cloud-Integrated WOBAN (CIW) by deploying a few cloud components (CCs) in the WOBAN itself and serve local services from these local CCs. In this paper, we propose a novel energy-saving routing mechanism, called Green Routing for CIW (GRC), that manages the activation of network components, namely ONUs and CCs, to minimize the overall energy consumption of CIW. It performs load-balanced anycast routing across active devices. Our performance evaluation shows that GRC operates with low average packet delay and achieves significant energy savings by turning off about 50% of the ONUs and about 20% of the CCs.

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.

Today's access networks are increasingly shaped by the services that they provide to the end users. In a hybrid wireless-optical broadband access network (WOBAN), to access any service, the corresponding requests for the services and responses require multi-hop communication over the wireless mesh network (WMN) and Passive Optical Network (PON) subsequently over the Internet to some server in the application service provider's domain. This may cause bottlenecks in the WMN and may result in degraded performance. In this paper, we propose a design for WOBAN that integrates a cloud over WOBAN (CoW) that provides different "cloud services" from within the access network. This has multiple benefits: 1) it offloads traffic over wireless links, 2) it reduces bottleneck from the gateways of WOBAN, 3) it reduces delays, and 4) it allows providers to facilitate different cloud services with their access network. In this paper, we study the issue of determining how the cloud components providing the services should be placed in WOBAN in order to optimize the resources while providing better service. We formulate this problem as a Mixed-Integer Linear Program (MILP) and solve it on a realistic study case. We show that, by integrating cloud with WOBAN, we can obtain significant performance improvement.

A service-level agreement (SLA) typically specifies, among other metrics, the availability a service provider (SP) promises to a customer. In an optical wavelength division multiplexing (WDM) network, connection-oriented provisioning is commonly based on whether the path's statistical availability complies with the SLA-requested availability. Because of the stochastic nature of network failures, the actually provisioned availability over a specific time period is subject to uncertainty, and hence the SLA is usually at risk. We consider this uncertainty and study provisioning to minimize SLA violations. We show that the SLA Violation Risk is affected by a number of factors (e.g., failure profiles, availability target, and penalty period), and hence cannot simply be characterized by statistical path availability. We formulate the problem of risk-aware provisioning in WDM mesh networks, where path selection is dictated by SLA Violation Risk. In particular, we focus on devising an efficient scheme capable of computing path(s) that are likely to successfully accommodate the SLA-requested availability. A novel technique is applied to convert links with heterogeneous failure profiles to reference links that capture the main risk features in a relative manner. Based on the “reference link” concept, our Risk-Aware Provisioning scheme uses only limited failure information. We also extend our Risk-Aware Provisioning to use shared-path protection (SPP) for connections with strict availability requirements. We evaluate the performance and demonstrate the effectiveness of our schemes in terms of SLA violation ratio compared to the generic availability-aware approaches.

Passive Optical Networks (PONs) are experiencing their first evolutionary steps in order to support higher capacity. As high-bandwidth applications and services continue to emerge, it is expected that capacity upgrades for existing PON infrastructure will occur in the near future. In this paper, we address the upgrading problem of existing PONs that need to increase their capacity, in an “as-needed” fashion and at different points in time. We propose and investigate the characteristics of a method that upgrades network line-rates and enables migration of network services towards new wavelength channels based on increasing traffic demands and cost constraints. Our method is intended to minimize capital expenses and system disruptions, while ensuring optimal resource usage. To do so, we have designed a multi-step model based on Mixed Integer Linear Programming (MILP) and pricing policies. We consider a typical case study for this problem, which is solved using CPLEX. Results from our illustrative numerical examples demonstrate the aforementioned attractive properties of our method.

