The Experts below are selected from a list of 17382 Experts worldwide ranked by ideXlab platform
Dingde Jiang - One of the best experts on this subject based on the ideXlab platform.
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a compressive sensing based approach to end to end network Traffic reconstruction
IEEE Transactions on Network Science and Engineering, 2020Co-Authors: Dingde Jiang, Wenjuan Wang, Lei Shi, Houbing SongAbstract:Estimation of end-to-end network Traffic plays an important role in Traffic engineering and network planning. The direct measurement of a network's Traffic Matrix consumes large amounts of network resources and is thus impractical in most cases. How to accurately construct Traffic Matrix remains a great challenge. This paper studies end-to-end network Traffic reconstruction in large-scale networks. Applying compressive sensing theory, we propose a novel reconstruction method for end-to-end Traffic flows. First, the direct measurement of partial Origin-Destination (OD) flows is determined by random measurement Matrix, providing partial measurements. Then, we use the K-SVD approach to obtain a sparse Matrix. Combined with compressive sensing, this partially known OD flow Matrix can be used to recover the entire end-to-end network Traffic Matrix. Simulation results show that the proposed method can reconstruct end-to-end network Traffic with a high degree of accuracy. Moreover, in comparison with previous methods, our approach exhibits a significant performance improvement.
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Fine-granularity inference and estimations to network Traffic for SDN.
PloS one, 2018Co-Authors: Dingde Jiang, Liuwei HuoAbstract:An end-to-end network Traffic Matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network Traffic Matrix's inferences and estimations are a challenging problem. Moreover, attaining the Traffic Matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network Traffic Matrix in fine time granularity from the sampled Traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network Traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network Traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective.
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Modeling network Traffic for Traffic Matrix estimation and anomaly detection based on Bayesian network in cloud computing networks
Annales Des Télécommunications, 2017Co-Authors: Dingde Jiang, Zhihan LvAbstract:With the rapid development of a cloud computing network, the network security has been a terrible problem when it provides much more services and applications. Network Traffic modeling and analysis is significantly crucial to detect some lawless activities such as DDoS, virus and worms, and so on. Meanwhile, it is a common approach for acquiring a Traffic Matrix, which can be used by network operators to carry out network management and planning. Although a great number of methods have been proposed to model and analyze the network Traffic, it is still a remarkable challenge since the network Traffic characterization has been tremendously changed, in particular, for a cloud computing network. Motivated by that, we analyze and model the statistical features of network Traffic based on the Bayesian network in this paper. Furthermore, we propose an accurate network Traffic estimation approach and an efficient anomaly detection approach, respectively. In detail, we design a Bayesian network structure to model the causal relationships between network Traffic entries. Based on this Bayesian network model, we obtain a joint probability distribution of network Traffic by the maximum a posteriori approach. Then, we estimate the network Traffic in terms of a regularized optimization model. Meanwhile, we also perform anomaly detection based on the proposed Bayesian network structure. We finally discuss the effectiveness of the proposed method for Traffic Matrix estimation and anomaly detection by applying it to the Abilene and GEANT networks.
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Traffic Matrix prediction and estimation based on deep learning for data center networks
Global Communications Conference, 2016Co-Authors: Laisen Nie, Dingde Jiang, Lei Guo, Houbing SongAbstract:Network Traffic analysis is a crucial technique for systematically operating a data center network. Many network management functions rely on exact network Traffic information. Although a great number of works to obtain network Traffic have been carried out in traditional ISP networks, they cannot be employed effectively in data center networks. Motivated by that, we focus on the problem of network Traffic prediction and estimation in data center networks. We involve deep learning techniques in the network Traffic prediction and estimation fields, and propose two deep architectures for network Traffic prediction and estimation, respectively. We first use a deep architecture to explore the time-varying property of network Traffic in a data center network, and then propose a novel network Traffic prediction approach based on a deep belief network and a logistic regression model. Meanwhile, to deal with the highly ill-pose property of network Traffic estimation, we further propose a network Traffic estimation method using the deep belief network trained by link counts. We validate the effectiveness of our methodologies by real Traffic data.
