Simple and intelligent routing and wavelength assignment algorithm for all-optical networks

In optical WDM networks, transmission of information along optical lines is advantageous since it has high transmission capacity, scalability, feasibility and also high reliability. But large amount of information is being carried; any problem during transmission can lead to severe damage to the data being carried. Hence it is essential to consider the routing as well as the wavelength assignment problems and then develop a combined solution for both the problems. In this paper, we propose to develop a routing and wavelength assignment algorithm for selecting the suitable alternate path for the data packets transmission. Two stages are based on the available bandwidth and the number of wavelength used in the link as construction of alternate paths, route and wavelength selection. In proposed work, Adaptive Routing and First-Fit Wavelength Assignment (AR-FFWA) algorithm to be used. For each pair of source and destination, the path with the minimum granularity values are selected as the primary path for data transmission, allocating the sufficient wavelength and the performances will be evaluated by using ns-2 simulation models. When we compared to existing system the overall blocking probability will be reduced to too low.

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 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.

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.

Routing and wavelength assignment vs. EDFA reliability performance in optical backbone networks: An operational cost perspective

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.

WDM networks are increasingly gaining popularity as the backbone networks all over the world. In WDM network planning, electronic equipments like ADMs contribute heavily towards the capital expenditure of the network (Each ADM costs $50,000-$200,000). In WDM rings, determining minimum number of ADMs is NP-Hard. All the previous works have focused on Wavelength Assignment problem by keeping the routing fixed. In this paper, we suggest an integrated topology independent procedure called M3AWI (Minimum Average Minimum Weight Minimum Intersection). M3AWI is used to solve problems on four different types of routing namely, clockwise routing, Anti-clockwise routing, shortest-path routing, and hybrid routing. Our experimental results show that the combined Routing and Wavelength assignment (RWA) algorithms on an average save around 10% of the ADMs.We have also shown that the distribution of lightpaths is an important parameter to the service providers for selecting a routing policy.

In order to transmit data efficiently over an optical network, many routing and wavelength assignment (RWA) algorithms have been proposed. This work presents a genetic algorithm that aims at solving the RWA problem, which consists of choosing the most suitable lightpath (i.e., a combination of a route and a wavelength channel) between a source-destination pair of nodes in all-optical networks. A comparison to some already known approaches in terms of blocking probability was made. Results show a reasonable performance, since the average blocking probability achieved by the genetic algorithm was lower than or relatively equivalent to the standard approaches compared.

Transparent All Optical Networks carry huge traffic and any link failure can cause the loss of gigabits of data; hence protection and its guarantee becomes necessary at the time of failure. Many protection schemes were presented in the literature, but none of them speaks about protection guarantee. Also, in all optical networks, due to absence of regeneration capabilities, the physical layer impairments(PLI) accumulates along the lightpaths (LP) which causes sharp degradation of the Quality of Transmission (QoT), as measured by signal bit error rates (BER). The problem of protection with QoT issues was rarely studied in the literature. In this work, a novel protection backup path ensured, QoT aware Routing and Wavelength Assignment (RWA) algorithm called “Virtual Lit –Exhaustive Highest Q factor” (V-Lit EHQ) is presented which exhibits desirable qualities for reliable network operation. The results of the proposed work are compared with the standard QoT aware versions of the Shortest Path-First Fit schemes for both lit and dark protection. The blocking probability (BP) and BER are taken as the performance matrices and the proposed algorithm found to be outperforming as shown through simulations.

The Static RWA (Routing and Wavelength Assignment) problem in Optical Networks is a combinatorial optimization problem fit to iterative search methods. In this article we further investigate the static manycast RWA problem in optical networks and solve it using an evolutionary programming (EP) strategy such that the number of the manycast requests established for a given number of wavelengths is maximized. The proposed algorithm solves, approximately, the wavelength assignment problem while a backtracking approach is used to solve the routing issue. We present the details of our proposed algorithm and compare it to another metaheuristic named genetic algorithm (GA). EP shows a 24 % improvement over GA.

