The operational security of latency-sensitive networked applications is increasingly threatened by evolving malicious attacks that compromise operational integrity and network performance. Human-to-machine (H2M) applications, which rely on seamless bidirectional control signals and haptic feedback transmission, exemplify such latency-sensitive use cases. Existing learning-based malicious attack detection frameworks suffer from their reliance on pre-trained datasets, making machine learning models within them ineffective against previously unseen attack patterns. As attack profiles dynamically evolve, static models become obsolete, necessitating adaptive mechanisms to maintain detection accuracy. In this context, concept drift adaptation will serve as a critical tool for enabling models to continuously adjust to changing traffic distributions and emerging attack patterns. However, real-world H2M applications lack access to accurately labeled malicious traffic data, making real-time adaptation of defense mechanisms infeasible. To address these challenges, we propose a Concept Drift Adaptation-facilitated malicious attack Defense framework (CDAD). Firstly, CDAD employs Adaptive Random Forest as an incremental learning approach, integrating an error-rate-based concept drift detection mechanism to dynamically identify evolving attack patterns and trigger adaptive model updates. Secondly, a haptic behavior classifier is introduced to classify expected human operator interactions and compare them with real-time haptic feedback from remote machines. This enables automated traffic relabeling, allowing CDAD to adapt to previously unseen attacks without relying on pre-labeled datasets. The superior performance of CDAD over existing state-of-the-art methods is demonstrated across various malicious attack scenarios through extensive simulations. Results show that with CDAD, the attack success rate can be limited to 3%, while maintaining an inference time below 1ms, thereby ensuring effective and efficient malicious attack defense in latency-sensitive H2M applications.

Satellite optical networks combined with multi-beam technologies can be referred to as multi-beam satellite optical networks (MB-SONs). These networks are expected to play a crucial role in satellite Internet, potentially achieving Gigabit/s inter-satellite (IS) communication in the future. However, the growing demand for the satellite-to-mobile communications bring a challenge for making use of hybrid IS and satellite-to-ground (SG) transmission resources, which is worthy of studying. Given the different characteristics of IS links and SG links in terms of time windows and transmission capacities, existing solutions can hardly provide a suitable option for the optimal utilization of transmission capacity in MB-SONs. In this paper, we focus on the joint scheduling of optical wavelengths in IS and multiple beams in SG for the services sent from one ground station to the other ground station (which are referred to as end-to-end services). In response to the above-mentioned different characteristics of IS and SG links, we find that at least two constraints need to be followed in the joint scheduling. Also, the length of common time window will not exceed the minimum time window of IS and SG links on the path. The second one is that the volume of transmission capacity of a path depends on the length of CTW and the minimum bandwidth of IS and SG links, which should meet the service requirements. Considering these two constraints related to the length of CTW and the volume of transmission capacity, the main contributions of this paper can be concluded: i) defining the joint time slot allocation (JTSA) problem for multiple beams and wavelengths with different transmission capacities, ii) proposing an integer linear programming (ILP) model with object to minimize the number of time slots occupied by services, iii) designing the common time window for time slot assignment (CTW-TSA) algorithm as an option in practical implementations. The proposed ILP and CTW-TSA algorithm are evaluated by comparing the simulation results with the scheme using. The simulation of the CTW-TSA algorithm was compared to separate resource scheduling (SRS) without the store-and-forward function. The results showed a reduction of almost 0.201 in service blocking probability and an increase in average bandwidth utilization of about 0.159 for IS links and 0.164 for SG uplinks/downlinks.

