68 research outputs found

    An Ant-based Approach for Dynamic RWA in Optical WDM Networks

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    Ant-based Survivable Routing in Dynamic WDM Networks with Shared Backup Paths

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    A Survey of the Routing and Wavelength Assignment Problem

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    Particle swarm optimization for routing and wavelength assignment in next generation WDM networks.

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    PhDAll-optical Wave Division Multiplexed (WDM) networking is a promising technology for long-haul backbone and large metropolitan optical networks in order to meet the non-diminishing bandwidth demands of future applications and services. Examples could include archival and recovery of data to/from Storage Area Networks (i.e. for banks), High bandwidth medical imaging (for remote operations), High Definition (HD) digital broadcast and streaming over the Internet, distributed orchestrated computing, and peak-demand short-term connectivity for Access Network providers and wireless network operators for backhaul surges. One desirable feature is fast and automatic provisioning. Connection (lightpath) provisioning in optically switched networks requires both route computation and a single wavelength to be assigned for the lightpath. This is called Routing and Wavelength Assignment (RWA). RWA can be classified as static RWA and dynamic RWA. Static RWA is an NP-hard (non-polynomial time hard) optimisation task. Dynamic RWA is even more challenging as connection requests arrive dynamically, on-the-fly and have random connection holding times. Traditionally, global-optimum mathematical search schemes like integer linear programming and graph colouring are used to find an optimal solution for NP-hard problems. However such schemes become unusable for connection provisioning in a dynamic environment, due to the computational complexity and time required to undertake the search. To perform dynamic provisioning, different heuristic and stochastic techniques are used. Particle Swarm Optimisation (PSO) is a population-based global optimisation scheme that belongs to the class of evolutionary search algorithms and has successfully been used to solve many NP-hard optimisation problems in both static and dynamic environments. In this thesis, a novel PSO based scheme is proposed to solve the static RWA case, which can achieve optimal/near-optimal solution. In order to reduce the risk of premature convergence of the swarm and to avoid selecting local optima, a search scheme is proposed to solve the static RWA, based on the position of swarm‘s global best particle and personal best position of each particle. To solve dynamic RWA problem, a PSO based scheme is proposed which can provision a connection within a fraction of a second. This feature is crucial to provisioning services like bandwidth on demand connectivity. To improve the convergence speed of the swarm towards an optimal/near-optimal solution, a novel chaotic factor is introduced into the PSO algorithm, i.e. CPSO, which helps the swarm reach a relatively good solution in fewer iterations. Experimental results for PSO/CPSO based dynamic RWA algorithms show that the proposed schemes perform better compared to other evolutionary techniques like genetic algorithms, ant colony optimization. This is both in terms of quality of solution and computation time. The proposed schemes also show significant improvements in blocking probability performance compared to traditional dynamic RWA schemes like SP-FF and SP-MU algorithms

    A Novel Solution to the Dynamic Routing and Wavelength Assignment Problem in Transparent Optical Networks

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    We present an evolutionary programming algorithm for solving the dynamic routing and wavelength assignment (DRWA) problem in optical wavelength-division multiplexing (WDM) networks under wavelength continuity constraint. We assume an ideal physical channel and therefore neglect the blocking of connection requests due to the physical impairments. The problem formulation includes suitable constraints that enable the algorithm to balance the load among the individuals and thus results in a lower blocking probability and lower mean execution time than the existing bio-inspired algorithms available in the literature for the DRWA problems. Three types of wavelength assignment techniques, such as First fit, Random, and Round Robin wavelength assignment techniques have been investigated here. The ability to guarantee both low blocking probability without any wavelength converters and small delay makes the improved algorithm very attractive for current optical switching networks.Comment: 12 Pages, IJCNC Journal 201

    An ant-based algorithm for distributed routing and wavelength assignment in dynamic optical networks

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    Future optical communication networks are expected to change radically during the next decade. To meet the demanded bandwidth requirements, more dynamism, scalability and automatism will need to be provided. This will also require addressing issues such as the design of highly distributed control plane systems and their associated algorithms to respond to network changes very rapidly. In this work, we propose the use of an ant colony optimization (ACO) algorithm to solve the intrinsic problem of the routing and wavelength assignment (RWA) on wavelength continuity constraint optical networks. The main advantage of the protocol is its distributed nature, which provides higher survivability to network failures or traffic congestion. The protocol has been applied to a specific type of future optical network based on the optical switching of bursts. It has been evaluated through extensive simulations with very promising results, particularly on highly congested scenarios where the load balancing capabilities of the protocol become especially efficient. Results on a partially meshed network like NSFNET show that the ant-based protocol outperforms other RWA algorithms under test in terms of blocking probability without worsening other metrics such as mean route length.Peer ReviewedPostprint (published version

    Ant-Based Alternate Routing in All-Optical WDM Networks

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    A new proposal of an efficient algorithm for routing and wavelength assignment in optical networks.

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    The routing and wavelength assignment (RWA) algorithms used in optical networks are critical to achieve good network performance. However, despite several previous studies to optimize the RWA, which is classified as an NP-Hard, it seems that there is not, a priori, any solution that would lead to standardization of this process. This article presents the proposed RWA algorithm based on a Generic Objective Function (GOF) which aims to establish a base from which it is possible to develop a standard or multiple standards for optical networks. The GOF algorithm introduces the concept of implicit constraint, which guarantees a simple solution to a problem not as trivial as the RWA

    QoS multicast tree construction in IP/DWDM optical internet by bio-inspired algorithms

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    Copyright @ Elsevier Ltd. All rights reserved.In this paper, two bio-inspired Quality of Service (QoS) multicast algorithms are proposed in IP over dense wavelength division multiplexing (DWDM) optical Internet. Given a QoS multicast request and the delay interval required by the application, both algorithms are able to find a flexible QoS-based cost suboptimal routing tree. They first construct the multicast trees based on ant colony optimization and artificial immune algorithm, respectively. Then a dedicated wavelength assignment algorithm is proposed to assign wavelengths to the trees aiming to minimize the delay of the wavelength conversion. In both algorithms, multicast routing and wavelength assignment are integrated into a single process. Therefore, they can find the multicast trees on which the least wavelength conversion delay is achieved. Load balance is also considered in both algorithms. Simulation results show that these two bio-inspired algorithms can construct high performance QoS routing trees for multicast applications in IP/DWDM optical Internet.This work was supported in part ny the Program for New Century Excellent Talents in University, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant no. 60673159 and 70671020, the National High-Tech Reasearch and Development Plan of China under Grant no. 2007AA041201, and the Specialized Research Fund for the Doctoral Program of Higher Education under Grant no. 20070145017
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