Power, routing, Modulation Level and Spectrum Assignment in all-optical and elastic networks

Elastic optical networks have been highlighted for attending to large volumes of data with different transmission rates. To meet high transmission rates, you need to troubleshoot RMLSA (Routing, Modulation Level, Spectrum Assignment) which is to select a route, choose a modulation level and a range of free spectrum. In the context, this paper we present a new RMLSA algorithm aware of the physical layer effects using the fuzzy system to assist in choosing the best route in elastic optical networks, called Fuzzy-RQoTO. The proposed algorithm selects the route of best quality for a given pair (o, d) considering metrics the relative fragmentation and number of occupied slots. The Fuzzy-RQoTO algorithm attempts to reduce the blockage caused by the degradation of the transmission quality of the optical circuits and concomitantly selects a best route for the establishment of the circuit. A performance evaluation study was performed on the NSFNet and EON topologies comparing the performance of the Fuzzy-RQoTO algorithm with three algorithms already proposed in the literature. The Fuzzy-RQoTO algorithm presented a minimum gain of 27.5% in terms of circuit blocking probability. In terms of bandwidth blocking probability, Fuzzy-RqoTO reported a minimum gain of 21.9%.

Fragmentation-aware algorithms for multipath routing and spectrum assignment in elastic optical networks

This study investigates the survivable traffic grooming problem for elastic optical networks with flexible spectrum grid employing new transmission technologies. In such networks, instead of following the traditional fixed ITU-T wavelength grid, optical transponders are capable of properly tuning their rates, and consequently their spectrum occupation, by introducing the fine-granular spectrum unit, called a frequency slot. The number of contiguous frequency slots allocated to an optical path (i.e., lightpath) is adjusted to the current network flow. In this study, we propose a novel shared protection specific to elastic networks, namely, elastic separate-protection-at-connection (ESPAC). It not only provides traditional backup sharing, but also offers a new opportunity of spectrum sharing enabled by the elasticity of the transponders: 1) if the working paths of two connections are link disjoint physically, and 2) if their backup paths traverse two lightpaths which are adjacent on a fiber link, then the two backup lightpaths can share spectrum. The new opportunity of spectrum sharing is realized by using First-Fit to assign working traffic and Last-Fit to assign backup traffic, and allowing spectrum overlap between adjacent backup wavelengths. The elasticity of the transponder enables the expansion and contraction of the lightpaths, thus when a single failure occurs in the network, lightpaths carrying backup flows can be tuned to appropriate rates in such a way that the overlap spectrum is used by only one of the adjacent lightpaths. The results show ESPAC is very spectrum efficient in elastic network setting.

A brief review of protection based routing and spectrum assignment in elastic optical networks and a novel p-cycle based protection approach for multicast traffic demands

Extending light-trail into elastic optical networks for dynamic traffic grooming

This paper focus on the routing, modulation level and spectrum allocation (RMLSA) problem in the next generation datacentre networks, which own the advantages of both Elastic Optical Networks (EON) and Software Defined Networking (SDN). We proposes an Integer Linear Programming (ILP) model for this problem. Simulation results show the ILP model achieves an outstanding load balancing performance.

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.

An elastic optical network (EON) plays an important role in transport technology for virtualization of networks. A key aspect of EONs is to establish lightpaths (virtual links) with exactly the amount of spectrum that is needed and with the possibility of grooming, the process of grouping many small traffic flows into larger units, creating a super-lightpath. Grooming eliminates the need for many guard bands between lightpaths and also saves transceivers; however, it often leads to the need to perform optical–electrical–optical conversions to multiple-data-rate optical signals at intermediate nodes. The aim of this paper is to provide a mixed-integer linear programming (MILP) formulation, as well as heuristic and meta-heuristic approaches, for the design of multiple virtual optical networks (VONs) in an elastic optical substrate network with bandwidth-variable lightpaths, modulation format constraints, and virtual elastic regenerator placement. Traffic grooming is allowed inside each VON, and a distance-adaptive modulation format technique is employed to guarantee efficiency in terms of bandwidth for a physical substrate, subject to several virtual topologies. A reduced MILP formulation without grooming capability is also proposed for comparison. The complete MILP formulation jointly solves the virtual topology design, regenerator placement, and grooming problems, as well as the routing, modulation, and spectrum assignment (RMSA) problem. The reduced MILP formulation, heuristics, and meta-heuristic, on the other hand, separate the virtual topology design problem from the RMSA problem. It is shown that the grooming approach can provide good results, since it solves the problem for a complete design when compared to the approach without grooming. Furthermore, heuristic solutions for large networks are proposed, which present good performance (in terms of saving spectrum) for the design with large instances.