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Traffic Matrix prediction and estimation based on deep learning in large scale ip backbone networks
Journal of Network and Computer Applications, 2016Co-Authors: Laisen Nie, Dingde Jiang, Lei GuoAbstract:Network Traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network Traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network Traffic data. Though the network Traffic estimation method has been the most prevalent technique for acquiring network Traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network Traffic estimation problem is more deteriorated. Besides, the statistical features of network Traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network Traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network Traffic, and then propose a novel network Traffic prediction approach based on a deep belief network. We further propose a network Traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GANT backbone networks.
Emmanouel Varvarigos - One of the best experts on this subject based on the ideXlab platform.
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elastic bandwidth allocation in flexible ofdm based optical networks
Journal of Lightwave Technology, 2011Co-Authors: K Christodoulopoulos, I Tomkos, Emmanouel VarvarigosAbstract:Orthogonal Frequency Division Multiplexing (OFDM) has recently been proposed as a modulation technique for optical networks, because of its good spectral efficiency, flexibility, and tolerance to impairments. We consider the planning problem of an OFDM optical network, where we are given a Traffic Matrix that includes the requested transmission rates of the connections to be served. Connections are provisioned for their requested rate by elastically allocating spectrum using a variable number of OFDM subcarriers and choosing an appropriate modulation level, taking into account the transmission distance. We introduce the Routing, Modulation Level and Spectrum Allocation (RMLSA) problem, as opposed to the typical Routing and Wavelength Assignment (RWA) problem of traditional WDM networks, prove that is also NP-complete and present various algorithms to solve it. We start by presenting an optimal ILP RMLSA algorithm that minimizes the spectrum used to serve the Traffic Matrix, and also present a decomposition method that breaks RMLSA into its two substituent subproblems, namely 1) routing and modulation level and 2) spectrum allocation (RML+SA), and solves them sequentially. We also propose a heuristic algorithm that serves connections one-by-one and use it to solve the planning problem by sequentially serving all the connections in the Traffic Matrix. In the sequential algorithm, we investigate two policies for defining the order in which connections are considered. We also use a simulated annealing meta-heuristic to obtain even better orderings. We examine the performance of the proposed algorithms through simulation experiments and evaluate the spectrum utilization benefits that can be obtained by utilizing OFDM elastic bandwidth allocation, when compared to a traditional WDM network.
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routing and spectrum allocation in ofdm based optical networks with elastic bandwidth allocation
Global Communications Conference, 2010Co-Authors: K Christodoulopoulos, I Tomkos, Emmanouel VarvarigosAbstract:Orthogonal Frequency Division Multiplexing (OFDM) has been recently proposed as a modulation technique for optical networks, due to its good spectral efficiency and impairment tolerance. Optical OFDM is much more flexible compared to traditional WDM systems, enabling elastic bandwidth transmissions. We consider the planning problem of an OFDM-based optical network where we are given a Traffic Matrix that includes the requested transmission rates of the connections to be served. Connections are provisioned for their requested rate by elastically allocating spectrum using a variable number of OFDM subcarriers. We introduce the Routing and Spectrum Allocation (RSA) problem, as opposed to the typical Routing and Wavelength Assignment (RWA) problem of traditional WDM networks, and present various algorithms to solve the RSA. We start by presenting an optimal ILP RSA algorithm that minimizes the spectrum used to serve the Traffic Matrix, and also present a decomposition method that breaks RSA into two substituent subproblems, namely, (i) routing and (ii) spectrum allocation (R+SA) and solves them sequentially. We also propose a heuristic algorithm that serves connections one-by-one and use it to solve the planning problem by sequentially serving all Traffic Matrix connections. To feed the sequential algorithm, two ordering policies are proposed; a simulated annealing meta-heuristic is also used to obtain even better orderings. Our results indicate that the proposed sequential heuristic with appropriate ordering yields close to optimal solutions in low running times.
