Elastic Optical Networks Research Articles

Modulation-adaptive resource allocation integrating ML-based routing and QoT estimation for elastic optical network planning

We propose a pointer network-based joint routing, modulation format, and spectrum allocation (PtrNet-RMSA) scheme to optimize the network capacity in elastic optical networks (EONs). Specifically, for a service request, EON spectra are first split into multiple spectrum window planes (SWPs), regarding various numbers of contained frequency slots (FSs). Then, over each SWP, a lightpath with an optimal estimated quality of transmission (QoT) is generated using a PtrNet-based routing agent. Further, different modulation formats are verified for the generated lightpath over the considered SWP, with respect to a calculated QoT threshold, to check whether a specific preforward error correction (FEC) requirement can be satisfied. Finally, two heuristic approaches, first fit and highest fit, are applied in the joint RMSA to select an optimal lightpath of an SWP and modulation format. To evaluate the efficiency of the PtrNet-RMSA scheme, extensive simulations were conducted in EONs with dynamic traffic. Results show that the proposed PtrNet-RMSA scheme can significantly improve the network capacity with various network topologies, compared with two benchmarks.

Retraction Note: Elastic optical network routing analysis using intelligent machine learning architecture with security analysis

Retraction Note: Elastic optical network routing analysis using intelligent machine learning architecture with security analysis

Ultra-high-capacity band and space division multiplexing backbone EONs: multi-core versus multi-fiber

Both multi-band and space division multiplexing (SDM) independently represent cost-effective approaches for next-generation optical backbone networks, particularly as data exchange between core data centers reaches the petabit-per-second scale. This paper focuses on different strategies for implementing band and SDM elastic optical network (BSDM EON) technology and analyzes the total network capacity of three sizes of backbone metro-core networks: ultra-long-, long-, and medium-distance networks related to the United States, Japan, and Spain, respectively. Two BSDM strategies are considered, namely, multi-core fibers (MCFs) and BSDM based on standard single-mode fiber (SSMF) bundles of multi-fiber pairs (BuMFPs). For MCF-based BSDM, we evaluated the performance of four manufactured trench-assisted weakly coupled (TAWC) MCFs with 4, 7, 13, and 19 cores. Simulation results reveal that, in the regime of ultra-low (UL) loss and inter-core crosstalk (ICXT), MCF-based throughput can be up to 14% higher than SSMF BuMFP-based BSDM when the core pitch exceeds 43 µm and the loss coefficient is lower than that of standard single-mode fibers. However, increasing the number of cores with (non-)standard cladding diameters, UL loss, and ICXT coefficient is not beneficial. As core counts increase up to 13 for non-standard cladding diameters (<230µm), the core pitch and loss coefficient also increase, leading to degraded performance of MCF-based BSDM compared to SSMF BuMFP-based BSDM. The results indicate that, in scenarios with 19 MFPs, SSFM BuMFP-based BSDM outperforms 19-core MCF-based scenarios, increasing the throughput by 55% to 73%, from medium-backbone networks to ultra-long ones.

Minimizing unavailability in Elastic Optical Networks: Pre-provisioning and provisioning protection strategy using DLP and DPP

The rapid growth of internet traffic, driven by advancements in 5G and cloud services, has intensified the need for reliable optical networks. Infrastructure failures in these networks can lead to significant disruptions and financial losses, highlighting the critical importance of robust protection mechanisms. While existing research has explored various protection strategies based on on-demand provisioning of resources, a gap remains in understanding the effectiveness of pre-provisioning mechanisms specifically tailored for Elastic Optical Networks (EONs). Pre-provisioning is a strategy that involves reserving network resources, such as bandwidth or lightpaths, in advance of anticipated traffic demands. This proactive approach ensures that resources are readily available when needed, reducing setup times and enhancing network resilience. In this paper we propose a novel algorithm, BPreProvEON, designed to optimize resource allocation and enhance the survivability of EONs through a hybrid combination of pre-provisioning and on-demand provisioning of protection schemes. Extensive comparisons with traditional non-pre-provisioning and Shared Path Protection (SPP)-based pre-provisioning demonstrate BPreProvEON’s superior performance in bandwidth blocking rate and connection availability, highlighting its potential for enhancing the resilience and performance of optical networks.