The routing and spectrum assignment problems in elastic optical networks are well-known NP-hard problem, and are even more complex and challenging when the energy consumption is taken into account. To tackle this challenging problem, we establish a bi-level programming model with the energy consumption of the optical networks and the maximum index of used frequency slots as the leader’s and follower’s objectives to be minimized, respectively, which are used to determine the optimal schemes of routing and spectrum assignments. To solve the model effectively, a hybrid genetic algorithm framework integrating a heuristic algorithm into a genetic algorithm is proposed. We use heuristic algorithm to sort the connection requests and design a genetic algorithm with tailor-made crossover, mutation and local search operator to look for an optimal routing and spectrum assignment scheme. Finally, simulation experiments are conducted, and the experimental results indicate the effectiveness of the proposed model and algorithms.

We consider an elastic all-optical network, where each node is multicast-capable and does not support spectrum conversion. In such a network, for a given set of static multicast demands, we consider distance-adaptive spectrum resource allocation, and aim to optimize multicast routing, modulation, and spectrum assignment with shared protection in a way that minimizes the required spectrum resources for accommodating all multicast sessions. In our design, we provision each multicast demand by a light-tree where spectrum resources are allocated in all links included in the tree. We protect each light-tree from any single link failure in both directions by having a backup path that is link-disjoint to the path from the source to each destination on the primary tree. We reserve spectrum resources in the links that are not in the primary tree but in the backup paths between all source-destination pairs. The reserved spectrum resources can be shared to protect multiple light-trees as long as they do not fail simultaneously. For such a problem, we provide a mixed integer linear programming formulation. We also develop a scalable heuristic algorithm with an attribute that enables it to improve the quality of the results at the cost of longer running times. Numerical results for small problems show that the heuristic algorithm performs close to the optimum. In addition, we use a Markov chain simulation of the network to evaluate the performance of our proposed algorithm in terms of blocking probability in a dynamic environment, which demonstrates a significant improvement over straightforward approaches.

The problem of multicast routing, modulation level, and spectrum assignment is essential for the efficient performance of elastic optical networks. This problem seeks to compute light forests with the least optical network resources to satisfy multicast requests. The proposed strategies in the literature addressed this problem with exact or heuristic techniques, which are suitable when the number of requests is low. Developing scalable and efficient strategies is critical when the problem is complex, therefore, this study proposes a metaheuristic approach based on genetic algorithms. Given a set of multicast requests and a network topology, the proposed approach seeks to compute a set of light forests that minimizes (1) the number of blocked requests, (2) the maximum used frequency slot, and (3) the number of used transponders. Numerical simulations were performed to study the proposed algorithm performance under different static traffic loads and network topologies. The experimental results show that the proposed algorithm is promising for obtaining competitive solutions in reasonable computational time compared to the competitive heuristic approaches of state-of-the-art.

Deep reinforcement learning (DRL) has been introduced to the routing and spectrum assignment (RSA) of elastic optical networks (EONs) where the RSA policies are learnt during the interaction of a DRL agent with the EON environment. Upon each new traffic arrival, the DRL agent senses the EON state, and makes the RSA decision accordingly. Therefore, the EON state feature extraction is essential for the performance. However, current approaches, mainly based on fully connected neural networks or convolutional neural networks, are unsatisfactory in EON state feature extraction due to the following two reasons. (1) The optical network topology information is not well considered. (2) The path-level features are highly related to the RSA decision, due to the spectrum continuity constraint, while path-level feature extraction has not been well exploited. To overcome the above shortcomings, in this paper, we propose to utilize the Graph Convolutional Neural Network (GCN) and the Recurrent Neural Network (RNN) for the feature extraction of the network topology, and the aggregation of link-level features to path-level features. By doing this, critical RSA-related information can be sensed by the DRL agent to make better actions. Simulation results demonstrate that our proposed method outperforms previous approaches.