Internet customers may have strict resilience requirements for specific times periods. However, these time-differentiated resilience requirements are not effectively addressed by current SLA frameworks. To satisfy the resilience-sensitive periods, a generic SLA framework typically provides upgraded protection over the entire service duration, which is unnecessary and expensive. In this study, we propose a novel SLA framework, which allows customers to specify Critical Windows (CW) to address time-differentiated resilience demands. CWs correspond to the periods that require extra protection, and are backed up using path-level pre-cross-connected protection. Considering the complementary profiles of CWs, we identify the opportunity for backup resource sharing in the time domain. To exploit backup sharing, a heuristic scheme is proposed (Globally-CW-Aware connection assignment) to reduce backup resources. Our study shows that, using the proposed SLA framework: 1) low operational complexity can be achieved; 2) backup resources can be significantly reduced; and 3) high resilience (Service Level) can be achieved for CWs.

Since telecom networks consume a large (and increasing) amount of energy, "green" strategies are desirable to help Service Providers (SP) operate their networks and provision services more energy-efficiently. In this study, we focus on operating optical backbone networks with green strategies. We consider a typical optical backbone network architecture, and minimize the Operational Power for service provisioning following a Traffic Engineering (TE) approach. Service provisioning is schematically decomposed as multiple serial operations, and power efficiency is analyzed for both optical bypass and traffic grooming. We propose a novel auxiliary graph, which can capture the flow of operations and their associated power. Based on the auxiliary graph, we present a Power-Aware scheme that minimizes the Operational Power. Simulation results show reduced power consumption by our scheme, in comparison to a generic traffic grooming approach.

A Service Level Agreement (SLA) typically specifies the availability a Service Provider (SP) promises to a customer. In an Optical Transport Network, finding a lightpath for a connection is commonly based on whether the availability of a lightpath availability complies with the connection's SLA-requested availability. Because of the stochastic nature of network failures, the actual availability of a lightpath over a specific time period is subject to uncertainty, and the SLA is usually at risk. We consider the network uncertainty, and study routing to minimize the probability of SLA violation. First, we use a single-link model to study SLA Violation Risk (i.e., the probability of SLA violation) under different settings. We show that SLA Violation Risk may vary by paths and is affected by other factors (e.g., failure rate, connection holding time, etc.), and hence cannot be simply described by path availability. We then formulate the problem of risk-aware routing in mesh networks, in which routing decisions are dictated by SLA Violation Risk. In particular, we focus on devising a scheme capable of computing lightpath(s) that are likely to successfully accommodate a connection's SLA-requested availability. A novel technique is applied to convert links with heterogeneous failure profiles to reference links which capture the main risk features in a relative manner. Based on the "reference link" concept, we present a polynomial Risk-Aware Routing scheme using only limited failure information. In addition, we extend our Risk-Aware Routing scheme to incorporate shared path protection (SPP) when protection is needed. We evaluate the performance and demonstrate the effectiveness of our schemes in terms of SLA violation ratio and, more generally, contrast them with the generic availability-aware approaches.

Segment Protection (SP) is an efficient scheme for protection in WDM optical networks. This letter provides algebraic formulations to evaluate SP availability both in the dedicated and shared backup case. The availability models are applied to numerical examples and significant relations between SP availability and relevant connection parameters are identified.

Protection techniques for optical networks mainly rely on preallocated backup bandwidth, which may not be able to provide full protection guarantee when multiple failures occur in a network. To protect against multiple concurrent potential failures and to utilize the available resources more efficiently, strategies such as backup reprovisioning (BR) rearrange backups of protected connections after one failure occurs or, more generally, whenever the network state changes, e.g., when a new request arrives or terminates. Recently, new solutions for automatized management in optical networks promise to allow customers to specify on-demand the terms of the service level agreement (SLA) to be guaranteed by the service provider. In this paper, we study different backup reprovisioning techniques able to further reduce the capacity requirements exploiting the knowledge, among the other service level specifications (SLSs), of the connection holding time.

