  The new wave of digitization induced by Industry 4.0 calls for ubiquitous and
reliable connectivity to perform and automate industrial operations. 5G
networks can afford the extreme requirements of heterogeneous vertical
applications, but the lack of real data and realistic traffic statistics poses
many challenges for the optimization and configuration of the network for
industrial environments. In this paper, we investigate the network traffic data
generated from a laser cutting machine deployed in a Trumpf factory in Germany.
We analyze the traffic statistics, capture the dependencies between the
internal states of the machine, and model the network traffic as a production
state dependent stochastic process. The two-step model is proposed as follows:
first, we model the production process as a multi-state semi-Markov process,
then we learn the conditional distributions of the production state dependent
packet interarrival time and packet size with generative models. We compare the
performance of various generative models including variational autoencoder
(VAE), conditional variational autoencoder (CVAE), and generative adversarial
network (GAN). The numerical results show a good approximation of the traffic
arrival statistics depending on the production state. Among all generative
models, CVAE provides in general the best performance in terms of the smallest
Kullback-Leibler divergence.
