Routing and Spectrum Allocation Heuristic for Sliced Elastic Optical Network System

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.

With the ever increasing demand for optical networks, many decisions about network planning are becoming increasingly challenging. One of these crucial decisions is the network provisioning, known as the Routing and Spectrum Assignment (RSA) problem. What makes this problem practically challenging to service providers is the significant difference in size between the realistic problem instances and the instances that can be solved using the existing algorithms. Motivated to solve larger problem sizes, we propose a decomposition model and a nested column generation algorithm for the RSA problem. Compared to recent studies, the proposed algorithm produces higher-quality solutions for larger data instances quite efficiently. Furthermore, it provides a measure of the solution accuracy, i.e., an upper bound on the maximum distance of the solution obtained compared to the optimal solution.

We consider dynamic Routing and Spectrum Assignment problem in elastic optical networks with multiple fibers per link. The proposed path selection and spectrum management scheme is demonstrated to improve spectrum efficiency.

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.

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 this paper, we compare performance of fixed-grid Wavelength Division Multiplexing (WDM) and Elastic Optical Networks (EONs) in dynamic provisioning of many-to-many (m2m) flows. Currently, with a growth of services relying on many-to-many communication paradigm such as distributed computing, inter-Data Center communication, content delivery networking or other requiring high-bandwidth in group communication, it is vital to deliver new solutions facilitating these demanding transmissions. Therefore, the authors investigate provisioning of m2m traffic in high-performance optical networks. This work focuses on dynamic Routing and Wavelength Assignment (RWA) problem in WDM networks as well as Routing and Spectrum Allocation (RSA) problem in EONs for m2m flows using replicated communication through auxiliary rendezvous servers.

The routing and spectrum assignment problem in modern optical networks is an NP‐hard problem that has received increasing attention during the last years. The majority of existing integer linear programming models for the problem uses edge‐path formulations where variables are associated with all possible routing paths so that the number of variables grows exponentially with the size of the instance. To bypass this difficulty, precomputed subsets of all possible paths per demand are typically used, which cannot guarantee optimality of the solutions in general. Our contribution is to provide a framework for the use of edge‐path formulations to minimize the spectrum width of a solution. For that, we select an appropriate subset of paths to operate on with the help of combinatorial properties in such a way that optimality of the solution can be guaranteed. Computational results indicate that our approach is indeed promising to solve the routing and spectrum assignment problem.

This paper presents the features of an advanced software simulator for the Elastic Optical Network (EON) - Complex Elastic Optical Networks Simulator (CEONS). The CEONS is as a test environment for solving three main issues addressed in EON: Routing and Spectrum Assignment (RSA) problems; Routing, Modulation and Spectrum Assignment (RMSA) problems, and addressing regenerator placement problem. With a universal API, users can design their own algorithms in any programming language and implement them in the simulator as plugins to solve the problems mentioned above. The ease and versatility of implementing the algorithms allows usage of the CEONS in advanced problems and for the introduction of EON technology.

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.

Spectrum Expansion/Contraction and Survivable Routing and Spectrum Assignment problems on EONs with time-varying traffic

Minimum link and boundary distance spectrum assignment algorithm based on adaptive modulation scheme in semi-filterless optical networks

In this work, novel metaheuristic algorithms are proposed to address the network coding (NC)-based routing and spectrum allocation (RSA) problem in elastic optical networks, aiming to increase the level of security against eavesdropping attacks for the network's confidential connections. A modified simulated annealing, a genetic algorithm, as well as a combination of the two techniques are examined in terms of confidentiality and spectrum utilization. Performance results demonstrate that using metaheuristic techniques can improve the performance of NC-based RSA algorithms and thus can be utilized in real-world network scenarios.

In this paper, we investigate a scenario in which protected and unprotected services coexist in an elastic optical network under dynamic traffic. In the investigated scenario, unprotected services can reuse the reserved idle bandwidth to provide protection to the protected services. Under this scenario, we propose a new heuristic algorithm that enables such reuse as well as define and introduce a new assignment problem in elastic optical networks, named a Transmission Spectrum Assignment (T-SA) problem. In this paper, we consider a scenario in which services may be routed using the multipath routing approach. Additionally, protection using bandwidth squeezing is also considered. We assess our proposal through simulations on three different network topologies and compare our proposal against the classical protection approach, in which bandwidth reuse is not allowed. For the simulated range of network loads, the maximum (minimum) blocking probability reduction obtained by our proposal is approximately 48% (10%) in the European topology, 46% (7%) in the NSFNET topology, and 32% (6%) in the German topology.

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.

A deep reinforcement learning approach is applied, for the first time, to solve the routing, modulation, spectrum and core allocation (RMSCA) problem in dynamic multicore fiber elastic optical networks (MCF-EONs). To do so, a new environment - compatible with OpenAI's Gym - was designed and implemented to emulate the operation of MCF-EONs. The new environment processes the agent actions (selection of route, core and spectrum slot) by considering the network state and physical-layer-related aspects. The latter includes the available modulation formats and their reach and the inter-core crosstalk (XT), an MCF-related impairment. If the resulting quality of the signal is acceptable, the environment allocates the resources selected by the agent. After processing the agent's action, the environment is configured to give the agent a numerical reward and information about the new network state. The blocking performance of four different agents was compared through simulation to 3 baseline heuristics used in MCF-EONs. Results obtained for the NSFNet and COST239 network topologies show that the best-performing agent achieves, on average, up to a four-times decrease in blocking probability concerning the best-performing baseline heuristic methods.