A post-optimization method for the routing and wavelength assignment problem applied to scheduled lightpath demands

In WDM optical networks connection requests can be classified into three different types: Permanent Lightpath Demands (PLDs), Scheduled Lightpath Demands (SLDs), and Random Lightpath Demands (RLDs). These three types of traffic demands are considered for network planning, network engineering and traffic engineering respectively. In this paper we investigate the Routing and Wavelength Assignment (RWA) problem in all-optical WDM networks operating under the wavelength continuity constraint by jointly considering PLDs, SLDs and RLDs. We aim to model a realistic situation where an operator wishes to employ its optical network initially designed for PLDs and/or SLDs to offer on the fly lightpath provisioning. To the best of our knowledge, our global approach has not already been investigated in the literature. According to the considered timescale, various types of optimization tools are used. Numerous numerical results are presented and discussed to outline the benefits of such a global perspective.

In WDM all-optical networks without wavelength conversion, traffic rerouting is motivated either by an optimization of resource utilization or by network survivability. In this paper, we use rerouting to optimize network resources allocation in order to set up an incoming lightpath demand to be blocked for lack of resources. Rerouting aims at reassigning the wavelength and/or the path of one or several established connections in order to free enough wavelengths to satisfy the incoming demand. Rerouting refers implicitly to dynamic traffic. In most previous studies related to rerouting, only random (dynamic) traffic is considered. In this paper, we propose a new lightpath rerouting scheme considering three types of traffic demands, referred to as permanent lightpath demands (PLDs), scheduled lightpath demands (SLDs), and random lightpath demands (RLDs). PLDs are static, whereas SLDs and RLDs are dynamic. SLDs are preplanned, whereas RLDs are stochastic. PLDs may be seen as a particular case of SLDs. PLDs and SLDs correspond to guaranteed services, whereas RLDs correspond to best-effort services. Thus, PLDs and SLDs cannot be rerouted. We here describe two new routing and wavelength assignment (RWA) strategies applying rerouting. Both strategies assume that PLDs are routed offline during the network planning phase. The first strategy computes the RWA for SLDs and RLDs on the fly. The second strategy proceeds in two separate phases. It first computes offline the RWA for SLDs before considering RLDs on the fly on the remaining network resources. Our rerouting schemes aim at minimizing the number of RLDs or the number of optical channels to be rerouted. Through numerical examples and experimental simulations, we outline that routing SLDs offline and RLDs online instead of routing SLDs and RLDs online enables lower rejection ratios. We also compare our proposed rerouting algorithms with other approaches from the literature in terms of complexity.

The foreseeable lightpath demands (FLD) include scheduled lightpath demands (SLD) and predictable lightpath demands (PLD, lightpath demands that can be approximatively forecasted through the history traffic record). Since the time disjointness each FLD, the network planner can design the capacity of the network more compactly. We formulate the routing and wavelength assignment (RWA) problem when network dimensioning is coupled as the Integer linear programming (ILP) which includes the link formulation and path formulation. To reduce the computing complexity, a new routing algorithm based on the consideration of backup path is proposed. The software experiment is run and shows that the gain is about 20% when the backup path needs to be setup. At last we propose a new RWA algorithm to adapt the FLDs and dynamic traffic into the wavelength routing network.

In this paper a novel approach for modelling Routing and Wavelength Assignment (RWA) problem in wavelength-routed Dense Wavelength Division Multiplexing (DWDM) optical networks is proposed. A new idea based on Artificial Bee Colony (ABC) algorithm is introduced for solving RWA problem which is known to be an NP-hard problem. In the proposed ABC-RWA approach every food source represents a possible and feasible lightpath between each original and destination node pair in demand matrix. The positions of food sources are modified by some artificial bees in the population where the aim is to discover the places of food sources. The food source with the highest nectar value seems to be a solution which is evaluated by the fitness function. The simulation results demonstrate the ability and efficiency of proposed approach for solving RWA in real-world optical networks. The proposed approach could be extended for dynamic RWA schemes in real-time applications and employed by network resilience architectures.

Ant colony optimization (ACO) is a probabilistic technique used for solving complex computational problems, such
\nas finding optimal routes in networks. It has been proved to perform better than simulated annealing and genetic algorithm
\napproaches for solving dynamic problems. ACO algorithms can quickly adapt to real-time changes in the system. In this paper, we propose an ACO-based algorithm to solve the dynamic anycast routing and wavelength assignment (RWA) problem in wavelength-routed optical networks. Using extensive simulations, we show that ACO-based anycast RWA significantly reduces blocking probability compared to the fixed shortest-path first (SPF) and other load-balancing and dynamic algorithms.

A wavelength-routed optical network consists of a number of cross-connects which are interconnected by optical fibers. Transparent optical networks allow signals to bypass extensive electronic signal processing at intermediate nodes. The quality of an optical signal degrades as it travels though several optical components along its lightpath. It is essential to incorporate imapact of the physical impairments in the routing and wavelength assignment (RWA) problem in transparent optical networks. This work compiles a comprehensive survey, from various research publications, of the proposed algorithms that address this issue. The physical layer impairments classification in optical networks are initially presented followed by physical layer impairments (PLI) RWA algorithms and algorithmic approaches for the PLI-RWA problem. Performance metrics for evaluating the performance of the proposed PLI-RWA algorithms are presented at the end of the work. Further research topics are presented in this study.

