  More and more management and orchestration approaches for (software) networks
are based on machine learning paradigms and solutions. These approaches depend
not only on their program code to operate properly, but also require enough
input data to train their internal models. However, such training data is
barely available for the software networking domain and most presented
solutions rely on their own, sometimes not even published, data sets. This
makes it hard, or even infeasible, to reproduce and compare many of the
existing solutions. As a result, it ultimately slows down the adoption of
machine learning approaches in softwarised networks. To this end, we introduce
the "softwarised network data zoo" (SNDZoo), an open collection of software
networking data sets aiming to streamline and ease machine learning research in
the software networking domain. We present a general methodology to collect,
archive, and publish those data sets for use by other researches and, as an
example, eight initial data sets, focusing on the performance of virtualised
network functions.
