Routing And Wavelength Assignment Research Articles

Routing and Wavelength Assignment for Folded Hypercube in Linear Array WDM Optical Networks

The folded hypercube is one of the hypercube variants and is of great significance in the study of interconnection networks. In a folded hypercube, information can be broadcast using efficient distributed algorithms. In the context of parallel computing, folded hypercube has been studied as a possible network topology as an alternative to the hypercube. The routing and wavelength assignment (RWA) problem is significant, since it improves the performance of wavelength-routed all-optical networks constructed using wavelength division multiplexing approach. Given the physical network topology, the aim of the RWA problem is to establish routes for the connection requests and assign the fewest possible wavelengths in accordance with the wavelength continuity and distinct wavelength constraints. This paper discusses the RWA problem in a linear array for the folded hypercube communication pattern by using the congestion technique.

Cost minimization of multi-class demands groomed over multi-rate OTN interfaces considering WDM-layer constraints

This paper introduces a practical comprehensive approach to optimize the cost of installed Optical Transport Network (OTN) interfaces, as well as the required number of wavelengths in the substrate Wavelength Division Multiplexing (WDM) optical network. The approach assumes grooming variable-bit-rate traffic demands on multi-rate OTN interfaces. We propose an Integer Linear Programming (ILP) formulation to address the combined problem of OTN interface deployment, virtual-network formation, traffic grooming on the established distinct-capacity virtual paths, and virtual network embedding on the WDM physical substrate. The presented model focuses on mitigating the capital expenditure required to deploy OTN interfaces, usually the most costly element in the network, as well as minimizing the required number of wavelengths in the Routing and Wavelength Assignment (RWA) problem, contributing to the overall reduction in network costs and ensuring open capacity for future expansions. As the RWA problem is NP-hard and involves significant computational costs, we also propose a multi-step heuristic approach to tackle larger network instances more efficiently. In this approach, the optimization steps for the OTN design and the RWA solutions are conducted independently. For the OTN-layer planning, the optimum ILP formulation (OTN-ILP) is maintained, so that the optimum solution for the cost of installed interfaces on the OTN layer is guaranteed. The RWA problem is proposed through either an ILP formulation (RWA-ILP) or a heuristic approach. The RWA-ILP model can find the same or slightly superior number of wavelengths compared to the optimum solution of the Integrated ILP formulation, but it can provide results in reasonable times only for small networks. On the other hand, the heuristic RWA is able to run any network size, but it requires more wavelengths compared to the RWA-ILP model.

Fragmentation aware heuristic algorithm for routing and wavelength assignment in optical networks

Wavelength division multiplexing (WDM) is one of the dominating technologies with high-capacity back bone networks. The cost associated with the high-capacity networks given more importance. The major issue is allocating and managing the available resources. To achieve this most efficient algorithms has to be used. We are considering the routing of lightpath and wavelength assignment problem, called as the routing and wavelength assignment (RWA) problem. The optimization of wavelength fragmentation in the WDM network is very much important in resource utilization. Wavelength fragmentation is one of the most important challenges in the area of the WDM network. Where it leads to some serious issues for the operators, such as the rejection of new requests. We are using integer linear program (ILP), here the problem is based on the node link formation. It is based on the multilayer concept and the original WDM network consists of several layers. We propose an efficient heuristic approach to solve this problem of finding the shortest path and assigning a wavelength without wavelength conversion. The model achieves better performance with fragmentation aware wavelength allocation strategy that minimizes fragmentation.

SDSFLF: fault localization framework for optical communication using software digital switching network

Optical network is an emerging technology for data communication inworldwide. The information is transmitted from the source to destination through the fiber optics. All optical network (AON) provides good transmission transparency, good expandability, large bandwidth, lower bit error rate (BER), and high processing speed. Link failure and node failure haveconsistently occurred in the traditional methods. In order to overcome the above mentioned issues, this paper proposes a robust software defined switching enabled fault localization framework (SDSFLF) to monitor the node and link failure in an AON. In this work, a novel faulty node localization (FNL) algorithm is exploited to locate the faulty node. Then, the software defined faulty link detection (SDFLD) algorithm that addresses the problem of link failure. The failures are localized in multi traffic stream (MTS) and multi agent system (MAS). Thus, the throughput is improved in SDSFLF compared than other existing methods like traditional routing and wavelength assignment (RWA), simulated annealing (SA) algorithm, attackaware RWA (A-RWA) convex, longest path first (LPF) ordering, and biggest source-destination node degree (BND) ordering. The performance of the proposed algorithm is evaluated in terms of network load, wavelength utilization, packet loss rate, and burst loss rate. Hence, proposed SDSFLF assures that high performance is achieved than other traditional techniques.