Many networking applications require distribution of data from a central point to multiple destinations; this distribution can be efficiently achieved by the means of multicasting. Traditionally, multicasting has been considered for on-demand applications such as HDTV, Video-on-Demand (VoD), IPTV, which usually require to start data transmission immediately. However, in the case of emerging e-Science and high-performance applications (which frequently need to replicate large datasets to multiple locations), the data distribution does not necessarily need to take place instantaneously; instead, the multicast session can be accommodated considering a flexible start time for the large data transfer. We study the efficient provisioning of Multicast Data-Distribution Requests (MDDRs) with flexible scheduling over WDM networks. We consider the practical case of multicast sessions that may require less than the entire capacity of a wavelength; hence the multicast sessions need to be "traffic-groomed". Our first multicast provisioning approach (named Rand) generates randomized alternate multicast trees on which we try to provision the multicast session, and then attempts to assign wavelengths and schedule the session's start time. In our second approach (named AllSlots), for each available start time S, we dynamically generate trees depending on the network state at time S. In our next approach (named Break), for the cases when provisioning an entire multicast tree fails, we enable the possibility of "breaking" the tree into subtrees (with independent start times) serving subsets of destinations. Moreover, we study the impact of partitioning the datasets into pieces on our multicast provisioning approaches, and also compare our multicast algorithms with an unicast approach.

Recently, new solutions for automatized management in optical networks promise to allow customers to specify on-demand the terms of the service level agreement (SLA) to be guaranteed by the service provider. In this paper we show that is possible to design a highly efficient load balancing algorithm, called RABBIT, for the dynamic provisioning of connections exploiting the knowledge, among the other service level specifications (SLS), of the connections duration. The core idea of RABBIT consists in routing connections based on the transient probability of future-link congestion, that can be estimated with higher precision when the knowledge of connections durations is given. So, we introduce a time-dependent link-weight assignment that evaluates future link congestions probability based on the transient analysis of the Markovian model of the link, making it computationally feasible by means of an effective approximation technique. By means of an extensive set of simulative experiments, we compare our approach to other traditional holding-time agnostic, yet efficient, dynamic routing algorithms. We consider different performance metrics, among which the blocking probability (BP), in a wavelength-convertible WDM mesh network scenario. For a typical US nationwide network, RABBIT obtains savings on BP of up to 20% for practical scenarios.

In this paper we devise an highly-efficient load balancing algorithm, called LB-HTA, for the dynamic provisioning of connections with known duration in WDM networks. We introduce a time-dependent link-weight assignment that captures future congestion of links, leveraging the knowledge of connection durations. By means of an extensive set of simulative experiments, we compare our approach to other traditional, yet holding-time agnostic, dynamic routing algorithms. For a typical US nationwide network, LB-HTA obtains significant saving in blocking probability for practical scenarios. Moreover, we address two key-questions regarding holding-time-aware dynamic routing. Three main traffic models are considered here, namely i) dynamic traffic, ii) dynamic traffic with known durations and iii) scheduled traffic: how much the knowledge of connection durations improves the performance with respect of a holding- time-agnostic solution? Is the obtained solution close to the most effective solution provided by scheduled traffic? In order to exhaustively evaluate the performance of LB-HTA, we consider as benchmark the solution obtained under the well-known Scheduled Traffic model (TI-ST) and also under an approximated, but more effective, approach for traffic scheduling, called Time- Variant Scheduled Traffic (TV-ST). For both TI-ST and TV-ST, Integer Linear Program (ILP) formulations are proposed and results compared with dynamic routing algorithms.

A service level agreement (SLA) typically specifies the availability a service provider (SP) promises to a customer. Current schemes usually employ backup resources to achieve high SLA satisfaction. We propose a new provisioning framework, called service cluster (SC), which uses no explicit backup resources. By grouping several services with (typically) different availability specifications, an SC can dynamically re-allocate resources to avoid SLA violations. Services that can tolerate additional down time lend resources to services that need to be kept running. We first analyze a condition for admission control. We then propose a dynamic resource allocation scheme (ADT1balancing scheme, SC-ABS) and apply it to the SC framework. The dynamic management of SC-ABS is realized by novel SLA-violation estimation in an event-driven manner without continuous monitoring. We compare SC-ABS with shared-path protection, and numerically show its various advantages in terms of: 1) higher SLA satisfaction (up to 30% more); 2) lower service blocking ratio; 3) higher tolerance of failures; 4) more balanced SLA satisfaction; and 5) consuming no explicit backup (or standby) resources. Our scheme can meet the SLA of significantly more services than protection-based schemes, thus providing more profit for the SP and lower cost for the customer.