Layout design of densest weakly coupled multi-core fibers to minimize the network blocking rate

The suppression of inter-core crosstalk (IC-XT) that affects each lightpath is crucial for resource allocation in space-division multiplexing elastic optical networks (SDM-EONs) with multi-core fibers (MCFs). Resource allocation approaches that limit the simultaneous use of adjacent cores in the same frequency band to the MCFs composing each lightpath have been widely adopted to suppress IC-XT. However, in principle, such methods are inefficient because they cannot fully utilize all cores. This study examines the core density from the perspective of the core layout in weakly coupled MCFs and the IC-XT suppression requirement. The densest MCF layout maximizes the network capacity while restricting the amount of IC-XT within the tolerance threshold for each lightpath. Specifically, we propose an XT-free condition, maintaining the IC-XT to each lightpath within the acceptable tolerance level. In addition, we evaluated numerous MCFs that satisfy or do not satisfy the XT-free condition with various network topologies and cladding diameters. This evaluation also validates the IC-XT reduction performance of the proposed framework compared with that of the conventional resource-allocation approach. Here, we incorporate our indirect IC-XT calculation method that affects lightpaths from other cores via its nearest cores, which was overlooked in the resource allocation problem. Based on these comprehensive examinations, we propose a method to determine the densest core layout for a given network topology and route and modulation format selection algorithm.

Routing Algorithms for SDM Flexible Optical Networks.

This paper considers the online routing, modulation, spectrum, and core allocation problem in elastic optical networks. Three algorithms are proposed to minimize the bandwidth blocking probability while taking into account the impact of interference from established lightpaths. These algorithms are based on a sequence of alternative paths, ordered in terms of increasing lengths, determined by Yen's algorithm. Each of the algorithms for the selected single or dual lightpath enforces the constraints of spectrum continuity, contiguity of the slots, and non-overlapping of the spectra with the established lightpaths, maximizes the number of bits per symbol, and selects a specific core on each link of the path. Simulation studies performed for two sample networks showed that the crosstalk has a significant impact on the bandwidth blocking probability. The best results were obtained by iterating over all paths and all slots, selecting the lightpath with the lowest average crosstalk per slot, consisting of the most loaded cores.

Novel Application of Quantum Computing for Routing and Spectrum Assignment in Flexi-Grid Optical Networks

Flexi-grid technology has revolutionized optical networking by enabling Elastic Optical Networks (EONs) that offer greater flexibility and dynamism compared to traditional fixed-grid systems. As data traffic continues to grow exponentially, the need for efficient and scalable solutions to the routing and spectrum assignment (RSA) problem in EONs becomes increasingly critical. The RSA problem, being NP-Hard, requires solutions that can simultaneously address both spatial routing and spectrum allocation. This paper proposes a novel quantum-based approach to solving the RSA problem. By formulating the problem as a Quadratic Unconstrained Binary Optimization (QUBO) model, we employ the Quantum Approximate Optimization Algorithm (QAOA) to effectively solve it. Our approach is specifically designed to minimize end-to-end delay while satisfying the continuity and contiguity constraints of frequency slots. Simulations conducted using the Qiskit framework and IBM-QASM simulator validate the effectiveness of our method. We applied the QAOA-based RSA approach to small network topology, where the number of nodes and frequency slots was constrained by the limited qubit count on current quantum simulator. In this small network, the algorithm successfully converged to an optimal solution in less than 30 iterations, with a total runtime of approximately 10.7 s with an accuracy of 78.8%. Additionally, we conducted a comparative analysis between QAOA, integer linear programming, and deep reinforcement learning methods to evaluate the performance of the quantum-based approach relative to classical techniques. This work lays the foundation for future exploration of quantum computing in solving large-scale RSA problems in EONs, with the prospect of achieving quantum advantage as quantum technology continues to advance.

