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Space-division multiplexing elastic optical networks (SDM-EONs) with multi-core fibers (MCFs) are expected to meet the exponentially increasing demand for traffic. However, it is inevitable that inter-core crosstalk (IC-XT) will hinder efforts to increase capacity. Thus, attempts have been made to leverage SDM-EONs by solving the route, modulation format, core, and spectrum assignment problems, but the effects of IC-XT still need to be mitigated. Existing methods such as XT-Aware first fit and core prioritization methods are insufficient for addressing this issue. In this paper, we propose generalized hierarchical core prioritization for the graceful degradation of IC-XT in core and spectrum allocation. Simulation results establish that our approach mitigates adverse influences from IC-XT and has high flexibility in network environments. This study shows that the dynamic resource allocation method, despite being heuristic and computationally inexpensive, can sufficiently reduce the blocking probabilities in any SDM-EONs with hexagonal MCFs.

Optical network failure management (ONFM) is a promising application of machine learning (ML) to optical networking. Typical ML-based ONFM approaches exploit historical monitored data, retrieved in a specific domain (e.g., a link or a network), to train supervised ML models and learn failure characteristics (a signature) that will be helpful upon future failure occurrence in that domain. Unfortunately, in operational networks, data availability often constitutes a practical limitation to the deployment of ML-based ONFM solutions, due to scarce availability of labeled data comprehensively modeling all possible failure types. One could purposely inject failures to collect training data, but this is time consuming and not desirable by operators. A possible solution is transfer learning (TL), i.e., training ML models on a source domain (SD), e.g., a laboratory testbed, and then deploying trained models on a target domain (TD), e.g., an operator network, possibly fine-tuning the learned models by re-training with few TD data. Moreover, in those cases when TL re-training is not successful (e.g., due to the intrinsic difference in SD and TD), another solution is domain adaptation, which consists of combining unlabeled SD and TD data before model training. We investigate domain adaptation and TL for failure detection and failure-cause identification across different lightpaths leveraging real optical SNR data. We find that for the considered scenarios, up to 20% points of accuracy increase can be obtained with domain adaptation for failure detection, while for failure-cause identification, only combining domain adaptation with model re-training provides significant benefit, reaching 4%–5% points of accuracy increase in the considered cases.

Elastic optical networks (EONs) and spacedivision multiplexing (SDM) are promising technologies for future core optical networks with high transmission capacity. Traditional optical node architectures for SDM-EONs have two important problems: flexibility and power consumption. Architecture on demand (AoD) is a new concept for an optical node architecture, and an optical node based on this concept is called an AoD node. AoD nodes can be dynamically constructed according to the traffic request by interconnecting input/output ports and building modules with optical switches. Although AoD nodes have great flexibility, power consumption is still a serious problem for them. In this paper, we propose an energy-efficient network system that includes a novel energy-efficient AoD node architecture and resource assignment method cooperating with this node architecture. The proposed system solves the power-consumption problem by simplifying the implemented building modules based on spatial multiplicity of SDM-EONs. The proposed on-demand routing and spectrum allocation method efficiently satisfies the restricted spectrum arrangement that is required by energy-efficient AoD nodes. Finally, we evaluate the proposed system of node architecture and resource allocation method through computer simulations. The simulations show that the proposed network system can improve both the power consumption and the blocking probability of path setup requests in the entire network.

Numerous studies have investigated elastic optical networks (EONs) with the aim of expanding the transmission capacities of core networks. To achieve this goal, it is necessary to solve the spectrum resource wastage problem caused by spectrum fragmentation. Moreover, due to the potentially high traffic demands in future networks, it is important to handle requests that need to be reserved immediately (immediate reservation, IR) as well as those that can be reserved in advance (advance reservation, AR). In networks that support the coexistence of IR and AR requests, IR service degradation by AR requests is a challenging issue because AR requests tend to reserve future resources, which causes a lack of current resources to meet IR requests. Therefore, we address the problem of spectrum fragmentation and the service-level control of IR and AR requests by routing and spectrum allocation (RSA). First, we summarize related research into EONs and resource- allocation methods for IR and AR requests. Next, we propose a novel dynamic RSA method to reduce spectrum fragmentation and control the service level of IR and AR requests in terms of bandwidth blocking probability (BBP) in EONs considering the multiplexing effect of spatial channels. Finally, we evaluate the proposed method based on computer simulations and our results demonstrate that the proposed method can improve the BBP for the entire traffic flow by reducing spectrum fragmentation, as well as the service control of AR requests and IR requests under various network conditions.

Elastic optical networks (EONs) are considered to be one of the promising future networks for spectrum flexibility. In conventional wavelength-division multiplexing networks, routing and wavelength assignment is one of the key issues, whereas the routing and spectrum assignment (RSA) problem considerably affects the network performance in EONs. In addition, the data-center traffic and mobile back-haul traffic keeps increasing. To deal with such increasing capacity of applications, space-division multiplexing (SDM) technologies such as multi-core fiber (MCF) and multi-mode fiber (MMF) have been intensively researched. From the network perspective, this paper focuses on the routing, spectrum, and core and/or mode assignment (RSCMA) problem for future SDM-EONs. Introducing MCF or MMF further complicates the RSA problem because the fiber core or mode dimension is newly expanded. In addition, physical impairment caused by MCF or MMF must be considered. In this paper, the target RSCMA problem is first divided into routing and SCMA problems, and a pre-computation method based on the K-shortest path is introduced as the routing solution. Next, we propose SCMA methods with efficiency and flexibility awareness, exploiting prioritized area concept and crosstalk awareness depending on whether MCF or MMF supports intercore/intermode crosstalk. Finally, the paper evaluates and compares the effectiveness of the proposed algorithms with that of representative algorithms.

In the past few years, many researchers have studied elastic optical networks, which exhibit a dramatically improved transmission capacity compared with conventional optical networks. However, the transmission capacity per fiber will soon reach the physical limit for traditional single-mode fibers. Multicore fiber (MCF) is among the innovative fibers based on space-division multiplexing technology. MCF has multiple cores and achieves a far larger transmission capacity than traditional single-mode fibers. However, signals transmitted in these crowded multiple cores interfere with each other and are degraded. This degradation has a serious impact on the network resource management in elastic optical networks. Previous research indicates that the crosstalk effect of MCF is dependent on an arrangement of signals regarding the spectrum and core. Therefore, we approach the problem of the crosstalk in MCF with regard to the spectrum and core allocation, from the network perspective. First, this paper summarizes the related work regarding elastic optical networks and MCFs. Next, we propose an “on-demand” spectrum and core allocation method that reduces both the crosstalk and fragmentation in elastic optical networks with MCFs. This proposed method is based on two predefined policies related to the crosstalk and fragmentation. The first, the core prioritization policy, is based on the MCF's structure, and the other is a core classification policy based on the required bandwidth of the connections. The core prioritization policy realizes the core allocation that reduces crosstalk by avoiding filling adjacent cores. The core classification policy reduces the spectrum fragmentation by allocating a uniform bandwidth connection for each core. Finally, we evaluate the proposed method using computer simulations. The results indicate that the proposed method can, under various network conditions, improve both the crosstalk and blocking probability of the total network through our two policies.