Elastic optical network (EON) is a promising candidate for the next generation optical transport network because of its high spectrum efficiency and flexible spectrum allocation. However, to migrate from today’s dense wavelength division multiplexing (DWDM) network to EON, optical nodes such as reconfigurable optical add/drop multiplexers (ROADMs) should be replaced with the ones with expensive flexi-grid wavelength selective switches (WSSs). Thus, whether and how today’s optical network will be evolved to EON is still under debate. Meanwhile, with the technical maturity of optical super-channels that transmit bit rates at the level of 1 Tb/s, super-channels are expected to dominate the future optical transport network because of their attractiveness of high spectrum efficiency and superior cost effectiveness. In this paper, we propose a new quasi-coarse wavelength division multiplexing (Quasi-CWDM) optical network architecture that is compatible to today’s WDM network in spectrum operation, while still maintaining the flexibility of adaptation between the bit rate and the transparent reach of an optical super-channel, but not requiring expensive flexi-grid WSSs. Specifically, the Quasi-CWDM optical network employs large fixed frequency grids, whose spacing falls between the traditional CWDM and DWDM, e.g., 200 GHz or 400 GHz. To maintain the flexibility between the bit rate and the transparent reach of an optical super-channel, different modulation formats are chosen according to channel physical distances. To minimize network hardware cost and maximize carried user traffic demand, we develop a mixed integer linear programming (MILP) model and a heuristic algorithm that can efficiently groom upper-layer IP traffic onto the optical layer by optical super-channels. We compare the design based on the Quasi-CWDM network to those based on the DWDM network and the advanced EON. It is found that the proposed Quasi-CWDM network has a much lower hardware cost than the other two types of networks, while achieving spectrum efficiency close to EON.

Objectives: The current technologies of wavelength routing and allocation of available for WDM networks are no longer applicable in Elastic Optical Networks (EON), since all their connections are allocated in a fixed manner, as provided for ITU. Therefore, the topic concerning routing and allocation of resources in EON is relevant given the growth projected in web traffic. This survey includes the evolution, main characteristics, and the process of routing and allocation of wavelengths required by EON. Methodology: The processes of allocation of sub-wavelengths and those of super-wavelengths are presented, as well as the multiple storage for different transmission rates and the principle of spectral contiguity. The process of routing and allocation taking OFMD into account is explained. Findings: In this study, we present a survey with routing and allocation techniques of wavelengths that improve the spectral efficiency of EON. Application/Improvements: This survey provides a wide knowledge on the methods to improve the spectral efficiency in EON. Such efficiency can promote an advance in new routing and allocation techniques for the optical transport networks. Keywords: Elastic Optical Networks, EON, Optical Networks, Routing, WDM

The transmission capacity of traditional single core fibers is reaching its upper bound. Space-division multiplexing (SDM) leveraging multi-core fibers is considered to be a promising solution for this problem. Elastic optical networks (EONs) with Multi-core fibers (MCF) are a kind of SDM-enabled EONs (SDM-EON) providing larger transmission capacity. However, the resource assignment in these networks becomes more complex due to inter-core crosstalk. Another important issue to be considered in future networks is the time domain behavior of incoming traffic which means considering Advance Reservation (AR) requests, in addition to regular Immediate Reservation (IR) requests. In this paper, the problem of routing, modulation level, core and spectrum assignment and scheduling is addressed in an SDM-EON with AR/IR traffic, in which inter-core crosstalk is modeled. This problem is formulated as a Mixed Integer Linear Programming (MILP) and a heuristic algorithm is proposed to solve it.