Nowadays, digital businesses with diverse deployment models such as cloud, mobile and edge devices for the internet of things will impact traffic, in both volume and dynamicity, at unprecedented rates. Moreover, due to the recent advances in optical networks and systems, the complexity of provisioning lightpaths is growing dramatically. Hence, optical network operators are forced to change their insight and move toward intent-based and self-driven networking, to cost-efficiently accommodate these challenging requirements. In this regard, knowledge-defined networking (KDN) promises to play a paramount role in realizing flexible and self-driven optical networks. In this work, we focus on one of the key aspects in this environment, i.e., prediction of quality of service for unestablished lightpaths. KDN is a solution that introduces machine learning techniques into the control plane of the network, to cope with inevitable complexities that arise in enabling network to operate autonomously and faster. For this, five machine learning models are evaluated for the classification and regression approaches. Multilayer perceptron, radial basis function and generalized regression neural network (GRNN) models are used for both of the regression and classification approaches, while support-vector machine and probabilistic neural network (PNN) models are used only for the classification scenario. Also, to discard the redundant features (among the considered experimental features) in the classification approach, input features are selected using the analysis of variance (ANOVA) test. The proposed models can accelerate and handle a significant part of operations in the closed-loop architecture of knowledge-defined optical networks, as a paradigm for designing self-driven optical networks. The best accuracies of quality of transmission prediction (classification approach) and optical signal-to-noise ratio estimation (regression approach) are achieved using PNN (with average accuracy of 99.6 ± 0.5%) and GRNN (with R-squared value of 0.957), respectively.

如今，具有多种部署模型的数字企业（例如，物联网的云，移动和边缘设备）将以空前的速度影响流量的数量和动态。此外，由于光网络和系统的最新进展，提供光路的复杂性急剧增加。因此，光网络运营商被迫改变见解，转向基于意图和自我驱动的网络，以经济高效地满足这些挑战性要求。在这方面，知识定义网络（KDN）有望在实现灵活和自驱动的光网络中发挥至关重要的作用。在这项工作中，我们专注于该环境中的关键方面之一，即，针对未建立的光路的服务质量的预测。KDN是一种将机器学习技术引入网络控制平面的解决方案，以应对使网络能够自主且快速运行的不可避免的复杂性。为此，针对分类和回归方法评估了五个机器学习模型。多层感知器，径向基函数和广义回归神经网络（GRNN）模型用于回归和分类方法，而支持向量机和概率神经网络（PNN）模型仅用于分类方案。另外，要在分类方法中丢弃冗余特征（在考虑的实验特征中），请使用方差分析（ANOVA）测试选择输入特征。提出的模型可以加速和处理知识定义的光网络的闭环体系结构中的大部分操作，作为设计自驱动光网络的范例。使用PNN（平均准确度为99.6±0.5％）和GRNN（平均准确度为9）时，可获得最佳的传输质量预测（分类方法）和光信噪比估计（回归方法）R平方值分别为0.957）。