We study the routing and wavelength assignment (RWA) problem in multi-segment optical networks. In these networks, each segment has different resource availability or hardware characteristics. The differences between multi-segment optical networks and homogeneous optical networks are discussed. We then present a resource abstraction technique called blocking island and define a multi-segment BIG (blocking island graph) network model. Using a minimum splitting routing heuristic introduced in the context of the blocking island paradigm in conjunction with the multi-segment BIG model, we propose a general RWA algorithm that takes a combined view of the network resource to integrate routing, wavelength assignment and gateway selection in a single routing framework. Using simulation, we demonstrate the effectiveness of our proposed algorithm by comparing it with other state-of-the-art heuristics in this area.

In this paper, we propose a new ant-based algorithm for the dynamic routing and wavelength assignment (RWA) problem in optical WDM networks under the wavelength continuity constraint. Unlike conventional approaches, which usually require centralized global network information, our new RWA algorithm constructs the routing solution in a distributed manner by means of cooperative ants. To facilitate the ants’ foraging task, we adopt in our algorithm a probabilistic routing table structure for route selection. The new algorithm is highly adaptive in that it always keeps a suitable number of ants in the network to cooperatively explore the network states and continuously update the routing tables, so that the route for a connection request can be determined promptly by the current states of routing tables with only a small setup delay. Some new schemes for path scoring and path searching are also proposed to enhance the performance of our ant-based algorithm. Extensive simulation results upon three typical network topologies indicate that the proposed algorithm has a very good adaptability to traffic variations and it outperforms both the fixed routing algorithm and the promising fixed–alternate routing algorithm in terms of blocking probability. The ability to guarantee both a low blocking probability and a small setup delay makes the new ant-based routing algorithm very attractive for both the optical circuit switching networks and future optical burst switching networks

In this paper, we deal with the static Routing and Wavelength Assignment (RWA) problem in networks with no wavelength converters, and where a given static set of connection demands is prearranged. Our objective is to maximize the number of optical connection-requests that can be established for a given number of wavelengths. A mathematical formulation for Max-RWA was presented. In this article, we implement and compare the performance of two random search algorithms namely: the genetic algorithm and the tabu search algorithm. Using these metaheuristics we solved approximately the wavelength assignment problem for Max-RWA while we computed its routing by a deterministic method which is the backtracking. Therefore we conducted many extensive experiments under different circumstances. Diagrams and representative numerical examples indicate the accuracy of our algorithms.

We study the energy-efficient routing and wavelength assignment (RWA) problem in translucent WDM networks, taking into account transmission limits of optical signals and the consequent need for signal regeneration. The problem is formulated through an integer linear programming model, and a heuristic algorithm called least regeneration first (LRF) is proposed to solve it in large networks. We first show that taking into account physical layer impairments and the consequent possible regeneration is mandatory to achieve satisfactory energy efficiency in RWA algorithms. In this respect, results show that LRF is much more efficient than ?impairment unaware? RWA algorithms. Then, we assess the energy efficiency achievable with different modulation formats using the proposed LRF algorithm in single-line-rate networks. Finally, the case of mixed-line-rate networks is also considered, and a heuristic method is proposed to efficiently decide the set of connections, at different bit rates, to be assigned to each traffic demand, with the goal of minimizing the total consumed power. Results show that this method is able to achieve power saving up to 25%.

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.

In this paper we consider the routing and wavelength assignment problem in a wavelength routed all optical network. Inspired by techniques from artificial intelligence, in particular the Blocking Island (BI) abstraction, we propose a simple and intelligent routing and wavelength assignment (RWA) algorithm: BI_RWA. This algorithm can be used in arbitrarily connected optical networks. In addition, it is general enough such that with some simple modifications, it can be applied to different optical networking scenarios: static or dynamic traffic, single or multiple fiber links between node pairs, with or without wavelength converters. We have conducted simulation experiments to evaluate the performance of our algorithm. The simulation is carried out in two parts: static traffic and dynamic traffic. The results will demonstrate that our RWA algorithm outperforms state-of-the-art related algorithms.

This paper proposes the optimal design of lightpath Routing and Wavelength Assignment (RWA) problem in Wavelength Division Multiplexing (WDM) networks without wavelength conversion. We formulate RWA as a mixed Integer Linear Programming (ILP) problem where the objective is to minimize the cost of wavelength assignment to the fiber links in the network. The Lagrangean relaxation technique and the optimization-based heuristics are used to solve this problem. Two sets of computational experiments are performed to test the algorithms for the maximum carried traffic and minimum wavelength requirements in three different network topologies (GTE, ARPA, OCT network). Based on solution quality to the computational experiments, the error gaps between the upper bound and the lower bound are close enough that the near optimal solutions could be obtained. On the other hand, we also show that the solution quality degrade gracefully under more and more heavy traffic network environment. By assessing solution quality and the computational time, we propose the efficient and effective optimization-based algorithms based on the Lagrangean relaxation method for the RWA problem in the WDM networks without wavelength conversion.

In this paper we consider the wavelength requirement for all‐optical networks based on wavelength‐division multiplexing (WDM). Given a static channel demand, the problem of routing and wavelength assignment (RWA) in such networks has to be solved. Due to the restricted number of wavelengths available on each link it is desirable to get a solution of the RWA problem, in which the needed number of wavelengths is minimized. Unfortunately, this optimization problem belongs to the class of NP‐complete problems. Therefore in this paper we present a new powerful method for the approximative solution of the RWA problem. This new approach is based on the combination of a genetic algorithm and a quite simple heuristic. The efficiency of this combined algorithm is demonstrated by the application to some well known benchmark problems.

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.