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offline impairment aware routing and wavelength assignment algorithms in translucent wdm optical networks
Journal of Lightwave Technology, 2009Co-Authors: Konstantinos Manousakis, K Christodoulopoulos, I Tomkos, E Kamitsas, Emmanouel VarvarigosAbstract:Physical impairments in optical fiber transmission necessitate the use of regeneration at certain intermediate nodes, at least for certain lengthy lightpaths. We design and implement impairment-aware algorithms for routing and wavelength assignment (IA-RWA) in translucent optical networks. We focus on the offline version of the problem, where we are given a network topology, the number of available wavelengths and a Traffic Matrix. The proposed algorithm selects the 3R regeneration sites and the number of regenerators that need to be deployed on these sites, solving the regenerator placement problem for the given set of requested connections. The problem can be also posed in a slightly different setting, where a (sparse) placement of regenerators in the network is given as input and the algorithm selects which of the available regenerators to use, solving the regenerator assignment problem. We formulate the problem of regenerator placement and regenerator assignment, as a virtual topology design problem, and address it using various algorithms, ranging from a series of integer linear programming (ILP) formulations to simple greedy heuristic algorithms. Once the sequence of regenerators to be used by the non-transparent connections has been determined, we transform the initial Traffic Matrix by replacing non-transparent connections with a sequence of transparent connections that terminate and begin at the specified 3R intermediate nodes. Using the transformed Matrix we then apply an IA-RWA algorithm designed for transparent (as opposed to translucent) networks to route the Traffic. Blocked connections are re-routed using any remaining regenerator(s) in the last phase of the algorithm.
Lei Guo - One of the best experts on this subject based on the ideXlab platform.
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Traffic Matrix prediction and estimation based on deep learning for data center networks
Global Communications Conference, 2016Co-Authors: Laisen Nie, Dingde Jiang, Lei Guo, Houbing SongAbstract:Network Traffic analysis is a crucial technique for systematically operating a data center network. Many network management functions rely on exact network Traffic information. Although a great number of works to obtain network Traffic have been carried out in traditional ISP networks, they cannot be employed effectively in data center networks. Motivated by that, we focus on the problem of network Traffic prediction and estimation in data center networks. We involve deep learning techniques in the network Traffic prediction and estimation fields, and propose two deep architectures for network Traffic prediction and estimation, respectively. We first use a deep architecture to explore the time-varying property of network Traffic in a data center network, and then propose a novel network Traffic prediction approach based on a deep belief network and a logistic regression model. Meanwhile, to deal with the highly ill-pose property of network Traffic estimation, we further propose a network Traffic estimation method using the deep belief network trained by link counts. We validate the effectiveness of our methodologies by real Traffic data.
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Traffic Matrix prediction and estimation based on deep learning in large scale ip backbone networks
Journal of Network and Computer Applications, 2016Co-Authors: Laisen Nie, Dingde Jiang, Lei GuoAbstract:Network Traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network Traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network Traffic data. Though the network Traffic estimation method has been the most prevalent technique for acquiring network Traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network Traffic estimation problem is more deteriorated. Besides, the statistical features of network Traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network Traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network Traffic, and then propose a novel network Traffic prediction approach based on a deep belief network. We further propose a network Traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GANT backbone networks.