We propose a first Asynchronous Federated Split Learning (AFSL), to add the flexibility of asynchronous computing to the combination of federated and split learning. This amounts to designing a harmonious combination of different paradigms in order to benefit from the advantages of each of them and to reduce the impacts of their shortcomings.This way, AFSL answers to the increasingly rising interest for distributed algorithms with the advent of edge computing in order to support new market segments, such as cloud gaming, immersive eXtended Reality (XR), indoor positioning, and mission critical IoT networks, with stringent requirements on latency and reliability.Computational experiments are conducted on IID and non-IID datasets to investigate the added value of the asynchronous feature. Results indicate that AFSL can accelerate model learning by up to 86% without sacrificing the model’s convergence and accuracy. Indeed, not only average training times are reduced, but clients use fewer resources, a critical characteristic for devices with limited computing capabilities, e.g., in edge devices. Performance degradation can be mitigated by a careful selection of the aggregation principle. Other advantages are with AFSL training in dynamic scenarios as it provides robustness with a short recovery time by leveraging asynchronous client training.

Network operators must deal with Classes of Service (CoS), which have several quality requirements, such as latency, bandwidth/capacity, priority, etc. Besides, it is observed an increase in the volume of traffic that is offered to the transport network. This traffic can be affected by network natural disasters or human-made attacks. In this case, network operators must decide which services to restore, considering their different requirements. In this work, we present a Lightpath Selection Algorithm (LSA) that aims to select lightpaths to be restored after a resource crunch. This algorithm has a multicriteria decision approach considering CoS, Bandwidth, number of Hops, and Holding time. Moreover, service degradation is also considered for those lightpaths that can not be restored with full bandwidth. Results show that our proposed algorithm can improve network restorability and availability without penalizing low-level CoSs.

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.

Service providers can leverage shared resources to reduce the overall amount of required resources while keeping acceptable Quality of Service (QoS) levels. Kubernetes (K8s) provides a Horizontal Pod Autoscaling (HPA) mechanism that allows to automatically adjust the number of Pods to closely follow the user demand variations over time. To properly leverage shared resources with HPA, service providers need to limit the use of dedicated resources and overprovisioning. However, in the case of traffic spikes, there may not be enough resources to satisfy the demand. The HPA, which relies on resource usage to drive the scaling, is unaware of how many requests could not be served with the required QoS. This might result in an underestimation of the number of required Pods to be added, leading to additional QoS degradation. This demonstration showcases the effectiveness of a new Pod autoscaling mechanism (i.e., Service Aware Pod Autoscaling (SAPA)) that relies on user request measurements from the service load balancer to better estimate the number of required Pods. SAPA allows selecting the amount of Pod resources (dedicated and shared) in a simple way. We demonstrate the benefits of SAPA by comparing it to a K8s cluster based on the traditional HPA in terms of resource usage and service latency.

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.

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.

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.

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.

The Network Function Virtualization (NFV) is very promising for efficient provisioning of network services and is attracting a lot of attention. NFV can be implemented in commercial off-the-shelf servers or Physical Machines (PMs), and many network services can be offered as a sequence of Virtual Network Functions (VNFs), known as VNF chains. Furthermore, many existing network devices (e.g., switches) and collocated PMs are underutilized or over-provisioned, resulting in low power-efficiency. In order to achieve more energy efficient systems, this work aims at designing the placement of VNFs such that the total power consumption in network nodes and PMs is minimized, while meeting the delay and capacity requirements of the foreseen demands. Based on existing switch and PM power models, we formulate an Integer Linear Programming (ILP) model to find the optimal solution. We also propose a heuristic based on the concept of Blocking Islands (BI), and a baseline heuristic based on the Betweenness Centrality (BC) property of the graph. Both heuristics and the ILP solutions have been compared in terms of total power consumption, delay, demands acceptance rate, and computation time. Our simulation results suggest that BI-based heuristic is superior compared with the BC-based heuristic, and very close to the optimal solution obtained from the ILP in terms of total power consumption and demands acceptance rate. Compared to the ILP, the proposed BI-based heuristic is significantly faster and results in 22% lower end-to-end delay, with a penalty of consuming 6% more power in average.

In this paper, we propose a new restoration algorithm for disaster recovery in Elastic Optical Networks (EONs). The algorithm considers the different requirements on restoration delay and bandwidth degradation while restoring over multiple routes. Furthermore, the algorithm is able to adapt the connection recover process to the post-disaster network conditions and, consequently, increase the number of restored connections. Results indicate that the proposed strategy is able to improve connections restorability and decrease bandwidth blocking probability when compared to restoration approach based on bandwidth degradation and multipath recovery.