Wavelength Assignment Techniques with Converters for All Optical Networks (AONs)

We present an optical routing and wavelength assignment (RWA) technique for the best placement of the wavelength converters. The wavelength converter placement was considered independently at the complete nodes and partial nodes. In comparison to existing approaches, our technique performs better and requires fewer wavelength converters to get the required performance. As much as 7%, the likelihood of blocking is decreased. The study also demonstrates that the top four nodes have the biggest influence over the blocking performance of the network. It is clear that placing the converters in the right location is just as crucial to improving performance as simply adding more converters.

Quality aware path finding algorithm for mixed line rates WDM/DWDM networks

Abstract The optical network plays a vital role in the current technology for providing high-speed data communication, which the networks operate at Tb/s. In this case, different modulation techniques can be used for different line rates to achieve high-speed data transmission. The light paths and various data rates such as 10, 40, and 100 Gb/s are the important parameters for mixed line rate networks. The wavelengths and line rates are powerful tools for mixed line rates networks. It can exist on different optical fibers. In this paper, advanced modulation techniques achieve a relative performance with the required Q-factor. This paper analysis for different matrix computations to achieve a superior Q-factor. It can affect the data rates and quality of transmission. This paper also proposed an algorithm that can improve the Q-factor. Q-factor is analyzed and proposes routing and wavelength assignment (RWA) techniques based on the Q-factor obtained at different line rates. This paper is a brief overview of a quality-aware path-finding algorithm for mixed line rates WDM/DWDM networks are present and discussed.

Techniques for applying reinforcement learning to routing and wavelength assignment problems in optical fiber communication networks

We propose a novel application of reinforcement learning (RL) with invalid action masking and a novel training methodology for routing and wavelength assignment (RWA) in fixed-grid optical networks and demonstrate the generalizability of the learned policy to a realistic traffic matrix unseen during training. Through the introduction of invalid action masking and a new training method, the applicability of RL to RWA in fixed-grid networks is extended from considering connection requests between nodes to servicing demands of a given bit rate, such that lightpaths can be used to service multiple demands subject to capacity constraints. We outline the additional challenges involved for this RWA problem, for which we found that standard RL had low performance compared to that of baseline heuristics, in comparison with the connection requests RWA problem considered in the literature. Thus, we propose invalid action masking and a novel training method to improve the efficacy of the RL agent. With invalid action masking, domain knowledge is embedded in the RL model to constrain the action space of the RL agent to lightpaths that can support the current request, reducing the size of the action space and thus increasing the efficacy of the agent. In the proposed training method, the RL model is trained on a simplified version of the problem and evaluated on the target RWA problem, increasing the efficacy of the agent compared with training directly on the target problem. RL with invalid action masking and this training method outperforms standard RL and three state-of-the-art heuristics, namely, k shortest path first fit, first-fit k shortest path, and k shortest path most utilized, consistently across uniform and nonuniform traffic in terms of the number of accepted transmission requests for two real-world core topologies, NSFNET and COST–239. The RWA runtime of the proposed RL model is comparable to that of these heuristic approaches, demonstrating the potential for real-world applicability. Moreover, we show that the RL agent trained on uniform traffic is able to generalize well to a realistic nonuniform traffic distribution not seen during training, thus outperforming the heuristics for this traffic. Visualization of the learned RWA policy reveals an RWA strategy that differs significantly from those of the heuristic baselines in terms of the distribution of services across channels and the distribution across links.

On Deep Reinforcement Learning for Static Routing and Wavelength Assignment

Deep Reinforcement Learning (DRL) is rising as a promising tool for solving optimization problems in optical networks. Though studies employing DRL for solving static optimization problems in optical networks are appearing, assessing strengths and weaknesses of DRL with respect to state-of-the-art solution methods is still an open research question. In this work, we focus on Routing and Wavelength Assignment (RWA), a well-studied problem for which fast and scalable algorithms leading to better optimality gaps are always sought for. We develop two different DRL-based methods to assess the impact of different design choices on DRL performance. In addition, we propose a Multi-Start approach that can improve the average DRL performance, and we engineer a shaped reward that allows efficient learning in networks with high link capacities. With Multi-Start, DRL gets competitive results with respect to a state-of-the-art Genetic Algorithm with significant savings in computational times. Moreover, we assess the generalization capabilities of DRL to traffic matrices unseen during training, in terms of total connection requests and traffic distribution, showing that DRL can generalize on small to moderate deviations with respect to the training traffic matrices. Finally, we assess DRL scalability with respect to topology size and link capacity.

