WDM Optical Networks - A Review

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International Journal on Advanced Electrical and Computer Engineering (IJAECE)

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ISSN(Online): 2349-9338, ISSN(Print): 2349-932X Volume -3, Issue -3, 2016 1

WDM Optical Networks - A Review

1Shailaja S, ²Santhosh R, ³Swathi Bhardwaj R, ⁴Varshini R, ⁵Mallika M, ⁶Chethan N

1,2,3,4,5,6Dr. Ambedkar Institute of Technology, Bangalore

Abstract: Genetic algorithms (GA) provide an attractive approach to solving the challenging problem of dynamic routing and wavelength assignment (RWA) in optical wavelength division multiplexing (WDM) networks, with the aim of achieving a significantly low blocking probability. However, available GA-based dynamic RWA algorithms were designed mainly for WDM networks with the wavelength continuity constraint, and they cannot be applied directly to WDM networks with wavelength conversion capability. In this paper, the genetic algorithm based optimized routing strategy in wavelength division multiplexing optical networks is studied. The proposed approach shows that the blocking probability is improved.

The study of OPC (optimal placement of converter) problem has great implications to network design and applications.

I. INTRODUCTION

With fast growth of the Internet and World Wide Web, the network bandwidth requirements have increased dramatically in recent years. The research and technology development in Wavelength Division Multiplexing (WDM) networks are now evolving at a staggering pace to fulfill the increasing bandwidth requirement and the deployment of new network services. WDM wide-area networks (WANs) employ tunable lasers and filters at access nodes and optical/electronic switches at routing nodes which increase the bandwidth considerably.

In communication networks, the use of the optical technology has been popular for the need of wide band width, high speed transmission and large no of nodes.

An important goal of the design of WDM (wavelength division multiplexing) networks is to use less wavelengths to serve more communication needs.

According to the wavelength conflict rule, the number of wavelengths required in a WDM network is at least equal to the maximal number of channels over a fiber (called maximal link load) in the network. By placing wavelength converters at some nodes in the network, the number of wavelengths needed can be made equal to the maximal link load.

For optical transmission, wavelength division multiplexing (WDM) is proposed which has the ability to allocate many independent optical wavelengths on a single fiber link. A light path is an all optical channel which may be used to carry circuit-switched traffic and it may span multiple fiber links. In the absence of wavelength convertors, a light path would occupy the same wavelength on all fiber links through which it passes.

II. METHODOLOGY

An access node may transmit signals on different wavelengths, which are coupled into the fiber using wavelength multiplexers. However, the electronic switching and processing costs at the nodes can potentially be very high leading to severe performance bottlenecks and limiting the delivery of optical link bandwidth to the end users. Thus we look to assign single hop traffic (single light path) for most of the source-destination pairs.

One wavelength is dedicated to each channel between two adjacent nodes of the network. Interconnection between distant nodes is made possible by a set of available wavelengths over the path. With wavelength conversion capabilities inside the network, the nodes are capable of routing different wavelengths, which are reused throughout the network to establish all the required connections.

A wide range of optimization methods have been employed to solve various other optical network optimization problems. Some use traditional optimization methods that are guaranteed to find the global optimum, such as integer linear programming.

III. OBJECTIVE

The main objective of the optical network is to increase the acceptance ratio or to decrease the blocking probability of the connections in the network. For this achievement heuristic algorithms are be used.

To solve this problem three different methods are used namely: fixed, fixed-alternative and dynamic approach.

To establish a light path a set of pre-computed shortest paths are used in the fixed and fixed-alternative approach. These two approaches are very simple and have less setup time and also low control overhead. But these approaches results in high blocking probability.

The problem of assigning routes and wavelengths to light paths, called the Routing and wavelength Assignment (RWA) problem, an extra wavelength involves considerable increase in network cost, the objective is to minimize the number of wavelengths required, called the Network Wavelength Requirement (NWR).

IV. EVOLUTIONARY ALGORITHMS

 historical backgrounds

 representations

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 variation operators

 selection schemes

These all share a common conceptual base of simulating the evolution of individual structures via processes of selection, recombination, and mutation reproduction, there by producing better solutions. The processes depend on the perceived performance of the individual structures as defined by the problem.

The procedure is then iterated, as illustrated in Figure 1.

A population of candidate solutions (for the optimization task to be solved) is initialized. New solutions are created by applying reproduction operators (crossover and/or mutation). The fitness (how good the solutions are) of the resulting solutions is evaluated and suitable selection strategy is then applied to determine which solutions will be maintained into the next generation.

V.GENETIC ALGORITHMS

1. First formulated by Holland for adaptive search and by his students for optimisation from mid 1960s-1970s.

2. Binary strings have been used as individuals 3. Simulate Darwinian evolution.

4. Search operators are only applied to the genotypic representation (chromosome) of individuals.

5. Emphasise the role of recombination (crossover).

Mutation is only used as a background operator.

