  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.