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accurate estimation of large scale ip Traffic Matrix
Aeu-international Journal of Electronics and Communications, 2011Co-Authors: Dingde Jiang, Xingwei Wang, Lei Guo, Zhenhua ChenAbstract:Abstract Traffic Matrix (TM) estimation, which is an interesting and important research topic at present, is used to conduct network management, Traffic detecting, provisioning and so on. However, because of inherent characteristics in the IP network, especially in the large-scale IP network, TM estimation itself is highly under-constrained, and so it is an very ill-posed problem. how fast and accurately to attain large-scale IP TM estimation is a challenge. Based on back-propagation neural network (BPNN), this paper proposes a novel method for large-scale IP TM estimation, called BPNN TM estimation (BPTME). In contrast to previous methods, BPTME can easily avoid the complex mathematical computation so that we can quickly estimate the TM. The model of large-scale IP TM estimation built on top of BPNN, whose outputs can sufficiently represent TM's spatial-temporal correlations, ensures that we can attain an accurate estimation result. Finally, we use the real data from the Abilene Network to validate and evaluate BPTME. Simulation results show that BPTME not only improves remarkably and holds better robustness, but it can also make more accurate estimation of large-scale IP TM and track quickly its dynamics.
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an optimization method of large scale ip Traffic Matrix estimation
Aeu-international Journal of Electronics and Communications, 2010Co-Authors: Dingde Jiang, Xingwei Wang, Lei GuoAbstract:Abstract This letter studies the large-scale IP Traffic Matrix (TM) estimation problem and proposes a novel method called the simulated annealing and generalized inference (SAGI). The major challenge of large-scale IP TM estimation is its highly ill-posed nature. We describe the TM estimation into a modified simulated annealing process, and then by using the generalized inference we can easily overcome its ill-posed nature. Simulation results show that SAGI is promising.
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mahalanobis distance based Traffic Matrix estimation
European Transactions on Telecommunications, 2010Co-Authors: Dingde Jiang, Xingwei Wang, Lei GuoAbstract:This letter studies large-scale IP Traffic Matrix (TM) estimation problem and proposes a novel method called the Mahalanobis distance-based regressive inference (MDRI). By using Mahalanobis distance as an optimal metric, we can get rid of the highly ill-posed nature of this problem. We describe the TM estimation into an optimal process, and then by optimising the regularised equation about this problem, TM's estimation can accurately obtained. Testing results are shown to be promising. Copyright © 2010 John Wiley & Sons, Ltd.
K Christodoulopoulos - One of the best experts on this subject based on the ideXlab platform.
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elastic bandwidth allocation in flexible ofdm based optical networks
Journal of Lightwave Technology, 2011Co-Authors: K Christodoulopoulos, I Tomkos, Emmanouel VarvarigosAbstract:Orthogonal Frequency Division Multiplexing (OFDM) has recently been proposed as a modulation technique for optical networks, because of its good spectral efficiency, flexibility, and tolerance to impairments. We consider the planning problem of an OFDM optical network, where we are given a Traffic Matrix that includes the requested transmission rates of the connections to be served. Connections are provisioned for their requested rate by elastically allocating spectrum using a variable number of OFDM subcarriers and choosing an appropriate modulation level, taking into account the transmission distance. We introduce the Routing, Modulation Level and Spectrum Allocation (RMLSA) problem, as opposed to the typical Routing and Wavelength Assignment (RWA) problem of traditional WDM networks, prove that is also NP-complete and present various algorithms to solve it. We start by presenting an optimal ILP RMLSA algorithm that minimizes the spectrum used to serve the Traffic Matrix, and also present a decomposition method that breaks RMLSA into its two substituent subproblems, namely 1) routing and modulation level and 2) spectrum allocation (RML+SA), and solves them sequentially. We also propose a heuristic algorithm that serves connections one-by-one and use it to solve the planning problem by sequentially serving all the connections in the Traffic Matrix. In the sequential algorithm, we investigate two policies for defining the order in which connections are considered. We also use a simulated annealing meta-heuristic to obtain even better orderings. We examine the performance of the proposed algorithms through simulation experiments and evaluate the spectrum utilization benefits that can be obtained by utilizing OFDM elastic bandwidth allocation, when compared to a traditional WDM network.
