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

Abstract

Wavelength routed optical networks have emerged as a technology that can eectively utilize the enormous bandwidth of the optical ®ber. Wavelength converters play an important role in enhancing the ®ber 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 signi®cantly 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 trac 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. Ó

A Tabu Search Algorithm for Sparse Placement of Wavelength Converters in Optical Networks

“…The WIXC placement algorithms are tested using the 19-node European Optical Network (EON) [11]. The experiments are performed for different number of wavelengths per fiber: W =16, 32 and 64.…”

“…In the traffic model used in this study, lightpath demands exist between each pair of nodes. The number of demands between each node pair is randomly selected between [1][2][3], [2][3][4][5][6] and [4][5][6][7][8][9][10][11][12] for 16, 32 and 64 wavelengths, respectively. First, the GS algorithm is run a number of times in order to provide multiple solutions, then the TS algorithm is run starting from the best solution generated by the GS algorithm as initial the solution.…”

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

“…The blocking probability will be higher for random placement of wavelength converters [41]. This model can be extended to a path as proposed in [39].…”

“…The concept of adjacent wavelength conversion (AWC) model is introduced in [45]. Here, an incoming wavelength can be transformed to a small subset of nearby wavelengths at the output port i.e., an incoming wavelength can be converted only to a subset consisting of d adjacent wavelengths on either side.…”

Traffic grooming and wavelength conversion in optical networks

“…However, optimal placement was not always obtained since it was not always possible to divide the path into segments with equal blocking probability. Venugopal et al [38] proposed a heuristic to place limited-range wavelength converters based on node congestion, length of the lightpaths and nodes where conversion is high. Reference [39] proposed a solution for obtaining the best place ment of converters on a path.…”