Explainable artificial intelligence-based framework for efficient content placement in elastic optical networks

The rapid development of telecommunication networks brings new optimization problems and the urgent need for dedicated and highly efficient solution methods. Recently, the idea of aiding network optimization with machine learning (ml) algorithms has gained more and more attention in the research society. Despite numerous successful applications of these methods, their adaption in real networks and systems is hindered due to the lack of a full explainability of their decisions and, in turn — the lack of trust. Hopefully, these aspects may be addressed by explainable artificial intelligence methods (xai). In this paper, we study an essential problem of the anycast content placement. Having a set of physical data centers (dcs) located in selected network nodes and a set of different contents (services), the task is to decide in which dcs place each of the contents in order to improve the optical network performance (measured as a bandwidth blocking probability (bbp)). To this end, we propose a dedicated ml-based framework, which approaches the placement problem as a supervised learning task of predicting network’s bbp for a content placement configuration. We perform extensive numerical experiments to tune the framework, considering five supervised learning algorithms and three comparison metrics. We also use explainable artificial intelligence methods to interpret the models’ decisions and draw general conclusions regarding beneficial content placement in a real network. Lastly, we compare the performance of the proposed ml-based placement framework with three reference methods. The results prove our approach’s extremely high efficiency, which reduced the bbp significantly compared to the best reference approach. Depending on the network settings and the offered traffic volume, the framework allowed to serve up to 47% of the traffic that would be rejected by the best reference method (corresponding to 3.76 Tbps of data).

Time-Efficient RSA over Large-Scale Multi-Domain EON.

The poor timeliness of routing has always been an urgent problem in practical operator networks, especially in situations with large-scale networks and multiple network domains. In this article, a pruning idea of routing integrated with Dijkstra's shortest path searching is utilized to accelerate the process of routing in large-scale multi-domain elastic optical networks (EONs). The layered-graph approach is adopted in the spectrum allocation stage. To this end, an efficient heuristic algorithm is proposed, called "Branch-and-Bound based Routing and Layered Graph based Spectrum Allocation algorithm (BBR-LGSA)", which is an integrated RSA algorithm. Notably, the significant reduction in algorithm time complexity is not only reflected in the pruning method used in the routing stage but also in the construction of auxiliary graphs during the spectrum allocation stage utilizing the Branch-and-Bound method. Simulation results show that the proposed BBR-LGSA significantly reduces the average running time by nearly 78% with higher spectrum utilization in large-scale multi-domain EONs, compared with benchmark algorithms. In addition, the impact of key parameters on performance comparisons of different algorithms is evaluated.

Resource learning aware algorithm for route prediction and resources allocation in elastic optical networks with network analytics

We have proposed two algorithms in this paper for dynamic traffic in elastic optical networks (EON). Algorithm1 considers the spatial access blocking probability metric (SABPM) during dynamic operation and route traffic to a path with lower SABPM. Therefore, it considers a path with more contiguous blocks to support heterogeneous bandwidths (BW) and modulation schemes. Algorithm1 keeps a balance of fragmentation on available routes. Algorithm2 makes routing decision for dynamic traffic based on the maximum idle slots as well as minimum SABPM. Therefore, it selects a less fragmented path with more number of available contiguous blocks to support heterogeneous BW. Algorithm2 balances the path utilization and avoids over utilization of congested route. We have evaluated the performance of both algorithms in terms of spatial external fragmentation, contiguity ratio, contiguous aligned spectrum ratio, SABPM, and BW blocking probability (BwBP). Python with seaborn, pandas, and other libraries are used for network analytics. Results show that resource utilization improves with the proposed algorithms. It has been shown that algorithm2 and algorithm1 achieve 27% and 18% traffic gains respectively over the alternate routing.