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routing and spectrum allocation in ofdm based optical networks with elastic bandwidth allocation
Global Communications Conference, 2010Co-Authors: K Christodoulopoulos, I Tomkos, Emmanouel VarvarigosAbstract:Orthogonal Frequency Division Multiplexing (OFDM) has been recently proposed as a modulation technique for optical networks, due to its good spectral efficiency and impairment tolerance. Optical OFDM is much more flexible compared to traditional WDM systems, enabling elastic bandwidth transmissions. We consider the planning problem of an OFDM-based optical network where we are given a Traffic Matrix that includes the requested transmission rates of the connections to be served. Connections are provisioned for their requested rate by elastically allocating spectrum using a variable number of OFDM subcarriers. We introduce the Routing and Spectrum Allocation (RSA) problem, as opposed to the typical Routing and Wavelength Assignment (RWA) problem of traditional WDM networks, and present various algorithms to solve the RSA. We start by presenting an optimal ILP RSA algorithm that minimizes the spectrum used to serve the Traffic Matrix, and also present a decomposition method that breaks RSA into two substituent subproblems, namely, (i) routing and (ii) spectrum allocation (R+SA) and solves them sequentially. We also propose a heuristic algorithm that serves connections one-by-one and use it to solve the planning problem by sequentially serving all Traffic Matrix connections. To feed the sequential algorithm, two ordering policies are proposed; a simulated annealing meta-heuristic is also used to obtain even better orderings. Our results indicate that the proposed sequential heuristic with appropriate ordering yields close to optimal solutions in low running times.
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offline impairment aware routing and wavelength assignment algorithms in translucent wdm optical networks
Journal of Lightwave Technology, 2009Co-Authors: Konstantinos Manousakis, K Christodoulopoulos, I Tomkos, E Kamitsas, Emmanouel VarvarigosAbstract:Physical impairments in optical fiber transmission necessitate the use of regeneration at certain intermediate nodes, at least for certain lengthy lightpaths. We design and implement impairment-aware algorithms for routing and wavelength assignment (IA-RWA) in translucent optical networks. We focus on the offline version of the problem, where we are given a network topology, the number of available wavelengths and a Traffic Matrix. The proposed algorithm selects the 3R regeneration sites and the number of regenerators that need to be deployed on these sites, solving the regenerator placement problem for the given set of requested connections. The problem can be also posed in a slightly different setting, where a (sparse) placement of regenerators in the network is given as input and the algorithm selects which of the available regenerators to use, solving the regenerator assignment problem. We formulate the problem of regenerator placement and regenerator assignment, as a virtual topology design problem, and address it using various algorithms, ranging from a series of integer linear programming (ILP) formulations to simple greedy heuristic algorithms. Once the sequence of regenerators to be used by the non-transparent connections has been determined, we transform the initial Traffic Matrix by replacing non-transparent connections with a sequence of transparent connections that terminate and begin at the specified 3R intermediate nodes. Using the transformed Matrix we then apply an IA-RWA algorithm designed for transparent (as opposed to translucent) networks to route the Traffic. Blocked connections are re-routed using any remaining regenerator(s) in the last phase of the algorithm.
I Tomkos - One of the best experts on this subject based on the ideXlab platform.
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elastic bandwidth allocation in flexible ofdm based optical networks
Journal of Lightwave Technology, 2011Co-Authors: K Christodoulopoulos, I Tomkos, Emmanouel VarvarigosAbstract:Orthogonal Frequency Division Multiplexing (OFDM) has recently been proposed as a modulation technique for optical networks, because of its good spectral efficiency, flexibility, and tolerance to impairments. We consider the planning problem of an OFDM optical network, where we are given a Traffic Matrix that includes the requested transmission rates of the connections to be served. Connections are provisioned for their requested rate by elastically allocating spectrum using a variable number of OFDM subcarriers and choosing an appropriate modulation level, taking into account the transmission distance. We introduce the Routing, Modulation Level and Spectrum Allocation (RMLSA) problem, as opposed to the typical Routing and Wavelength Assignment (RWA) problem of traditional WDM networks, prove that is also NP-complete and present various algorithms to solve it. We start by presenting an optimal ILP RMLSA algorithm that minimizes the spectrum used to serve the Traffic Matrix, and also present a decomposition method that breaks RMLSA into its two substituent subproblems, namely 1) routing and modulation level and 2) spectrum allocation (RML+SA), and solves them sequentially. We also propose a heuristic algorithm that serves connections one-by-one and use it to solve the planning problem by sequentially serving all the connections in the Traffic Matrix. In the sequential algorithm, we investigate two policies for defining the order in which connections are considered. We also use a simulated annealing meta-heuristic to obtain even better orderings. We examine the performance of the proposed algorithms through simulation experiments and evaluate the spectrum utilization benefits that can be obtained by utilizing OFDM elastic bandwidth allocation, when compared to a traditional WDM network.
