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

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

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

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

Improving Routing Efficiency, Fairness, Differentiated Servises And Throughput In Optical Networks

Wavelength division multiplexed (WDM) optical networks are rapidly becoming the technology of choice in next-generation Internet architectures. This dissertation addresses the important issues of improving four aspects of optical networks, namely, routing efficiency, fairness, differentiated quality of service (QoS) and throughput. A new approach for implementing efficient routing and wavelength assignment in WDM networks is proposed and evaluated. In this approach, the state of a multiple-fiber link is represented by a compact bitmap computed as the logical union of the bitmaps of the free wavelengths in the fibers of this link. A modified Dijkstra\u27s shortest path algorithm and a wavelength assignment algorithm are developed using fast logical operations on the bitmap representation. In optical burst switched (OBS) networks, the burst dropping probability increases as the number of hops in the lightpath of the burst increases. Two schemes are proposed and evaluated to alleviate this unfairness. The two schemes have simple logic, and alleviate the beat-down unfairness problem without negatively impacting the overall throughput of the system. Two similar schemes to provide differentiated services in OBS networks are introduced. A new scheme to improve the fairness of OBS networks based on burst preemption is presented. The scheme uses carefully designed constraints to avoid excessive wasted channel reservations, reduce cascaded useless preemptions, and maintain healthy throughput levels. A new scheme to improve the throughput of OBS networks based on burst preemption is presented. An analytical model is developed to compute the throughput of the network for the special case when the network has a ring topology and the preemption weight is based solely on burst size. The analytical model is quite accurate and gives results close to those obtained by simulation. Finally, a preemption-based scheme for the concurrent improvement of throughput and burst fairness in OBS networks is proposed and evaluated. The scheme uses a preemption weight consisting of two terms: the first term is a function of the size of the burst and the second term is the product of the hop count times the length of the lightpath of the burst

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

A heuristic for placement of limited range wavelength converters in all-optical networks

Wavelength routed optical networks have emerged as a technology that can effectively utilize the enormous bandwidth of the optical fiber. Wavelength converters play an important role in enhancing the fiber utilization and reducing the overall call blocking probability of the network. As the distortion of the optical signal increases with the increase in the range of wavelength conversion in optical wavelength converters, limited range wavelength conversion assumes importance. Placement of wavelength converters is a NP complete problem [K.C. Lee, V.O.K. Li, IEEE J. Lightwave Technol. 11 (1993) 962-970] in an arbitrary mesh network. In this paper, we investigate heuristics for placing limited range wavelength converters in arbitrary mesh wavelength routed optical networks. The objective is to achieve near optimal placement of limited range wavelength converters resulting in reduced blocking probabilities and low distortion of the optical signal. The proposed heuristic is to place limited range wavelength converters at the most congested nodes, nodes which lie on the long lightpaths and nodes where conversion of optical signals is significantly high. We observe that limited range converters at few nodes can provide almost the entire improvement in the blocking probability as the full range wavelength converters placed at all the nodes. Congestion control in the network is brought about by dynamically adjusting the weights of the channels in the link thereby balancing the load and reducing the average delay of the traffic in the entire network. Simulations have been carried out on a 12-node ring network, 14-node NSFNET, 19-node European Optical Network (EON), 28-node US long haul network, hypothetical 30-node INET network and the results agree with the analysis. (C) 2001 Elsevier Science B.V, All rights reserved

Joint dimensioning of server and network infrastructure for resilient optical grids/clouds

We address the dimensioning of infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We design the resulting grid/cloud to be resilient against network link or server failures. To this end, we exploit relocation: Under failure conditions, a grid job or cloud virtual machine may be served at an alternate destination (i.e., different from the one under failure-free conditions). We thus consider grid/cloud requests to have a known origin, but assume a degree of freedom as to where they end up being served, which is the case for grid applications of the bag-of-tasks (BoT) type or hosted virtual machines in the cloud case. We present a generic methodology based on integer linear programming (ILP) that: 1) chooses a given number of sites in a given network topology where to install server infrastructure; and 2) determines the amount of both network and server capacity to cater for both the failure-free scenario and failures of links or nodes. For the latter, we consider either failure-independent (FID) or failure-dependent (FD) recovery. Case studies on European-scale networks show that relocation allows considerable reduction of the total amount of network and server resources, especially in sparse topologies and for higher numbers of server sites. Adopting a failure-dependent backup routing strategy does lead to lower resource dimensions, but only when we adopt relocation (especially for a high number of server sites): Without exploiting relocation, potential savings of FD versus FID are not meaningful