Probabilistic Path Computation and Frequency Assignment to Mitigate Spectral Fragmentation in Elastic Optical Networks

Probabilistic Path Computation and Frequency Assignment to Mitigate Spectral Fragmentation in Elastic Optical Networks

Benchmarking framework for resource allocation algorithms in multicore fiber elastic optical networks

The lack of standards in the performance evaluation of new resource allocation algorithms in multicore fiber elastic optical networks (MCF-EONs) compromises the fairness when comparing them with the state of the art. This paper reviews the different transmission parameters, network parameters, performance metrics, and baselines used by the recent proposals to build a framework for future benchmarking of such algorithms according to the nature of the network operation, whether static or dynamic. This framework aims to provide standards regarding evaluation criteria, scenarios, and performance metrics, as well as recommendations concerning technology advances to promote methodology and reproducibility in further related studies.

Transfer learning-assisted modulation classification and OSNR estimation using constellation diagrams

Abstract A cost-effective deep transfer learning (TL) learned with characteristics derived from the constellation diagram is proposed for executing modulation classification and optical signal-to-noise ratio (OSNR) estimation for the future generation of elastic optical networks. The information acquired with the ImageNet dataset is transferred using pre-trained TL versions including VGG19, ResNet152V2, InceptionV3, and ResNet50 to identify various modulation formats and their corresponding OSNR. Adam optimizer is employed to ascertain the appropriate settings for the hyperparameters. Numerical results demonstrate that the proposed VGG19 method achieves the highest level of accuracy (100%) in the recognition of various modulation forms. To fulfil the requirements of actual use, OSNR monitoring is also explored, with a total precision equal to 95.2%. Furthermore, a thorough investigation of the impact of training data dimensions, on TL performance is conducted. The outcomes of the proposed method demonstrate that the suggested TL-based algorithms are more accurate and need much less training and testing time than non-TL approaches.

Retraction Note: Deep reinforcement learning for comprehensive route optimization in elastic optical networks using generative strategies

Retraction Note: Deep reinforcement learning for comprehensive route optimization in elastic optical networks using generative strategies

Joint resource allocation and power optimization in space division multiplexed elastic optical networks: a new algorithm and comparative analysis

Joint resource allocation and power optimization in space division multiplexed elastic optical networks: a new algorithm and comparative analysis

A RMSCA algorithm for space division multiplexing elastic optical networks with core switching

Space division multiplexing elastic optical networks (SDM-EONs) have attracted much attention due to the advantages of high capacity, flexibility, and spectrum utilization. Space division multiplexing technology enhances the capacity of a single fiber link by using multi-core fiber (MCF). With the support of MCFs, SDM-EONs will become an important form of future optical transmission networks.In order to reduce the blocking rate of the network, this paper proposes a routing, modulation format, spectrum and core allocation (RMSCA) algorithm based on the maximum number of available cores with core switching. The algorithm selects the spectrum block with the most available cores in the entire path for spectrum allocation by using core switching. We perform simulations on two networks, NSF-Net and UBN24, and compare them with the spectrum allocation algorithm with core switching (FF-ISC-RMSCA), which uses first-time adaptation, and the spectrum allocation algorithm with maximum number of cores available (N-ISC-RMSCA), which does not perform core switching. Simulation results show that the proposed algorithm significantly reduces the bandwidth blocking rate and has high spectrum utilization.

Modulation Format Recognition Scheme Based on Discriminant Network in Coherent Optical Communication System

In this paper, we skillfully utilize the discriminative ability of the discriminator to construct a conditional generative adversarial network, and propose a scheme that uses few symbols to achieve high accuracy recognition of modulation formats under low signal-to-noise ratio conditions in coherent optical communication. In the one thousand kilometres G.654E optical fiber transmission system, transmission experiments are conducted on the PDM-QPSK/-8PSK/-16QAM/-32QAM/-64QAM modulation format at 8G/16G/32G baud rates, and the signal-to-noise ratio parameters are traversed under experimental conditions. As a key technology in the next-generation elastic optical networks, the modulation format recognition scheme proposed in this paper achieves 100% recognition of the above five modulation formats without distinguishing signal transmission rates. The optical signal-to-noise ratio thresholds required to achieve 100% recognition accuracy are 12.4 dB, 14.3 dB, 15.4 dB, 16.2 dB, and 17.3 dB, respectively.

