The current trend for highly dynamic and virtualized networking infrastructure made automated networking a critical requirement. Multiple solutions have been proposed to address this, including the most sought-after machine learning ML-based solutions. However, the main hurdle when developing Next Generation Network is the availability of large datasets, especially in 5G and beyond and Optical Transport Networking (OTN) traffic. This need led researchers to look for viable simulation environments to generate the necessary volume with highly configurable real-life scenarios, which can be costly in setup and require subscription-based products and even the purchase of dedicated hardware, depending on the supplier. We aim to address this issue by generating high-volume and fidelity datasets by proposing a modular solution to adapt to the user's available resources. These datasets can be used to develop better-aforementioned ML solutions resulting in higher accuracy and adaptation to real-life networking traffic.

翻译：当前高度动态化和虚拟化网络基础设施的发展趋势，使得网络自动化成为关键需求。业界已提出多种解决方案应对这一挑战，包括备受关注的基于机器学习（ML）的解决方案。然而，开发下一代网络面临的主要障碍在于缺乏大规模数据集，尤其是在5G及未来通信网络与光传送网（OTN）流量领域。这一需求促使研究人员寻求可行的仿真环境，以通过高度可配置的真实场景生成所需规模的数据集。但此类仿真环境搭建成本高昂，通常需要购买基于订阅的产品甚至专用硬件，且成本因供应商而异。为解决此问题，我们提出一种模块化解决方案，可适应用户现有资源，从而生成高容量、高保真度的数据集。这些数据集将有助于开发更精准且更适应真实网络流量的前述ML解决方案。