Static Routing And Wavelength Assignment Problem Research Articles

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.

Efficient Spectrum Utilization in Large Scale RWA Problems

While the routing and wavelength assignment (RWA) has been widely studied, a very few studies attempt to solve realistic size instances, namely, with 100 wavelengths per fiber and a few hundred nodes. Indeed, state of the art is closer to around 20 nodes and 30 wavelengths, regardless of what the authors consider, heuristics or exact methods with a very few exceptions. In this paper, we are interested in reducing the gap between realistic data sets and test bed instances that are often used, using exact methods. Even if exact methods may fail to solve in reasonable time very large instances, they can, however, output $\varepsilon $ -solutions with a very good and proven accuracy. The novelty of this paper is to exploit the observations that optimal solutions contain a very large number of light paths associated with shortest paths or $k$ -shortest paths with a small $k$ . We propose different RWA algorithms that lead to solve exactly or near exactly much larger instances than in the literature, i.e., with up to 150 wavelengths and 90 nodes. Extensive numerical experiments are conducted on both the static and dynamic incremental planning RWA problem.

Genetic algorithm and tabu search algorithm for solving the static manycast RWA problem in optical networks

The static routing and wavelength assignment (RWA) problem in Optical Networks is a combinatorial optimization problem fit to iterative search methods. In this paper we deal with the static manycast RWA problem in optical networks and solve it by maximizing the number of manycast request established for a given number of wavelengths. In this article, we implement and compare the performance of two metaheuristics namely the GA "Genetic Algorithm" and the TSA "Tabu Search Algorithm". The proposed algorithms solve, approximately, the wavelength assignment problem and a backtracking approach is used to solve the routing problem. We first introduce our algorithms. We then evaluate and compare their performance. We corroborate our theoretical findings through extensive simulations. Representative empirical results show the accuracy of our GA and TSA.

Binary quadratic programming formulation for routing and wavelength assignment problem in all-optical WDM networks

Routing and wavelength assignment (RWA) is the most concern in wavelength routed optical networks. This paper proposes a novel binary quadratic programming (BQP) formulation for the static RWA problem in order to balance traffic load among a network links more fairly. Subsequently, a greedy heuristic algorithm namely variable-weight routing and wavelength assignment (VW-RWA) is proposed to solve the developed BQP problem. In this method, the weight of a link is proportional to the link congestion. Performance evaluation results for different practical network topologies show that our proposed algorithm can decrease the number of required wavelengths in the network, blocking rate and variance of used wavelengths in each link. Besides, it is shown that the number of required wavelengths to establish call requests for a given network topology can be reduced at lower cost compared to other heuristics.

Applying MOEAs to solve the static Routing and Wavelength Assignment problem in optical WDM networks

Wavelength-Division Multiplexing (WDM) in optical networks has revolutionized the Telecommunication field. This technology is able to exploit the enormous bandwidth capability of this kind of networks, allowing communication between end users via all-optical WDM channels (lightpath). Given a set of demands, the problem of setting up lightpaths by routing and assigning a wavelength to each connection is known as Routing and Wavelength Assignment (RWA) problem. There are two types of connection demands: static (demands are given in advance) and dynamic (demands are given in real-time). In this paper we present two different Multiobjective Evolutionary Algorithms (MOEA) with the aim of solving the static RWA problem. The first one is a population-based algorithm, the Differential Evolution (DE), but incorporating the Pareto Tournament concept (DEPT). The second one is a multiobjective version of the Variable Neighborhood Search (VNS), MO-VNS. In order to prove the goodness of our metaheuristics, we have compared them with the standard Fast Non-Dominated Sorting Genetic Algorithm (NSGA-II), typical heuristics in the Telecommunication field, and different varieties of Multiobjective Ant Colony Optimization Algorithms. On the whole, we conclude that our approaches have obtained very promising results.

A proof of wavelength conversion not improving Lagrangian bounds of the sliding scheduled RWA problem

Extensive previous studies confirmed that wavelength conversion may only marginally improve the solutions to the static Routing and Wavelength Assignment (RWA) problem. This means that, for the static RWA problem, certain RWA schemes that do not use wavelength conversion can achieve a performance almost as good as the one from the best RWA scheme. Previous research work on sliding scheduled RWA problems, where a given set of lightpath demands are allowed to slide within their time windows, has also indicated in limited simulation results that the benefit of using wavelength conversion is marginal. However, the observation cannot be conclusive without the solid mathematical proof. We are thus motivated to investigate whether schedule sliding really requires wavelength conversion to achieve a better performance. In this paper, we prove that wavelength conversion does not improve the Lagrangian bound of the sliding scheduled RWA problem. In most test cases, this bound is very close to the best achieved objective function value. Our proof implies that, for those cases, the improvements achieved by making use of wavelength conversion are very marginal.

Variable neighbourhood search for resolving routing and wavelength assignment problem in optical wavelength-division multiplexing networks

The issue of finding optimal signal routes is one of the main challenges within the framework of optimal networks. This study aims at illustrating the development of a new, mixed-integer mathematical model for solving the static routing and wavelength assignment (RWA) problem in optical wavelength-division multiplexing (WDM) networks and presents the comparison of results obtained by heuristics and CPLEX commercial software. The objective function of the mathematical model is to minimise the blocking probability by maximising the number of successfully connected lightpaths. In this paper two WDM networks with full wavelength conversion nodes were tested. The proposed heuristics are the variable neighbourhood search (VNS) and tabu search (TS). Firstly, the authors compare the results obtained by these two heuristics and CPLEX on a low-complexity WDM network, aiming to determine whether these heuristics may yield results which approximate optimal results. Secondly, having illustrated that the heuristics provide satisfactory results, the authors run these heuristics on a high-complexity WDM network. The results indicate minor differences in blocking probability and show that in majority cases, VNS spends ten times less CPU time as opposed to TS. In conclusion, the authors argue that VNS heuristic is appropriate for resolving this static RWA problem.

A proof of wavelength conversion not improving the lagrangian bound of the static RWA problem

The fact that wavelength conversion hardly improves the performance of static routing and wavelength assignment (RWA) in wavelength division multiplexing (WDM) networks has been observed in many previous studies. However, other than simulation results, until now there was no formal proof of such fact. In this paper, we formally prove that wavelength conversion does not improve the Lagrangian bound of the static RWA problem.

Optimization of Semi-Dynamic Lightpath Rearrangements in a WDM Network

In this paper, we study the routing and wavelength assignment (RWA) problem in a semi-dynamic scenario where rearrangements are conducted in a series of sessions after traffic demands vary. Unlike pure static RWA problems, each rearrangement scheme must consider established lightpaths in the previous session. A novel formulation of the WDM network rearrangement problem is used to minimize rejected new demands and rerouted lightpaths. This is done by coordinating the re-routing of existing lightpaths with the adaptation to varying demands. The Lagrangean Relaxation and Subgradient Method (LRSM) has been successfully used to solve the problem along with fairness consideration. The superior performance and reduced computation complexity of our algorithm are demonstrated in sample networks. In addition, we evaluate the benefit of using wavelength converters in a WDM network rearrangement. In contrast to previous studies with conclusions that wavelength converters are of little value in the static RWA problem, we show that wavelength converters improve network performance in a WDM network rearrangement.