Stochastic RWA and Lightpath Rerouting in WDM Networks

In a telecommunication network, routing and wavelength assignment (RWA) is the problem of finding lightpaths for incoming connection requests. When facing a dynamic traffic, greedy assignment of lightpaths to incoming requests based on predefined deterministic policies leads to a fragmented network that cannot make use of its full capacity because of stranded bandwidth. At this point, service providers try to recover the capacity via a defragmentation process. We study this setting from two perspectives: (i) while granting the connection requests via the RWA problem and (ii) during the defragmentation process by lightpath rerouting. For both problems, we present the first two-stage stochastic integer programming model incorporating incoming request uncertainty to maximize the expected grade of service. We develop a decomposition-based solution approach, which uses various relaxations of the problem and a newly developed problem-specific cut family. Simulation of two-stage policies for a variety of instances in a rolling-horizon framework of 52 stages shows that our stochastic models provide high-quality solutions when compared with traditionally used deterministic ones. Specifically, the proposed provisioning policies yield improvements of up to 19% in overall grade of service and 20% in spectrum saving, while the stochastic lightpath rerouting policies grant up to 36% more requests, using up to just 4% more bandwidth spectrum.Summary of Contribution: For handling the intrinsic uncertainty of demand in the telecommunications industry, this paper proposes novel stochastic models and solution methodology for two fundamental problems in telecommunications at operational level: (i) routing and wavelength assignment (RWA) and (ii) lightpath rerouting problem. Despite the vast literature on the RWA problem, stochastic optimization has not been considered as a viable solution for resource allocation in optical networks. We propose two-stage stochastic programming models for both problems and design efficient decomposition-based solution methods that use various relaxations of the models and a new family of cutting planes. Our extensive and rigorous numerical experiments show the significant merit of incorporating uncertainty into decision making, as well as the effectiveness of the decomposition framework and our newly designed family of cuts in enhancing the solvability of both models. This work opens new avenues to explore where the powerful stochastic programming literature can be leveraged to make operational decisions in telecommunications problems, a field that currently relies mostly on deterministic and heuristic solution methods.

Design and control of highly spectrally efficient photonic networks enabled by fiber-granular routing on overlaid ring-shaped topologies

We propose an ultradense wavelength-division-multiplexing (WDM) network architecture that dramatically enhances channel accommodation density to achieve extremely high spectral efficiency. The trade-offs between high spectral efficiency and severe signal impairment due to optical filtering are resolved by network optimization of the overlaid subnetworks based on fiber-granular routing and wavelength-granular add/drop operations. The spectrum narrowing induced by node operations is well controlled by the impairment-suppressing routing and wavelength assignment (RWA) method. Numerical simulations on several metro-scale network topologies verify that the proposal enhances the spectral efficiency by up to 31.0% compared to comparable dense WDM networks.

Application of Machine Learning in Routing and Wavelength Assignment: Literature Survey

This paper presents the survey of the prominent issue in optical networks is Routing and Wavelength Assignment (RWA), due to the heavy traffic in network, there will be a need for wavelength assignment to the track in that network. The RWA problem is resolved using the approach of Genetic Algorithm and Ant Colony Optimization Algorithm, wherein up until now is noticed to be better than other optimization algorithms. By using Deep Q-Networks, a type of reinforcement learning, in optimizing the problem in selection of the routing path and wavelength assignment in an optical mesh network.

Solving Rwa Problem in All-optical Networks Based on Optimized Ant Colony Algorithm

The information bandwidth growth can be achieved using optical networking technology. Optical fiber has been used as a physical means through many network technologies. Wavelength Division Multiplexing (WDM) has emerged as a promising technology to conquer the use of large-bandwidth in optical networks. In WDM network, the essential problem is to serve the load traffic represented by the requests that arrive at the network and need to be served. For serve the request, the network will determine a path paired with a specific wavelength used to send the request's data from source to the destination node. This problem is called the routing and wavelength assignment (RWA). In this work, Multi-Node Optimized Ant Colony Algorithm (M-NOACA) proposed and implemented to solve the RWA problem in all-optical networks by simulating traffic with the RWA algorithms. A performance measurements applied to study the optimization problem of the RWA, and apply a comparison with the classic algorithms. Determining the best collection of parameters for an ant-based algorithm to obtain the best performance remains an open issue, in this work, a study is done to introduce a new contribution to obtain the optimal setting of Ant Colony parameters used to solve the RWA problem.