Machine-learning-based impairment-aware dynamic RMSCA in multi-core elastic optical networks

This paper presents a routing, modulation, spectrum, and core assignment (RMSCA) algorithm for space-division-multiplexing-based elastic optical networks (SDM-EONs) comprising multi-core links. A network state-dependent route and core selection method is proposed using a deep neural network (DNN) classifier. The DNN is trained using a metaheuristic optimization algorithm to predict lightpath suitability, considering the quality of transmission and resource availability. Physical layer impairments, including inter-core crosstalk, amplified spontaneous emission, and Kerr fiber nonlinearities, are considered, and a random forest (RF)-based link noise estimator is proposed. A feature importance selection analysis is provided for all the features considered for the DNN classifier and the RF link noise estimator. The proposed machine-learning-enabled RMSCA approach is evaluated on three network topologies, USNET, NSFNET, and COST-239 with 7-core and 12-core fiber links. It is shown to be superior in terms of blocking probability, bandwidth blocking probability, and acceptable computational speed compared to the standard and published benchmarks at different traffic loads.

CLARA+: dual machine learning optimized resource assignment for translucent SDM-EONs

Space division multiplexed elastic optical networks (SDM-EONs) enhance service provisioning by offering increased fiber capacity through the use of flexible spectrum allocation, multiple spatial modes, and efficient modulations. In these networks, the problem of allocating resources for connections involves assigning routes, modulations, cores, and spectrum (RMCSA). However, the presence of intercore crosstalk (XT) between ongoing connections on adjacent cores can degrade signal transmission, necessitating proper handling during resource assignment. The use of multiple modulations in translucent optical networks presents a challenge in balancing spectrum utilization and XT accumulation. In this paper, we propose a dual-optimized RMCSA algorithm called the Capacity Loss Aware Resource Assignment Algorithm (CLARA+), which optimizes network capacity utilization to improve resource availability and network performance. A two-step machine-learning-enabled optimization is used to improve the resource allocations by balancing the tradeoff between spectrum utilization and XT accumulation with the help of feature extraction from the network. Extensive simulations demonstrate that CLARA+ significantly reduces bandwidth blocking probability and enhances resource utilization across various scenarios. We show that our strategy applied to a few algorithms from the literature improves the bandwidth blocking probability by up to three orders of magnitude. The algorithm effectively balances spectrum utilization and XT accumulation more efficiently compared to existing algorithms in the literature.

Physical layer-aware circuit reallocation to eons using make-before-break and push-pull techniques

This paper proposes a new circuit reallocation algorithm with make-before-break and push-pull techniques that considers the effects of physical layers in transparent elastic optical networks called Just One Circuit Reallocation (JOC). The JOC algorithm reallocates just one already established circuit to avoid blocking new circuit requests due to impairments in the physical layer. The results of the JOC algorithm were compared to three other algorithms: Circuit Reallocation Strategy - Physical Layer (CRS-PL), Circuit Reallocation for Block Reduction related to the QoT of the circuits (R-RQoT), and Make-Before- Break based defragmentation algorithm (MBBr). The reallocation algorithms are evaluated under the Bandwidth Blocking Probability (BBP), Circuit Blocking Probability (CBP), and the Number of reallocated circuits (N), external fragmentation, and percerntual of circuit by modulation for USA and EON topologies. Moreover, we also evaluated the performance of reallocation algorithms using complete sharing, K-shortest path computation, modified Dijkstra paths computation, and K-shortest path with reduction of QoTO to routing and spectrum assignment. Simulation results show that the proposed algorithm performs better than the CRS-PL, R-RQoT and MBBr algorithms regarding BBP, PBC, and N. Regarding BBP our algorithm presented minimum reductions of approximately 65.36% e 55.6% for the USA and EON topology, respectively.