In this paper, we focus on the efficient dynamic routing in Spectrally-Spatially Flexible Optical Networks (ss-fon) realized using Single-Mode Fiber Bundles (smfbs). We study two scenarios – unprotected network (np) and network protected by dedicated path protection (dpp) against a single link failure. For these configurations, we propose a dedicated optimization approach (Enhanced Adaptive Spectral-Spatial Allocation – e-assa), which makes use of the traffic categorization and application of different allocation strategies for different traffic categories. To select beneficial categorization rules, we employ supervised learning paradigm. We show that the selection of a beneficial regression algorithm to support network optimization cannot be performed based only on standard metrics like r2 but some additional measures/experiments are necessary. Then, we carry out extensive numerical experiments in order to tune the approach and to evaluate its efficiency based on the comparison with reference methods. The results prove high efficiency of the proposed optimization framework, which provides low blocking probability and significantly shorter processing time compared to the best of reference methods.

在本文中，我们专注于使用单模光纤束( smfb s)实现的光谱空间灵活光网络( ss-fon ) 中的高效动态路由。我们研究了两种情况——不受保护的网络 ( np ) 和受专用路径保护 ( dpp )保护的网络，以防出现单个链路故障。对于这些配置，我们提出了一种专门的优化方法（增强型自适应频谱空间分配- e-assa），它利用流量分类和针对不同流量类别应用不同的分配策略。为了选择有益的分类规则，我们采用监督学习范式。我们表明，不能仅根据 r2 等标准指标来选择支持网络优化的有益回归算法，但需要一些额外的措施/实验。然后，我们进行了广泛的数值实验，以根据与参考方法的比较来调整该方法并评估其效率。结果证明了所提出的优化框架的高效率，与最佳参考方法相比，它提供了低阻塞概率和显着缩短的处理时间。