Statistical Approach for Fast Impairment-Aware Provisioning in Dynamic All-Optical Networks

Abstract

Physical layer impairments (PLIs) need to be considered in the routing and wavelength assignment (RWA) process of all-optical networks to ensure the provisioning of good quality optical connections (i.e., lightpaths). A convenient way to model the impact of PLIs on the signal quality is to use the so-called Q-factor. In a dynamic provisioning environment, impairment-aware RWA (IA-RWA) algorithms include Q-factor evaluation in their on-line decisions on whether to accept a connection request or not. The Q-factor can be computed in either an approximated or an exact way. IA-RWA algorithms using an approximated Q-factor estimation (i.e., worst case) can be very fast and allow for a short setup delay. However, connection request blocking can be unnecessarily high because of the worst-case assumption for the Q-factor parameters. In contrast, an exact Q-factor computation results in a better blocking performance at the expense of a longer setup delay, mainly due to the time spent for the Q-factor computation itself. Moreover, an exact Q-factor approach requires extensions of the generalized multi-protocol label switching suite. To overcome these problems, we propose a statistical approach for fast impairment-aware RWA (SAFIR) computation. The evaluation results reveal that SAFIR improves the blocking probability performance compared to the worst-case scenario without adding extra computational complexity and, consequently, without increasing the connection setup delay.