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.

Base station (BS) densification is increasingly adopted by mobile operators in order to support increasing traffic demand. However, a large number of BSs requires many backhaul connections, which may be very expensive. For this reason, provisioning high speed backhaul connections to BSs in a cost-effective way is challenging, and it is important to efficiently utilize an existing fixed network infrastructure if possible. This paper proposes two optimized infrastructure-aware planning strategies for small cells and fiber backhaul. The first strategy is referred to as joint design (JD) and is based on the joint cost minimization of small cells and fiber backhaul. The JD strategy is compared to a traditional design (TD) solution based on a two-step optimization approach. In the latter a cost-optimal small cells placement is identified first, then the corresponding minimum cost fiber backhaul deployment is determined accordingly. A comparison between these two approaches in dense 5G urban scenarios shows that the relative performance of JD and TD largely depends on the expected traffic demand and on the existing infrastructure. In dense urban areas with the average traffic levels expected for the year 2020 and beyond, JD returns up to 50% lower deployment cost in a greenfield scenario, and up to 70% lower deployment cost in a brownfield scenario. Only in areas with extremely high traffic demand (e.g., open-air festivals and stadiums) JD returns deployment costs very similar to TD.

With advances in the information and communication technology (ICT), connected vehicles are one of the key enablers to unleash intelligent transportation systems (ITS). On the other hand, the envisioned massive number of connected vehicles raises the need for powerful communication and computation capabilities. As an emerging technique, fog computing is expected to be integrated with existing communication infrastructures, giving rise to a concept of fog-enhanced radio access networks (FeRANs). Such architecture brings computation capabilities closer to vehicular users, thereby reducing communication latency to access services, while making users capable of sharing local environment information for advanced vehicular services. In the FeRANs service migration, where the service is migrated from a source fog node to a target fog node following the vehicle's moving trace, it is necessary for users to access service as close as possible in order to maintain the service continuity and satisfy stringent latency requirements of real-time services. Fog servers, however, need to have sufficient computational resources available to support such migration. Indeed, a fog node typically has limited resources and hence can easily become overloaded when a large number of user requests arrive, e.g., during peak traffic, resulting in degraded performance. This paper addresses resource management in FeRANs with a focus on management strategies at each individual fog node to improve quality of service (QoS), particularly for real-time vehicular services. To this end, the paper proposes two resource management schemes, namely fog resource reservation and fog resource reallocation. In both schemes, real-time vehicular services are prioritized over other services so that their respective vehicular users can access the services with only one hop. Simulation results show that the proposed schemes can effectively improve one-hop access probability for real-time vehicular services implying low delay performance, even when the fog resource is under heavy load.

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.

To efficiently handle the fast growing traffic inside data centers, several optical interconnect architectures have been recently proposed. However, most of them are targeting the aggregation and core tiers of the data center network, while relying on conventional electronic top-of-rack (ToR) switches to connect the servers inside the rack. The electronic ToR switches pose serious limitations on the data center network in terms of high cost and power consumption. To address this problem, we recently proposed a passive optical top-of-rack interconnect architecture, where we focused on the data plane design utilizing simple passive optical components to interconnect the servers within the rack. However, an appropriate control plane tailored for this architecture is needed to be able to analyze the network performance, e.g., packet delay, drop rate, etc., and also obtain a holistic network design for our passive optical top-of-rack interconnect, which we refer to as POTORI. To fill in this gap, this paper proposes the POTORI control plane design which relies on a centralized rack controller to manage the communications inside the rack. To achieve high network performance in POTORI, we also propose a centralized medium access control (MAC) protocol and two dynamic bandwidth allocation (DBA) algorithms, namely Largest First (LF)and Largest First with Void Filling (LFVF). Simulation results show that POTORI achieves packet delays in the order of microseconds and negligible packet loss probability under realistic data center traffic scenarios.