Most of the previous works on optical burst switching (OBS) assume in their analysis that signalling does not affect network performance. It is analysed here, under which conditions the effect of signalling is actually negligible, taking into account the effect of signalling in the evaluation of burst discard probability. First, analytical models for two different signalling approaches in an OBS network are presented: ‘out-of-band’ and ‘in-band’ techniques. The impact of these two signalling strategies in terms of the probability of burst discard are evaluated, identifying the component of bursts discarded as a consequence of control message losses or of excessive signalling delay. A new method is also discussed, based on the previous models, to assign the correct amount of resources to the control plane. To verify the accuracy of the analytical results, these are compared with results based on discrete-event simulationns: results are found to be in a highly satisfactory agreement with simulations.

Many large-scale scientific applications need to aggregate large amounts of data from multiple distributed sites to a centralized facility. We call such a request as a data-aggregation request (DAR). In this study, we investigate the novel problem of on-demand DAR provisioning over a wavelength-division multiplexing (WDM) backbone network. We provide a mathematical formulation for our problem as a mixed integer linear program (MILP). To solve large versions of our problem, we propose a DAR provisioning heuristic (called DARP). We use the MILP with various objectives as a benchmark for studying the performance of DARP.

In a wavelength-division-multiplexing (WDM) optical network, backup reprovisioning (BR) provides a means to improve survivability (by protecting against multiple concurrent potential failures) and to utilize the available resources more efficiently. In particular, global backup reprovisioning (GBR), i.e. the reprovisioning involving the totality of the backup capacity has been demonstrated to be almost as capacity-effective as other re-optimization techniques involving primary paths. Unfortunately, existing ILP and heuristic solutions for the GBR problem are very time consuming and unsuitable to be adopted in time-sensitive network scenarios (e.g., subject to frequent network-status changes and/or connection-provisioning setup- time constraints). In this paper, we first analytically evaluate how the GBR computational time may impact on the connection provisioning process. Then, we propose a fast BR procedure based on simulated annealing (SA) which significantly decreases the computational time with respect to existing heuristics while still achieving very high resource efficiency.

Protection techniques for optical networks mainly rely on pre-allocated backup bandwidth, which may not be able to provide full protection guarantee when multiple failures occur in a network. To protect against multiple concurrent potential failures and to utilize the available resources more efficiently, strategies such as backup reprovisioning rearrange backups of protected connections after one failure occurs or, more generally, whenever the network state changes, e.g., when a new request arrives or terminates. Recently, new solutions for automatized management in Optical networks promise to allow customers to specify on-demand the terms of the service level agreement (SLA) to be guaranteed by the service provider. In this paper we show that is possible to further reduce the capacity requirements of backup reprovisioning techniques exploiting the knowledge, among the other service level specifications (SLS), of the connection holding-time. Our main contributions are as follows. First, we prove that the problem of backup reprovisioning for all the lightpaths requiring shared-path protection under a current network state is NP-complete. Second, we provide a mathematical ILP model of the reprovisioning problem considering the additional holding-time information. Third, since the problem is NP-complete and we can not efficiently rely on exact approaches, a new global reprovisioning algorithm called Ph-GBR is proposed which can significantly reduce the resource overbuild exploiting the information about connection durations. By means of simulative experiments, we compare capacity requirement and computational complexity of Ph-GBR to those of another holding-time unaware, yet efficient algorithm, called GBR, considering a dynamic traffic in a wavelength-convertible WDM mesh network scenario.