Hierarchical Routing and Resource Assignment in Spatial Channel Networks (SCNs): Oriented Toward the Massive SDM Era

In the past few decades, the architecture of optical networks has undergone significant evolution, from the earliest wavelength-division multiplexing (WDM) optical networks to elastic optical networks (EONs) and later to space-division multiplexing (SDM) EONs, to address the continuous growth of Internet traffic. By 2024, Pbps-level optical networks are expected, far exceeding the capacity limit of single-mode fibers. The massive SDM era is on the horizon. In this context, a newly designed architecture for optical networks called the spatial channel network (SCN) architecture, which achieves high cost efficiency by means of practical hierarchical optical cross-connects, has recently been proposed. However, the evolution of optical network architectures will simultaneously present challenges related to network optimization. For instance, with the evolution from WDM optical networks to EONs, the kernel network optimization problem was transformed from the routing and wavelength assignment (RWA) problem into the routing and spectrum assignment (RSA) problem due to the additionally introduced constraint of spectrum contiguity. Similarly, specially designed algorithms are also expected to be essential for addressing the network optimization problem in SCNs. In this paper, we define this new problem as the routing, spatial channel, and spectrum assignment (RSCSA) problem. We propose an integer linear programming (ILP) model and a heuristic algorithm to solve the RSCSA problem. We examine the performance of the proposed approaches via simulation experiments. The results show that both proposed approaches are effective in finding the optimal solutions or solutions close to the lower bounds. To the best of our knowledge, this is the first work to focus on the network optimization problem in SCNs.

Dynamic routing and wavelength assignment for efficient traffic grooming

Abstract The routing and wavelength assignment (RWA) schemes play an important role in all computer networks. The performance of a computer network and resource utilization largely depend on the RWA. The demand for higher bandwidth is increasing with each passing day, so more efficient RWA schemes need to be devolved to cater the increasing requirements. RWA becomes more challenging for dynamic traffic as the nature and flow of data are not known in advance. In this paper, a dynamic RWA scheme has been proposed for establishing a path in optical networks. The proposed scheme is based upon dynamic conversion sensing algorithm. It can be applied on a number of different network topologies. The proposed scheme is dynamic in nature, which is significantly useful for dynamic traffic grooming. The proposed scheme has been applied on 14 nodes National Science Foundation Network (NFSNet). Simulation results have shown that the blocking probability of this scheme is very low as compared to the existing schemes. So, the proposed dynamic RWA scheme enhances the network efficiency. It is useful for congestion hit networks. The reduced blocking probability in wavelength division multiplexing optical networks leads to better resource utilization and enhanced performance.

HPDST: Holding pathlength domain scheduled traffic strategy for multi-domain optical networks

In this paper, we proposed a resources allocation strategy for Routing and Wavelength Assignment (RWA) problem in Multi-Domain Optical Networks (MDONs). In the proposed Holding Pathlength Domain Scheduled Traffic (HPDST) strategy, the selection of candidate route is based on the three network parameters: holding time of request, pathlength of the route, and the number of domains across the route. On the basis of these parameters, order of the arrived connection requests is decided. The HPDST strategy is compared with two existing strategies: Minimum Holding Time (MinHoldTime) and Minimum Path Length (MinPathLen). In MinHoldTime scheduling strategy, connections with lowest holding time is given priority. Whereas, shortest route connections are considered first, in MinPathLen strategy. The performance of the proposed HPDST strategy is evaluated with three different network parameters. These are blocking probability, network capacity utilization, and resources utilization ratio. Simulation is performed under three different ratios of the intra and inter-domain scheduled traffic requests, as well as for the consideration of all inter-domain requests. Simulation of the proposed HPDST strategy shows better results as compared to the existing strategies under the investigated network parameters.

Physical layer impairment-aware ant colony optimization approach in WDM network

Abstract Physical layer impairments (PLIs) is a matter of great concern in optical networks to achieve the optimum performance. In order to mitigate the PLI, a better routing and wavelength assignment (RWA) technique is required in optical wavelength division multiplexing (WDM) networks. In this paper, a dynamic ant colony routing algorithm with the consideration of various PLIs has been presented. This work also presents a Q-factor estimation model that includes impairments such as amplified spontaneous emission (ASE) noise, four-wave mixing (FWM), and cross phase modulation (XPM). The simulation results also prove that the proposed ant colony optimization (ACO) based algorithm significantly reduces the blocking probability as compared to dynamic load based shortest path algorithm (LB-SPA).