6. Often use roulette-wheel selection.

GA requires:

1. A genetic representation of the solution domain.

2. A fitness function to evaluate the solution domain.

VI. ALGORITHM

1 Generate the initial population P(0) at random, and set i ← 0

2. REPEAT

(a) Evaluate the fitness of each individual in P(i);

(b) Select parents from P(i) based on fitness in P(i);

(c) Generate offspring from the parents using crossover and mutation to form P(i + 1);

(d) i ← i + 1;

3. UNTIL halting criteria are satisfied

VII. WDM

The problem of finding the minimum set of network nodes such that, with wavelength converters at these nodes, broadcast can be supported in the network. A graph model is introduced to represent the WDM optical networks and give a mathematical formulation for the problem using this graph model. The problem of finding the minimum set of network nodes to place converters to support broadcast in WDM optical networks.

Fig 2 : Network Topologies

a) 9 node- network b) NSFNET 14 c) Germany 17 The 9-node network has a symmetric topology but the converter distribution is not symmetric. Node 2 and Node 4 are basically similar in the network but they have significant difference of wavelength converter distribution. One might wonder

the reason of this behaviour. This is because of the routing table calculation. Only three shortest routes are considered, and our routing calculation program tends to put the node with lower index into a route. For example, three routes connecting node-pair (1-9) are (1-2-3-6-9), (1-2-5-8-9) and (1-2-5-6-9).

Therefore, node 2 appears in more routes than node 4 and consequently, it has higher probability of being equipped with wavelength converters. This again confirms that the routing has a significant impact on converter placement. With a given routing strategy, there is a correspondent converter placement scheme.

VIII. RESULTS

The effectiveness of the proposed algorithm by extensive simulation. The simulation networks considered are real life existing networks like the 9 node network, 17 node Germany network and 14 node NSF network.

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Fig 3: Wavelength Conversion Distribution for node 9 Network

Fig 4: Wavelength Conversion Distribution for NSFNET 14 Network

Fig 5: Wavelength Conversion Distribution for Germany 17 Network

Hence, the number of wavelengths does not affect the wavelength converter distribution. So for a real-network, we can assume there is only a small number of wavelengths in the network, and do the experiment as described in Section III.A to find nodes with high probability of being equipped with wavelength Converters. This information is helpful for network planning, when the traffic is just roughly estimated and can change over time. Moreover, our approach can work with different routing strategies.

IX. EVOLUTIONARY COMPUTATION

As with any randomized algorithm, the results of a single run of an EA are meaningless.

X. APPLICATIONS

 The first step of EA applications is encoding (i.e., the representation of chromosomes).

 Evolutionary algorithms can also be combined with more traditional optimization techniques. This may be as simple as the use of a gradient minimization after primary search with an evolutionary algorithm.

 It may involve simultaneous application of other algorithms.

 In a typical application of GAs, the given problem is transformed into a set of genetic characteristics (parameters to be optimized) that will survive in the best possible manner in the environment.

 The emerging bandwidth-intensive computing and communication applications such as data browsing on the web, video conferencing, e-commerce, high- definition video/audio on-demand processing, data mining, database and decision-support transactions etc.

 EAs and survey application areas ranging from optimization, modeling and simulation to entertainment.

 Optimization problems form the most important application area of EAs.

 EC has been successfully used for control, electromagnetism, fluid mechanics, structural analysis.

XI. ADVANTAGES

 A primary advantage of evolutionary computation is that it is conceptually simple.

 Some specific advantages of genetic programming are that no analytical knowledge is needed and still accurate results could be obtained.

 GP approach does scale with the problem size. GP does impose restrictions on how the structure of solutions should be formulated.

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 The greatest advantage of evolutionary algorithms comes from the ability to address problems for which there are no human experts. Although human expertise should be used when it is available, it often proves less than adequate for automating problem solving routines.

 The major advantage of the GA approach is that it does not rely upon specific knowledge of the problem definition.

The success of the algorithm is attributed to various factors, including its powerful parallel search capability, computation simplicity, robustness, global search capability, ability to combine with other heuristic procedures and independence from solution characteristics such as linear or non-linear constraints and discrete or continuous search space.

XII. CONCLUSION

Fundamental aspects of evolutionary algorithms and its constituents, namely, genetic algorithm, evolution strategies, evolutionary programming, and genetic programming. Performance of genetic algorithms is demonstrated using two function optimization problems.

This paper discussed a genetic algorithm based optimized routing strategy in wavelength division multiplexing optical networks. The proposed approach shows that the blocking probability is improved. The study of OPC (optimal placement of converter) problem has great implications to network design and applications. The optimal solution to the OPC problem for duplex channels and an efficient approximation algorithm for unidirectional channels. Assumed that each wavelength converter has full wavelength conversion capability. Significant improvement in traffic-carrying capacity can be obtained in WDM networks. in any case find the nodes offering the highest pay-back when equipped with wavelength converters.

For future work, we will use the results from this study and then apply to dynamic traffic demands. The blocking probability will be investigated and compared to other wavelength converter placement algorithms.

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