In wavelength division multiplexed all optical networks; lightpath establishes a connection between sending and receiving nodes bypassing the electronic processing at intermediate nodes. One of the prime objectives of Routing and Wavelength Assignment (RWA) problem is to maximize the number of connections efficiently by choosing the best routes. Although there are several algorithms available, improving the blocking performance in optical networks and finding optimal solutions for RWA problem has still remained a challenging issue. Wavelength conversion can be helpful in restricting the problem of wavelength continuity constraint but it increases complexity in the network. In this paper, we propose new weight dependent routing and wavelength assignment strategy for all optical networks without use of wavelength converters. Proposed weight function reduces blocking probability significantly, improving the network performance at various load conditions. Further, due to absence of wavelength converters, the cost and complexity of network reduces. Results show that the proposed strategy performs better than earlier reported methods.

As one of the core technology of ASON (automatically switched optical network), the dynamic routing and wavelength assignment algorithm is the key for ASON to reflecting its intelligence. In this paper, a new distributed heuristic algorithm based ant colony optimization for dynamic RWA (routing and wavelength assignment) that is adapted to the environment of ASON networks was put forward. The route selecting and the wavelength assignment process were combined with as whole using multilayer-graph model. The algorithm implements in distributed way using local information stored in a pheromone matrix and will be refreshed on the way of searching path. Compare with shortest-path+first-fit algorithms, it can reduce communication overhead cost of networks and the simulation computing showed lower blocking probability and more balanced wavelength resource assignment.

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

In this paper we investigate traffic-engineering issues in Wavelength Division Multiplexing (WDM) all- optical networks. In such networks, the wavelength continuity constraint along with the wavelength clash constraint, lead to poor network performances when dealing with the lightpath provisioning problem. The impact of these constraints is especially severe when traffic demands are unpredictable and characterized by random arrivals and departures. In order to alleviate the impact of these constraints, we propose to employ intentional/active rerouting. Active lightpath rerouting is to intentionally reroute already established lightpaths, during their life period, so as to achieve a better blocking performance. We here assume that due to the large geographic area an optical WDM network can cover, upgrading such a network to support the huge demand for network bandwidth can be costly. Thereby, it is extremely important for network operators to apply traffic-engineering strategies to cost-effectively optimize network throughput. Two new routing and wavelength assignment (RWA) algorithms applying intentional rerouting are proposed. Both algorithms dynamically reroute some already established lightpaths from longer paths to vacant shorter ones so as to reduce the network resources consumption and hence improve the network throughput. The first algorithm, namely, Timer-Based Active Lightpath Rerouting (TB-ALR) initiates the rerouting procedure every time a timer expires. The second algorithm, namely, Sequential Routing with Active Lightpath Rerouting (SeqRwALR) initiates the rerouting procedure when a connection leaves and its lightpaths are released. To the best of our knowledge, our global approach has not already been investigated in the literature. Simulation results show that the proposed active rerouting algorithms yield much lower connection rejection ratios than rerouting algorithms previously presented in the literature while rerouting a small number of already established lightpaths. By rerouting a small number of existing lightpaths, we hope that the disruption period incurred due to rerouting is minimized.

Recently, machine learning (ML) has attracted the attention of both researchers and practitioners to address several issues in the optical networking field. This trend has been mainly driven by the huge amount of available data (i.e., signal quality indicators, network alarms, etc.) and to the large number of optimization parameters which feature current optical networks (such as, modulation format, lightpath routes, transport wavelength, etc.). In this paper, we leverage the techniques from the ML discipline to efficiently accomplish the routing and wavelength assignment (RWA) for an input traffic matrix in an optical WDM network. Numerical results show that near-optimal RWA can be obtained with our approach, while reducing computational time up to 93% in comparison to a traditional optimization approach based on integer linear programming. Moreover, to further demonstrate the effectiveness of our approach, we deployed the ML classifier into an ONOS-based software defined optical network laboratory testbed, where we evaluate the performance of the overall RWA process in terms of computational time.