Multi-band elastic optical networks (MB-EONs) transmit information in multiple bands to increase the available capacity. However, they suffer from quality of transmission (QoT) degradation caused by the inter-channel stimulated Raman scattering effect, which requires addressing through tailored resource assignment. Additionally, dynamically arriving and departing optical service requests generate spectrum fragmentation (SF), where spectrum resources become scattered into non-continuous chunks and aggravate service blocking ratio (SBR) even when the total available bandwidth is sufficient. To jointly address these challenges, we propose an SF- and QoT-aware algorithm for routing, band, modulation format, and spectrum assignment (RBMSA), along with proactive spectrum defragmentation (SD), referred to asSFQA-defrag. The algorithm considers SF metrics and QoT levels of available channels across multiple candidate paths to ensure that the QoT requirements are met while minimizing the SF. The SD process proactively reorganizes spectrum allocation to reduce fragmentation by consolidating the spectrum gaps, which leads to lower blocking of future requests. TheSFQA-defragalgorithm is evaluated against benchmark algorithms that independently consider either QoT or SF in three reference backbone topologies. The results demonstrate thatSFQA-defragsignificantly reduces the SBR and SF compared to benchmarks, albeit with a slight increase in the average path length.

As the foundation of global communication networks, optical fibers are vulnerable to various disruptive events, including mechanical damage, such as cuts, and malicious physical layer breaches, such as eavesdropping via fiber bending. Traditional monitoring methods often fail to identify subtle or novel anomalies, stimulating the proliferation of ML techniques for detection of threats before they cause significant harm. In this paper, we evaluate the performance of SSL and USL approaches for detecting various abnormal events, such as fiber bending and vibrations, by analyzing polarization signatures with minimal reliance on labeled data. We experimentally collect thirteen polarization signatures on three different types of fiber cable and process them using OCSVM as an SSL, and DBSCAN as a USL algorithm for anomaly detection. We introduce tailored evaluation metrics designed to guide hyper-parameter tuning and capture generalization over different anomaly types, detection consistency, and robustness to false positives, enabling practical deployment of OCSVM and DBSCAN in optical fiber security. Our findings demonstrate DBSCAN as a strong contender to detect previously unseen threats in scenarios where labeled data are not available, despite some variability in performance between different scenarios, with F1 score values between 0.615 and 0.995. In contrast, OCSVM, trained on normal operating conditions, maintains high F1 scores of 0.98 to 0.998, demonstrating accurate detection of complex anomalies in optical networks.

Filterless optical networks (FONs) are a cost-effective optical networking technology that replaces reconfigurable optical add-drop multiplexers, used in conventional, wavelength-switched optical networks (WSONs), by passive optical splitters and couplers. FONs follow thedrop-and-wastetransmission scheme, i.e., broadcast signals without filtering, which generates spectrum waste. Programmable filterless optical networks (PFONs) reduce this waste by equipping network nodes with programmable optical white box switches that support arbitrary interconnections of passive elements. Cost-efficient PFON solutions require optimal routing, modulation format and spectrum assignment (RMSA) to connection requests, as well as optimal design of the node architecture. This paper presents an optimization framework for PFONs. We formulate the RMSA problem in PFONs as a single-step integer linear program (ILP) that jointly minimizes the total spectrum and optical component usage. As RMSA is an NP-complete problem, we propose a two-step ILP formulation that addresses the RMSA sub-problems separately and seeks sub-optimal solutions to larger problem instances in acceptable time. Simulation results indicate a beneficial trade-off between component usage and spectrum consumption in proposed PFON solutions. They use up to 64% less spectrum than FONs, up to 84% fewer active switching elements than WSONs, and up to 81% fewer optical amplifiers at network nodes than FONs or WSONs.

Exponential growth of bandwidth demand, spurred by emerging network services with diverse characteristics and stringent performance requirements, drives the need for dynamic operation of optical networks, efficient use of spectral resources, and automation. One of the main challenges of dynamic, resource-efficient Elastic Optical Networks (EONs) is spectrum fragmentation. Fragmented, stranded spectrum slots lead to poor resource utilization and increase the blocking probability of in-coming service requests. Conventional approaches for Spectrum Defragmentation (SD) apply various criteria to decide when, and which portion of the spectrum to defragment. However, these polices often address only a subset of tasks related to defragmentation, are not adaptable, and have limited automation potential. To address these issues, we propose DeepDefrag, a novel framework based on reinforcement learning that addresses the main aspects of the SD process: determining when to perform de-fragmentation, which connections to reconfigure, and which part of the spectrum to reallocate them to. DeepDefrag outperforms the well-known Older-First First-Fit (OF-FF) defragmentation heuristic, achieving lower blocking probability under smaller defragmentation overhead.