Progress in network technologies and protocols is paving the road towards flexible optical transport networks, in which dynamic leasable circuits could be set up and released on a short-term basis according to customers requirements. Recently, new solutions for automated network management promise to allow customers to dinamically specify the terms of the Service Level Agreement (SLA) to be guaranteed by the service provider. Since this new information is made available, we propose to exploit the knowledge of connection holding time, among the other Service Level Specifications (SLS), to improve the routing efficiency. In this work, we consider that a typical electronic-layer (e.g., SDH or MPLS) demand requires only a fraction of the capacity of the single wavelength bandwidth and we investigate a new algorithm for traffic grooming of sub-wavelength connections in an optical mesh network. We rely on the knowledge of the holding time of connection requests to exploit lightpath capacity and hence to achieve significant reduction in blocking probability for the traffic grooming problem. Our new methodology is applied on a typical US nation-wide network and results are compared with those given by previous known approaches.

In this paper we propose an edge-to-edge closed-loop scheme to provide Quality-of-Service (QoS) in Optical Burst Switching (OBS) networks. Performance parameters for each burst flow are exchanged by a feedback message called Service Control Message (SCM) which is processed only at the edge/ingress nodes. According to the information contained in the SCM the edge nodes could adapt the values of some flow parameters (typically burst assembly parameters and offset time) to apply differentiated quality of service to the various flows. Simulations results are presented to investigate the feasibility of this new proposal.

Access control for group communication must ensure that the legitimate users are able to access the authorized data streams, while preventing the non-legitimate users from gleaning any unauthorized data stream. This could be done by distributing an encrypting key to each member of the group to be secured. To achieve a high level of security, the group key should be changed every time a user joins or leaves the group, so that a former group member has no access to current communications and a new member has no access to previous communications. Since group memberships could be very dynamic, the group key should be changed frequently. So far, different schemes for efficient key distribution have been proposed to limit the key-distribution overhead. In previous works, the performance comparison among these different schemes have been based on simulative experiments, where a set of users join and leave a secure group according to a basic statistical representation of users’ behavior. In this paper we propose a new statistical model to represent users’ behavior and compare it to the modelling approach so far adopted in the literature. Our new model is able to achieve a superior statistical confidence of the results and to lead the system to a steady state. In particular, we apply our new model to the comparison of two recent proposals for key distribution in order to show the better quality of the results achieved by means of our model.

Progress in network technologies and protocols is paving the road towards flexible optical transport networks, in which leasable circuits could be set up and released on a short-term basis. Thus we consider it reasonable that, at least for some types of services, the holding time of connection requests can be known in advance. In this paper, we propose to exploit the knowledge of connection-holding time to improve the performance of an algorithm for shared-segment protection (SSP). For a typical US nationwide network, we compared our approach to an holding-time-unaware, but otherwise shared segmented efficient, approach, obtaining savings on resource overbuild of up to 7% for practical scenarios.

This papers reports the advances in optical core networks research coordinated in the framework of the e- photon/ONe and e-photon/ONe+ networks of excellence.

A design technique for reliable optical transport networks is presented. The network is first dimensioned in order to carry a given set of static protected optical connections, each one routed maximizing its availability. The network can be further optimized by minimizing the number of fibers to be installed, while keeping a control on connection availability, which can remain the same or decrease by a prefixed margin factor. Design and optimization algorithms are provided for networks adopting dedicated and shared path-protection. The optimization approach is heuristic. Results obtained by applying the proposed technique to two case-study networks are shown and discussed. These two case-study experiments are carried out exploiting a realistic model to evaluate terrestrial and submarine optical link availability.

Efficient planning and optimization of wavelength division multiplexing networks is an important issue today. Integer linear programming (ILP) is the most used exact method to perform this task. We propose a new ILP formulation that allows to solve optimization with less computational effort compared to other ILP approaches. This formulation applies to multifiber mesh networks with or without wavelength conversion, when either the total fiber number or the total fiber length is the cost function to be minimized. After presenting the formulation we discuss the results we obtained by exploiting it in the optimization of two case-study networks.