This paper deals with resource management in data centre networks that feature optical interconnects. It first proposes an optical resource management framework as a platform to develop different solutions for multipoint-to-multipoint optical communication systems with a centralized controller. The paper takes optical intra-rack communications as an example application scenario of the framework and studies the intra-rack scheduling (IRS) problem using a theoretical approach. The problem is mapped into the classical open-shop scheduling problem, in which the optical interfaces are viewed as the jobs and the wavelengths used for communication among the interfaces are viewed as the machines. The IRS problem can therefore be solved by adopting the existing preemptive and/or non-preemptive open-shop scheduling algorithms. In a realistic intra-rack communication scenario with non-negligible network reconfiguration time, it is important to understand whether the preemptive or non-preemptive strategy provides better performance under given traffic conditions and system configurations. To address the question, this paper presents a performance analysis that allows to quantitatively compare the two scheduling strategies in terms of packet delay and potential energy savings obtained from sleep mode implementation. Analytical results reveal that the non-preemptive strategy outperforms the preemptive one for typical configurations used in data centre networks. In addition, the tuning and wake-up times of optical transceivers are shown to be key performance-determining factors. The proposed framework and obtained findings are not limited to the considered intra-rack communication scenario, but can be applied to any centralized optical switching systems featuring multipoint-to-multipoint transmissions with non-negligible reconfiguration time.

Each mobile network architecture able to meet the traffic requirements of future 5G services comes with its own set of benefits vs. requirements for the radio and the transport segments. This paper focuses on the energy performance of four mobile network architectures, each one with different splitting options for the baseband processing functions. The radio segment under exam is based on a new radio access technology referred to as 5G-NX, while the transport segment is based on wavelength division multiplexing (WDM). The energy consumption of each mobile network architecture is weighted against (i) the benefits for the radio segment as a function of the level of centralization of the baseband processing resources and (ii) the required transport capacity and consequently the power consumption levels needed to accommodate the backhaul, midhaul, and/or fronthaul traffic generated at each base station. Our results show that a fully centralized radio access network (C-RAN) with centralization of all the baseband functions is not a practical approach for 5G mobile networks, while a partially centralized C-RAN architecture in which the physical layer baseband processing is performed at the base station site represents a promising solution.

The reliance on the loss-of-signal (LOS) of upstream transmissions to indicate fiber/component failure is potentially unsuitable in networks that implement sleep/doze mode operation. In such networks, the transition into sleep/doze mode would result in no signal transmission, and when used in conjunction with conventional LOS to indicate network failure, would result in erroneous triggering of false alarm and subsequently protection switching. Recently, converged access networks using a hybrid passive optical architecture, have been favored as a low-cost and high-bandwidth solution to deliver high-bandwidth applications to both fixed access and mobile users. These networks are referred to as Hybrid PON Converged Access Networks. Protection against fiber/equipment failures in these networks is critical considering the customer base, network span, and traffic supported. This paper proposes four survivable architectures for such converged access networks. These architectures combine rapid fault detection and protection switching against high impact failures but without the need to rely on upstream transmissions for LOS detection. A comparison of the four architectures across three different area densities under three deployment scenarios, is presented. Guidance for selecting the best protection architecture to be deployed, considering area densities and deployment scenarios, is provided.

Mobile networks are the largest contributor to the carbon footprint of the telecom sector and their contribution is expected to rapidly increase in the future due to the foreseen traffic growth. Therefore, there is an increasing urgency in the definition of green mobile network deployment strategies. This paper proposes a four-step design and power assessment methodology for mobile networks, taking into consideration both radio and transport segments. A number of mobile network deployment architectures for urban residential areas based on different radio (i.e., macro base station, distributed indoor radio, femto cell) and transport (i.e., microwave, copper, optical fiber) technologies are proposed and evaluated to identify the most energy efficient solution. The results show that with low traffic the conventional macro base station deployment with microwave based backhaul is the best option. However, with higher traffic values heterogeneous networks with macro base stations and indoor small cells are more energy efficient. The best small cell solution highly depends on the transport network architecture. In particular, our results show that a femto cell based deployment with optical fiber backhaul is the most energy efficient, even if a distributed indoor radio architecture (DRA) deployment with fiber fronthaul is also a competitive approach.