The ongoing evolution of optical networks towards autonomous systems supporting high-performance services beyond 5G requires advanced functionalities for automated security management. To cope with evolving threat landscape, security diagnostic approaches should be able to detect and identify the nature not only of existing attack techniques, but also those hitherto unknown or insufficiently represented. Machine Learning (ML)-based algorithms perform well when identifying known attack types, but cannot guarantee precise identification of unknown attacks. This makes Root Cause Analysis (RCA) crucial for enabling timely attack response when human intervention is unavoidable. We address these challenges by establishing an ML-based framework for security assessment and analyzing RCA alternatives for physical-layer attacks. We first scrutinize different Network Management System (NMS) architectures and the corresponding security assessment capabilities. We then investigate the applicability of supervised and unsupervised learning (SL and UL) approaches for RCA and propose a novel UL-based RCA algorithm called Distance-Based Root Cause Analysis (DB-RCA). The framework’s applicability and performance for autonomous optical network security management is validated on an experimental physical-layer security dataset, assessing the benefits and drawbacks of the SL- and UL-based RCA. Besides confirming that SL-based approaches can provide precise RCA output for known attack types upon training, we show that the proposed UL-based RCA approach offers meaningful insight into the anomalies caused by novel attack types, thus supporting the human security officers in advancing the physical-layer security diagnostics.

Design of reliable wireless backhaul networks is challenging due to the inherent vulnerability of wireless backhauling to random fluctuations of the wireless channel. Considerable studies deal with modifying and designing the network topology to meet the reliability requirements in a cost-efficient manner. However, these studies ignore the correlation among link failures, particularly those caused by weather disturbances. Consequently, the resulting topology designs may fail to meet the network reliability requirements under correlated failure scenarios. To fill this gap, we study the design of cost-efficient and reliable wireless backhaul networks under correlated failures with a focus on rain disturbances. We first propose a new model to consider the pairwise correlation amongf links along a path. The model is verified on real data, indicating an approximation closer to reality than the existing independent failure model. Second, we model the correlation among different paths by defining a penalty cost. Considering the newly formalized link and path correlation, we formulate the correlation-aware network topology design problem as a quadratic integer program to find the optimal solutions. Two lightweight heuristic algorithms are developed to find near-optimal solutions within reasonable time. Performance evaluation shows that correlation-aware design substantially improves the resiliency under rain disturbances at a slightly increased cost compared to independent failure approaches.

Abstract High throughput, resilience, and low latency requirements drive the development of 5G‐enabled content delivery networks (CDNs) which combine core data centers (cDCs) with edge data centers (eDCs) that cache the most popular content closer to the end users for traffic load and latency reduction. Deployed over the existing optical network infrastructure, CDNs are vulnerable to link cut attacks aimed at disrupting the overlay services. Planning a CDN to balance the stringent service requirements and increase resilience to attacks in a cost‐efficient way entails solving the content placement problem (CPP) across the cDCs and eDCs. This article proposes a framework for finding Pareto‐optimal solutions with minimal user‐to‐content distance and maximal robustness to targeted link cuts, under a defined budget. We formulate two optimization problems as integer linear programming (ILP) models. The first, denoted as K‐best CPP with minimal distance (K‐CPP‐minD), identifies the eDC/cDC placement solutions with minimal user‐to‐content distance. The second performs critical link set detection to evaluate the resilience of the K‐CPP‐minD solutions to targeted fiber cuts. Extensive simulations verify that the eDC/cDC selection obtained by our models improves network resilience to link cut attacks without adversely affecting the user‐to‐content distances or the core network traffic mitigation benefits.

Inherent vulnerability of wireless backhauling to random fluctuations of the wireless channel complicates the design of reliable backhaul networks. In the presence of such disturbances, network reliability can be improved by providing redundant paths between given source and destination. Many studies deal with modifying and designing the network topology to meet the reliability requirements in a cost- efficient manner. However, these studies ignore the correlation among link failures, such as those caused by rain. Consequently, the resulting topology design solutions may fail to satisfy the network reliability requirements under correlated failure scenarios. To address this issue, this paper studies the design of reliable wireless backhaul networks under correlated failures with focus on rain fading. We consider green-field topology design and brown-field topology upgrade scenarios with the objective to minimize the total cost of wireless links added to meet the target reliability requirement in the presence of correlated link failures. We propose a new model to formulate the spatial correlation using pairwise joint probability distribution of rain attenuation between different links. This model is applied to consider the link- wise correlation along individual paths, as well as the correlation among the multiple redundant paths from the source to the destination node of a traffic flow. We formulate the problem as a quadratic integer program, which is NP-hard, and develop a heuristic algorithm to find near-optimal solutions. Performance evaluation shows that correlation-aware design improves the resiliency under rain disturbance at a slightly increased cost.

