Multicast Routing, Modulation Level and Spectrum Assignment in Elastic Optical Networks — A Genetic Algorithm-Based Approach

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

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

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

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.

Presents corrections to the paper, “Multicast routing and distance-adaptive spectrum allocation in elastic optical networks with shared protection,” (Cai, A., et al), J. Lightw. Technol., vol. 34, no. 17, pp. 4076–4088, Sep. 2016.

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%.

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

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.

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.

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.

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.

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.

In Elastic Optical Networks (EON), the literature usually measures QoT (Quality of Transmission) by the Optical Signal-to-Noise Ratio (OSNR) or estimated maximum transparent reach of the modulation technique adopted by the circuit. This paper evaluates the Quality of Transmission (QoT) margins adopted in traditional Routing, Modulation Level, and Spectrum Assignment (RMLSA) algorithms that quantity QoT through the maximum transparent reach of the modulation techniques. A RMLSA algorithm is proposed that categorizes the minimum margins that must be adopted for the establishment of new circuits, based on their expected degeneration and the impact it will have on the already established optical circuits. Simulation results show that the proposed minimum margins associated with circuit invigoration present better results than algorithms that propose a fairer margin or evaluate QoT through the maximum transparent reach. The proposal showed a reduction of up to 89% of bandwidth blocking rate and with a runtime performance similar to the simpler algorithms that evaluate QoT by distance.

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.

As the services in elastic optical networks (EONs) are bandwidth-intensive, unpredictable and dynamic, increasing network factors are emerging to affect the performance of network survivable planning and operation from network capacity to efficiency. Most of the traditional protection and restoration approaches may become before long inefficient due to the improvement of a particular network performance metric always at the expense of others. We argue that it would be more beneficial for comprehensive optimization of network performance to consider main network metrics jointly. Moreover, the highly dynamic features of EONs call for the new generation of machine learning-based solutions that are flexible and adaptable to cope with the dynamic nature of services to perform analytics. In this paper, we investigate the problem of global optimization of network performance under survivable EON environment. Specifically, a criterion, named the whole network cost-effectiveness value with survivability (WCES), is defined to measure the overall network performance by balancing the interaction among main network metrics. Then we propose a deep reinforcement learning (DRL) -based heuristic with the objective of improving overall network performance, in which two agents are utilized to provide working and protection schemes converging toward better survivable routing, modulation level and spectrum assignment (S-RMLSA) policies. Numerical results show that the proposed criterion can efficiently measure the overall network performance, and the double-agent DRL-based heuristic can greatly improve WCES while ensuring the network survivability and paying the acceptable extra consumption of request blocking probability.