An overview of ML-based applications for next generation optical networks

Over the past few decades, the demand for the capacity and reliability of optical networks has continued to grow. In the meantime, optical networks with larger knowledge scales have become sources of numerous heterogeneous data. In order to handle these new challenges, many issues need to be resolved, among which the low-margin optical networks design, power optimization, routing and wavelength assignment (RWA), failure management are quite important. However, the use of traditional algorithms in the above four applications shows some shortcomings. Fortunately, artificial intelligence (AI), especially machine learning (ML), is regarded as one of the most promising methods to overcome these shortcomings. In this study, we review the applications of ML methods in solving these four issues. Although many ML-based researches have emerged, the applications of ML techniques in optical networks still face challenges. Therefore, we also discuss some possible future directions of investigating ML-based approaches in optical networks.

Lightpath routing and wavelength assignment for static demand in translucent optical networks

Translucent optical networks use sparsely located regenerator nodes to increase the optical reach, otherwise limited by the physical layer impairments. Routing in translucent networks often causes the lightpath to traverse a fiber more than once, requiring the use of multiple wavelengths on a fiber for the same lightpath. Most of the routing and wavelength assignment (RWA) algorithms for static demand in translucent networks (termed static RWA) require loop-free routes for shortest path routing and also are agnostic to traffic load. In this paper, we address the problem of static RWA in translucent networks without using physical layer information and show how to route lightpaths on paths with a loop (i.e., not a simple path). We propose integer linear programming formulation to get the exact solutions to the static RWA problem. This work is the first one to optimally solve the static RWA problem for non-simple paths, providing the lower bound on the number of wavelengths and regeneration needed for static lightpath establishment when regeneration facilities are limited. We propose a heuristic algorithm based on the concept of Wardrop equilibrium, to adaptively equalize the mean delay along all the lightpath routes from source to destination. The proposed heuristic can not only accommodate the correlation between feasible lightpath routes but also account for the fact that alternate routes correspond to different levels of choice randomness. Wavelength assignment is done using graph coloring with branch-and-price method. Numerical results demonstrate that the proposed heuristic reduces the number of regenerators and wavelengths required to satisfy a given demand.

The Recent Contributions of Routing and Spectrum Assignment Algorithms in Elastic Optical Network (EON)

The speed is an important aspect of the current generation of Internet users. Currently WDM based optical fiber technology provides a good solution, still it is insufficient for future growth of cloud data centers, social media applications, online gaming, high definition video streaming and broadcasting, etc. The Elastic Optical Networks (EONs) recently added an energetic research field in the fiber-optic network. The EON is next generation of network that can provide flexibly in spectrum, improved service provisioning and accommodate high bandwidth applications in the single channel. In WDM, the Routing and Wavelength Assignment (RWA) is problem, likewise the EON has Routing and Spectrum Assignment (RSA) problem. In RSA problem the frequency slots (FSs) should allocate in continuity and contiguous manner for every demand request. Many research articles are published over the years to find the new feasible solutions for the RSA problem. This paper presents the recent approaches based on routing and spectrum assignment problem and fragmentation schemes which examines and compare the performance, features, and complexity of the approaches. This paper provides merits and demerits of existing approaches in RSA problem and provides more information about the approaches it will help for feature research.

A Routing and Wavelength Assignment Algorithm Based on Two Types of LEO Constellations in Optical Satellite Networks

Research on LEO inter-satellite laser communication based on wavelength division multiplexing inter-satellite links and its routing and wavelength assignment (RWA) technology is an important way to solve the problem of low-latency, high-capacity, high-speed transmission, and low-cost on-orbit real-time routing of free-space optical communications. Since both the transmitting and receiving sides of the inter-satellite communication are high-speed moving satellites, the network topology in the whole inter-satellite communication process is time-varying. Therefore, based on this particularity, we propose the SpacePro constellation and combined the classical NeLS constellation for mathematical modeling, and the network topology and inter-satellite links are predicted and analyzed in their entire cycles. The RWA under the super satellite nodes with computation and storage functions algorithm (SCSA) is proposed and its effect is compared and analyzed in the wavelength resource requirement and connectivity. The results showed that the SCSA algorithm can save up to 50% in wavelength requirements, and the SpacePro constellation can achieve the same network connectivity effect with fewer on-board processors and wavelengths than NeLS. Satellite optical network node connectivity and wavelength demand are a trade-off problem. Studying the RWA problem in this scenario has a very critical reference for how the future satellite optical network performs wavelength usage configuration to help improve the performance of networks.