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Dynamic RWA for All Optical Networks Using Linear Constraints for Optical Path Feasibility Assessment Svetoslav Duhovnikov, Dominic A. Schupke, Gottfried Lehmann, Thomas Fischer, Franz Rambach Siemens AG, Corporate Technologies, Information & Communication, Optical Transmission Networks Otto-Hahn-Ring 6, 81730 Munich, Germany, [email protected] Abstract We propose a fast and simple RWA algorithm with physical constraints which are modeled by linear restrictions based on wavelength groups, length, and hops. Simulation results indicate blocking performance close to unconstrained RWA. Introduction The emergence of networks with dynamic WDM connection provisioning can be foreseen in the future. There exists the problem that the feasibility of the candidate paths for the new connection and their impact on the other channels present in the system has to be determined. We propose a Routing and Wavelength Assignment (RWA) algorithm for transparent optical Wavelength Division Multiplexing (WDM) networks (subject to wavelength-continuity constraint) considering the physical constraints imposed by the specific characteristics of the optical fiber [1,2]. It is a modified shortest available path routing algorithm taking into account the performance of the individual optical channels. Algorithm description The approach essentially consists of three steps. First, the wavelength-dependent transmission performance in transparent NRZ-OOK modulated WDM networks with Single Mode Fiber (SMF) is assessed for all the wavelengths. This calculation is based on a comprehensive set of analytic models known from the literature. All transmission effects, which are relevant for bitrates of 10Gbit/s, are included: ASE, PMD, GVD, SPM, XPM, FWM and SRS. Simultaneously for all channels, the results of the individual models are consolidated to overall performance results measured by Q-factors. Networks with a line topology are considered that are fully loaded 40 wavelengths, equal span length of 80 km and a total length of 2960 km (37 spans). The nodes are placed at both ends of the network and randomly onto the possible intermediate locations (nodes can be only placed at a span end). For every path between a node pair, Q-factor values are computed. Second, from the obtained Q-factors, linear constraints are derived. They allow for simple implementation and fast verification of candidate path feasibility. Third, a standard shortest available transparent path (SATP) algorithm is modified to include the derived linear constraints. To verify the results of the algorithm, the blocking probability is simulated and compared with other algorithms that do not consider physical constraints Constraints derivation and wavelength groups Figure 1 presents the collected Q-factors, which are plotted in a graph with the x-axis representing the number of hops and the y-axis the length of the path. One should keep in mind that the hops displayed on the x-axis are only intermediate nodes. Every point represent a [hops, length] pair for a particular path. Multiple outcomes for a single point are possible, because of the random node placement. The discs depict feasible paths (Higher Q-factor) and the circles show unfeasible paths (Lower Q-factor). Figure 1: Q-factor values with given threshold for a wavelength group The aim is to locate a region, where all the Q-factors are above the threshold and based on this region derive a maximum number of hops, a maximum length and a straight line which depends on the hops and the length (more than one mixed constraint can be found for better precision). Furthermore we divide the wavelengths into 4 groups with similar performance resulting in the following constraints, see Table 1 and 2: Table 1: Hops and Length constraints for a path Group Max. Hops Max. Length, km 1 8 2000 2 8 2320 3 9 2640 4 12 2960 SATP-LW is used the blocking probability is nearly constant and at an unacceptably high level. The worst-wavelength assumption is therefore too stringent and this suggests that the wavelength assignment should consider the Q-factor performance of the individual wavelengths (SAPT-WD) as discussed in [4]. Table 2: Joint length and hops constraints for a path Group Mixed Constraints 1 − 120 * Hops + 2000 ≤ PathLength 2 3 4 − 110 * Hops − 110 * Hops − 144 * Hops − 480 * Hops + 2320 + 2750 + 3824 + 7520 ≤ PathLength ≤ PathLength ≤ PathLength ≤ PathLength Simulation setup In our scenario we consider the following routing algorithms: without constraints - shortest transparent path (STP) and SATP; with constraints - SATP with wavelength dependent performance (SAPT-WD), SAPT with all wavelengths assumed to perform like the wavelength with the highest Q-factor (SAPT-BW) and SATP with all wavelengths assumed to perform like the wavelength with the lowest Q-factor (SAPTLW). The SAPT-WD chooses the minimal reach available wavelength that can cover the distance demanded by the connection. For STP the routes are pre-computed, while for the other algorithms a new path search is performed based on the current available wavelengths. The wavelengths are assigned by “first fit.” To evaluate the network performance, measured by the ratio of rejected connections over routed connections (blocking probability), we compare the above mentioned algorithms with the following setup: • Network topology: Network of national size with X nodes and Y links designed for a static demand matrix • 40 system wavelengths; • Dense demand matrix; • Demand inter-arrival time: Negative exponentially distributed with mean of 10 time units [3]; • Demand service time: constant at 1 time unit; • Confidence level: 95%; • Confidence interval: ±1% relative to mean. Figure 2 compares the blocking probability of the different routing algorithms as a function of scaled demand matrix, i.e. we scale the offered traffic volume by the x-axis value. When SAPT-BW is used the blocking is close to the SATP, which is unconstrained. On the other hand, if Figure 2: Network performance given different routing algorithms The SAPT-WD has significantly lower blocking probability than the simple STP routing, which can be designed to automatically yield physically feasible paths, and slightly higher blocking probability compared to the SAPT. Further investigation is needed to include unequal spans lengths, which is the case for all real networks, into the model for Q-factor calculations. Conclusions In this paper, we have examined the dynamic RWA problem in the presence of the physical constraints imposed by the optical fiber characteristics. Based on linear constraints, derived for a set of wavelength groups given optical signal quality measured by Qfactor, candidate paths for a connection demand can be checked for feasibility. The performed simulations indicate that the network performance, measured in blocking probability, using SAPT-WD is comparable with the SATP (unconstrained) and SATP-BW, while it significantly outperforms the SATP-LW. References 1 Zang et al., Optical Networks Magazine, Jan. 2000. 2 Strand et al., IEEE Communications Magazine, Feb. 2001. 3 Schupke et al., DRCN, 2003. 4 Lehmann et al., APOC, 2005.