This paper targets the lifetime-aware management of a set of optical line amplifiers (OLAs) in an optical network exploiting sleep mode (SM) in order to save energy. We first present a simple model to predict the OLA lifetime. We then provide different mixed-integer linear programming (MILP) formulations, which jointly consider energy saving and lifetime. The proposed MILP formulations are then solved on different realistic scenarios, by taking into account the spatial and temporal variations of traffic demands. Results show that our lifetime-aware approach outperforms classical energy saving ILP formulations, which instead tend to notably decrease the OLA lifetime. More important, the proposed approaches can achieve a good lifetime performance without consuming significantly more energy than purely energy-aware strategies.

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.

Mobile operators are facing an exponential traffic growth due to the proliferation of portable devices that require a high-capacity connectivity. This, in turn, leads to a tremendous increase of the energy consumption of wireless access networks. A promising solution to this problem is the concept of heterogeneous networks, which is based on the dense deployment of low-cost and low-power base stations, in addition to the traditional macro cells. However, in such a scenario the energy consumed by the backhaul, which aggregates the traffic from each base station towards the metro/core segment, becomes significant and may limit the advantages of heterogeneous network deployments. This paper aims at assessing the impact of backhaul on the energy consumption of wireless access networks, taking into consideration different data traffic requirements (i.e., from todays to 2020 traffic levels). Three backhaul architectures combining different technologies (i.e., copper, fiber, and microwave) are considered. Results show that backhaul can amount to up to 50% of the power consumption of a wireless access network. On the other hand, hybrid backhaul architectures that combines fiber and microwave performs relatively well in scenarios where the wireless network is characterized by a high small-base-stations penetration rate.

The articles in this special issue focus on energy efficiency techniques deployed in optical fiber networking.

ABSTRACT Transparent optical networks can be highly vulnerable to various physical‐layer attacks, such as high‐power jamming, which can cause widespread service disruption and even service denial. The propagation of such attacks can be thwarted with wavelength‐selective attenuators, referred to as power equalizers, installed at the network nodes. However, employing all nodes with power equalization functionality can lead to substantial costs. In previous work, we proposed a heuristic approach for sparse power equalization placement to limit jamming attack propagation cost‐effectively. The approach provides suboptimal solutions quickly; however, it does not guarantee optimality. Because placement of such power equalization is a long‐term planning problem affecting the capital expenditures of the network operator, solution quality is more critical than execution time. Thus, in this paper, we propose an integer linear programming formulation for the problem to guarantee optimality in terms of the number of power equalizers placed. Evaluation results show that our proposed integer linear programming formulation is able to solve moderately sized problems in reasonable time. These results also support the efficiency of our previously proposed heuristic by confirming its ability to find optimal solutions for the cases tested. Copyright © 2014 John Wiley & Sons, Ltd.

Energy consumption of communication networks is growing very fast due to the rapidly increasing traffic demand. Consequently, design of green communication networks gained a lot of attention. In this paper we focus on optical Wavelength Division Multiplexing (WDM) networks, able to support this growing traffic demand. Several energy-aware routing and wavelength assignment (EA-RWA) techniques have been proposed for WDM networks in order to minimize their operational cost. These techniques aim at minimizing the number of active links by packing the traffic as much as possible, thus avoiding the use of lightly loaded links. As a result, EA-RWA techniques may lead to longer routes and to a high utilization on some specific links. This has a detrimental effect on the signal quality of the optical connections, i.e., lightpaths. In this study we quantify the impact of power consumption minimization on the optical signal quality. and address this problem by proposing a combined impairment and energy-aware RWA (IEA-RWA) approach. Towards this goal we developed a complete mathematical model that incorporates both linear and non-linear physical impairments together with an energy efficiency objective. The IEA-RWA problem is formulized as a Mixed Integer Linear Programming (MILP) model where both energy efficiency and signal quality considerations are jointly optimized. By comparing the proposed IEA-RWA approach with existing RWA (IA-RWA and EA-RWA) schemes, we demonstrate that our solution allows for a reduction of energy consumption close to the one obtained by EA-RWA approaches, while still guaranteeing a sufficient level of the optical signal quality.

