A multipopulation parallel genetic simulated annealing based QoS routing and wavelength assignment integration algorithm for multicast in optical networks

Copyright @ 2008 Elsevier B.V. All rights reserved.In this paper, we propose an integrated Quality of Service (QoS) routing algorithm for optical networks. Given a QoS multicast request and the delay interval specified by users, the proposed algorithm can find a flexible-QoS-based cost suboptimal routing tree. The algorithm first constructs the multicast tree based on the multipopulation parallel genetic simulated annealing algorithm, and then assigns wavelengths to the tree based on the wavelength graph. In the algorithm, routing and wavelength assignment are integrated into a single process. For routing, the objective is to find a cost suboptimal multicast tree. For wavelength assignment, the objective is to minimize the delay of the multicast tree, which is achieved by minimizing the number of wavelength conversion. Thus both the cost of multicast tree and the user QoS satisfaction degree can approach the optimal. Our algorithm also considers load balance. Simulation results show that the proposed algorithm is feasible and effective. We also discuss the practical realization mechanisms of the algorithm.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/1, the National Natural Science Foundation of China under Grant nos. 60673159 and 70671020, the National High-Tech Research and Development Plan of China under Grant no. 2006AA01Z214, Program for New Century Excellent Talents in University, and the Key Project of Chinese Ministry of Education under Grant no. 108040

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

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 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

An application of a genetic algorithm for throughput optimization in non-broadcast WDM optical networks with regular topologies

We apply a genetic algorithm from Podnar and Skorin-Kapov [5] to a virtual topology design of a Wide-Area WDM Optical Network with regular topologies. Based on a given physical topology a virtual topology consisting of optical lightpaths is constructed. The objective is to minimize the maximal throughput, which implies balancing link loads and accommodating on-growing traffic requirements in a timely fashion. The genetic algorithm is applied to benchmark instances of regular topologies

Resilient network dimensioning for optical grid/clouds using relocation

In this paper we address the problem of dimensioning infrastructure, comprising both network and server resources, for large-scale decentralized distributed systems such as grids or clouds. We will provide an overview of our work in this area, and in particular focus on how to design the resulting grid/cloud to be resilient against network link and/or server site failures. To this end, we will exploit relocation: under failure conditions, a request may be sent to an alternate destination than the one under failure-free conditions. We will provide a comprehensive overview of related work in this area, and focus in some detail on our own most recent work. The latter comprises a case study where traffic has a known origin, but we assume a degree of freedom as to where its end up being processed, which is typically the case for e. g., grid applications of the bag-of-tasks (BoT) type or for providing cloud services. In particular, we will provide in this paper a new integer linear programming (ILP) formulation to solve the resilient grid/cloud dimensioning problem using failure-dependent backup routes. Our algorithm will simultaneously decide on server and network capacity. We find that in the anycast routing problem we address, the benefit of using failure-dependent (FD) rerouting is limited compared to failure-independent (FID) backup routing. We confirm our earlier findings in terms of network capacity savings achieved by relocation compared to not exploiting relocation (order of 6-10% in the current case studies)

Designing a multi-hop regular virtual topology for ultrafast optical packet switching : node placement optimisation and/or dilation minimisation?

This paper studies the design of multi-hop regular virtual topologies to facilitate optical packet switching in networks with arbitrary physical topologies. The inputs to the virtual topology design problem are the physical topology, the traffic matrix and the regular topology. In this paper, this problem is tackled directly and also by decomposition into two sub-problems. The first sub-problem, dilation minimisation, uses only the physical topology and the virtual topology as optimisation inputs. The second sub-problem considers the traffic matrix and virtual topology as optimisation inputs. The solutions of these two sub-problems are compared with each other and against the results obtained when the global problem is optimised (using all three possible input parameters) for a variety of traffic scenarios. This gives insight into the key question of whether the physical topology or the traffic matrix is the more important parameter when designing a regular virtual topology for optical packet switching. Regardless of the approach taken the problem is intractable and hence heuristics must be used to find (near) optimal solutions in reasonable time. Five different optimisation heuristics, using different artificial intelligence techniques, are employed in this paper. The results obtained by the heuristics for the three alternative design approaches are compared under a variety of traffic scenarios. An important conclusion of this paper is that the traffic matrix plays a less significant role than is conventionally assumed, and only a marginal penalty is incurred by disregarding it in several of the traffic cases considered

Review of routing and wavelength assignment problem

In today’s internet world there is a growing demand of network bandwidth. Where traditional copper fibers offer very less bandwidth, optical fibers can offer very lager bandwidth. So, there is a growing sense of using optical fibers. Optical networks generally use wavelength division multiplexing (WDM) technique, which is the backbone of future generation internet. In WDM networks fibers are logically divided into non-interfering, circuit-switched communication channels. In optical network Routing and Wavelength Assignment (RWA) problem is a typical problem. This can be seen as a conjunction of two problems, one is Routing and other one is Wavelength Assignment. First one finds a route from source to destination for requested connection and the next one assigns a wavelength to this route. The nature of RWA problem is NP-complete. Hence, heuristic approaches suits well for this class of problems. RWA problem can be formulated as Integer linear programming (ILP) problem. This type of problem focuses on optimizing a single objective. Here objectives may be minimizing the number of amplifiers or maximizing the number of connections or minimizing the number of wavelength used. But our primary objective in RWA problem is to establish a loop free path which minimizes the crosstalk. To achieve this objective we are taking the help of genetic algorithm (GA). Congestion among the individual lightpath request will be the parameter for the application of genetic algorithm

Minimization of Handoff Failure Probability for Next-Generation Wireless Systems

During the past few years, advances in mobile communication theory have enabled the development and deployment of different wireless technologies, complementary to each other. Hence, their integration can realize a unified wireless system that has the best features of the individual networks. Next-Generation Wireless Systems (NGWS) integrate different wireless systems, each of which is optimized for some specific services and coverage area to provide ubiquitous communications to the mobile users. In this paper, we propose to enhance the handoff performance of mobile IP in wireless IP networks by reducing the false handoff probability in the NGWS handoff management protocol. Based on the information of false handoff probability, we analyze its effect on mobile speed and handoff signaling delay.Comment: 16 Page