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routing and spectrum allocation in ofdm based optical networks with elastic bandwidth allocation
Global Communications Conference, 2010Co-Authors: K Christodoulopoulos, I Tomkos, Emmanouel VarvarigosAbstract:Orthogonal Frequency Division Multiplexing (OFDM) has been recently proposed as a modulation technique for optical networks, due to its good spectral efficiency and impairment tolerance. Optical OFDM is much more flexible compared to traditional WDM systems, enabling elastic bandwidth transmissions. We consider the planning problem of an OFDM-based optical network where we are given a Traffic Matrix that includes the requested transmission rates of the connections to be served. Connections are provisioned for their requested rate by elastically allocating spectrum using a variable number of OFDM subcarriers. We introduce the Routing and Spectrum Allocation (RSA) problem, as opposed to the typical Routing and Wavelength Assignment (RWA) problem of traditional WDM networks, and present various algorithms to solve the RSA. We start by presenting an optimal ILP RSA algorithm that minimizes the spectrum used to serve the Traffic Matrix, and also present a decomposition method that breaks RSA into two substituent subproblems, namely, (i) routing and (ii) spectrum allocation (R+SA) and solves them sequentially. We also propose a heuristic algorithm that serves connections one-by-one and use it to solve the planning problem by sequentially serving all Traffic Matrix connections. To feed the sequential algorithm, two ordering policies are proposed; a simulated annealing meta-heuristic is also used to obtain even better orderings. Our results indicate that the proposed sequential heuristic with appropriate ordering yields close to optimal solutions in low running times.
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offline impairment aware routing and wavelength assignment algorithms in translucent wdm optical networks
Journal of Lightwave Technology, 2009Co-Authors: Konstantinos Manousakis, K Christodoulopoulos, I Tomkos, E Kamitsas, Emmanouel VarvarigosAbstract:Physical impairments in optical fiber transmission necessitate the use of regeneration at certain intermediate nodes, at least for certain lengthy lightpaths. We design and implement impairment-aware algorithms for routing and wavelength assignment (IA-RWA) in translucent optical networks. We focus on the offline version of the problem, where we are given a network topology, the number of available wavelengths and a Traffic Matrix. The proposed algorithm selects the 3R regeneration sites and the number of regenerators that need to be deployed on these sites, solving the regenerator placement problem for the given set of requested connections. The problem can be also posed in a slightly different setting, where a (sparse) placement of regenerators in the network is given as input and the algorithm selects which of the available regenerators to use, solving the regenerator assignment problem. We formulate the problem of regenerator placement and regenerator assignment, as a virtual topology design problem, and address it using various algorithms, ranging from a series of integer linear programming (ILP) formulations to simple greedy heuristic algorithms. Once the sequence of regenerators to be used by the non-transparent connections has been determined, we transform the initial Traffic Matrix by replacing non-transparent connections with a sequence of transparent connections that terminate and begin at the specified 3R intermediate nodes. Using the transformed Matrix we then apply an IA-RWA algorithm designed for transparent (as opposed to translucent) networks to route the Traffic. Blocked connections are re-routed using any remaining regenerator(s) in the last phase of the algorithm.