In recent years, energy-efficient design of optical WDM networks has become increasingly important as efforts to reduce the operational expenditure (Opex) and the carbon footprint of the internet are prioritized. In this paper we focus on energy- efficient survivable network design where backup resources are shared for efficient capacity consumption. However there is a trade-off between energy-efficiency and survivability. Survivable network design strategies lead to lightly loaded links in order to minimize the risk in case of a failure and to increase the shareability of backup resources. On the contrary, energy-efficient network design strategies tend to increase the load in a set of links as a consequence of concentrating the traffic in order to be able to switch off as much network resources as possible. In this study, we present a novel method to simultaneously minimize Capex and Opex while providing an energy-efficient, shared backup protected network, under the assumption of backup capacity in sleep mode. For the first time we propose an ILP formulation for the energy-efficient shared backup protection problem. By exploiting the sleep mode for the backup resources, we observe that the ILP solution of our mathematical model brings up to 40% gain in energy efficiency in comparison to energy-unaware shared backup protection approach.

Energy-efficient optical networks are gaining momentum as environmental-friendly solutions with reduced operational costs. Energy-efficiency can be achieved by using devices in sleep mode, i.e., a low-power, inactive state in which devices can be suddenly waken-up upon occurrence of triggering events. This paper advocates a sleep mode option for the optical devices (e.g., amplifiers, optical switches) installed for protection purposes only. These devices can be put in sleep mode to reduce the network power consumption, but they can be promptly waken up (if necessary) upon a failure occurrence. This principle is proposed and applied in Wavelength Division Multiplexing (WDM) networks with dedicated-path protection to ensure survivability against single-link failures. The main contribution of the paper is the definition of the energy-efficient network planning problem for resilient WDM networks where optical devices can be configured in sleep mode. Optimal results of the integer linear programming (ILP) problem show savings of up to 25% in the overall power consumption.

Security issues and attack management in transparent wavelength division multiplexing (WDM) optical networks have become of prime importance to network operators due to the high data rates involved and the vulnerabilities associated with transparency. Deliberate physical-layer attacks, such as high-powered jamming, can seriously degrade network performance and must be dealt with efficiently. While most approaches are focused on the developing fast detection and reaction mechanisms triggered in case of an attack, we propose a novel approach to help deal with these issues in the network planning and provisioning process as a prevention mechanism. Namely, we propose to route lightpaths in such a way as to minimize the potential damage caused by various physical-layer attacks. We present a new objective criterion for the routing and wavelength assignment (RWA) problem, which we call the maximum Lightpath Attack Radius(maxLAR), and formulate the routing subproblem as an integer linear program (ILP). We test it on small networks to get an insight into its complexity and compare it to a formulation that minimizes congestion. Results indicate that our formulation achieves significantly better results for the maxLAR while obtaining near-optimal or optimal congestion in all cases. For larger networks, we propose a tabu search algorithm for attack-aware lightpath routing, in combination with an existing graph-coloring algorithm for wavelength assignment. Testing and comparing with existing approaches from literature indicate its superiority with respect to themaxLARand average lightpath load, albeit at the expense of somewhat higher congestion. However, this is justified with the obtained improvement in network security.

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.

Reliability performances of seven equal-capacity optical asynchronous transfer mode switches based on different routing and buffering schemes are compared. Calculations are based on available reliability data for commercial components as well as expected life lengths for new components and systems. In general, for switching systems, the average downtime is required to be held below 3 min/year. However, our results indicate that the average downtime for the proposed configurations is much longer than requested, over ten times longer for the best case and over 1500 times longer for the worst case. Therefore, the multiplane structures (i.e., multiplying of the entire systems) have to be provided for the switching systems investigated here in order to satisfy reliability requirements.