Wireless network solutions, a dominant enabling technology for the backhaul segment, are susceptible to weather disturbances that can substantially degrade network throughput and/or delay, compromising the stringent 5G requirements. These effects can be alleviated by centralized rerouting realized by software defined networking architecture. However, careless frequent reconfigurations can lead to inconsistencies in the network states due to asynchrony between different switches, which can create congestion and limit the rerouting gain. The aim of this paper is to minimize the total data loss during rain disturbance by proposing an algorithm that decides on the timing, the sequence, and the paths for rerouting of network flows considering the imposed congestion during reconfiguration. At each time sample, the central controller decides whether to adopt the optimal routes at a switching cost, defined as the imposed congestion, or to keep using existing, sub-optimal routes at a throughput loss. To find optimal solutions with minimal data loss in a static scenario, we formulate a dynamic programming problem that utilizes perfect knowledge of rain attenuation for the whole rain period. For dynamic scenarios with unknown future rain attenuation, we propose an online consistency-aware rerouting algorithm, called consistency-aware rerouting with prediction (CARP), which uses the temporal correlation of rain fading to estimate future rain attenuation. Simulation results on synthetic and real networks validate the efficiency of our CARP algorithm, substantially reducing data loss and increasing network throughput with a fewer number of rerouting actions compared to a greedy and a regular rerouting benchmarking approaches.

Providing high capacity to the end users is one of the main challenges for the fifth generation (5G) of mobile networks. The users' habit to consume online contents indoor makes the outdoor-to-indoor capacity provisioning impractical, especially when the high-frequency bands proposed for 5G are employed. The centralized radio architecture (CRA) is an in-building solution, which relies on the centralization of baseband processing functions, fully or partly allowing for centralized cell management while providing signals directly inside the buildings. On the other hand, the massive deployment of CRAs in urban areas may yield to unacceptably high installation costs, due to the radio network equipment to be activated. To make CRAs appealing to mobile operators, we propose different deployment strategies to minimize the CRA deployment cost. We define the remote radio unit placement (RRUP) problem and formulate it as an integer linear program, obtaining optimal deployment solutions in small urban residential scenarios. We prove the RRUP problem to be NP-hard, requiring heuristic approaches to solve large problem instances. To this end, we propose an effective and scalable heuristic for minimizing the amount of radio equipment required to deploy CRAs in large urban areas.

Elastic optical networking is considered a promising candidate to improve the spectral efficiency of optical networks. One of the most important planning challenges of elastic optical networks is the NP-hard routing and spectrum assignment (RSA) problem. In this paper, we investigate offline RSA in elastic filterless optical networks, which use a passive broadcast-and-select architecture to offer network agility. Here, an elastic optical network is referred to as the optical network that can adapt the channel bandwidth, data rate, and transmission format for each traffic demand in order to offer maximum throughput. In elastic filterless networks, the presence of unfiltered signals resulting from the drop-and-continue node architecture must be considered as an additional constraint in the RSA problem. In this paper, first, the RSA problem in elastic filterless networks is formulated by using an integer linear program to obtain optimal solutions for small networks. Due to the problem complexity, two efficient RSA heuristics are also proposed to achieve suboptimal solutions for larger networks in reasonable time. Simulation results show that significant bandwidth savings in elastic filterless networks can be achieved compared with the fixed-grid filterless solutions. The proposed approach is further tested in multi-period traffic scenarios and combined with periodical spectrum defragmentation, leading to additional improvement in spectrum utilization of elastic filterless optical networks.

Long-reach passive optical networks (LR-PONs) are able to effectively support the growing demand of traffic originating from residential and business customers. Failures of metro/core (M/C) nodes serving the traffic to/from the access networks covered by LR-PONs, may potentially affect hundreds or thousands of customers. One way of guaranteeing 100% survivability from single-node failures is to apply dual-homing, where each LR-PON is connected to two M/C nodes, and combine it with node-disjoint dedicated-path protection (DPP). In this paper, we present a new approach to provide network survivability against single M/C node failures. Instead of applying dedicated path protection (DPP) strategy, which can require huge amount of extra resources, we combine an unprotected network design with a dynamic multilayer restoration algorithm. Our aim is to determine a suitable amount of resource overbuild (in terms of extra transponders) needed to provide average connection availability close to that guaranteed by DPP. Preliminary results show that dimensioning for the worst-case scenario among a set of predefined M/C node failures, i.e., the one disrupting the highest number of connections, yields to a cost-effective strategy requiring up to 35% less transponders than DPP, while offering the same average connection availability.

Service relocation represents a promising strategy to provide flexible and resource efficient resiliency from link failures in the optical cloud environment. However, when a failure affects a node hosting a datacenter (DC), service relocation from the affected DC is not possible. One alternative to protect against DC failures relies on using design strategies that duplicate the IT (i.e., storage and processing) resources in a backup DC at the expense of increasing resource overbuild (i.e., cost) of the network. This work proposes a dimensioning strategy based on the shared-path shared-computing (SPSC) concept able to protect against any single link, server, or DC failure scenario with minimal resource overbuild for the network and IT infrastructures. SPSC is based on the intuition that only storage units need complete replication in backup DC, while processing units can be instantiated only after the occurrence of a failure, leaving the design strategy some leeway to minimize their number. As result, the proposed SPSC design shows a considerable reduction in the amount of backup resources when compared to the dedicated protection strategies.