  Emerging 5G and beyond wireless industrial virtualized networks are expected
to support a significant number of robotic manipulators. Depending on the
processes involved, these industrial robots might result in significant volume
of multi-modal traffic that will need to traverse the network all the way to
the (public/private) edge cloud, where advanced processing, control and service
orchestration will be taking place. In this paper, we perform the traffic
engineering by capitalizing on the underlying pseudo-deterministic nature of
the repetitive processes of robotic manipulators in an industrial environment
and propose an integer linear programming (ILP) model to minimize the maximum
aggregate traffic in the network. The task sequence and time gap requirements
are also considered in the proposed model. To tackle the curse of
dimensionality in ILP, we provide a random search algorithm with quadratic time
complexity. Numerical investigations reveal that the proposed scheme can reduce
the peak data rate up to 53.4% compared with the nominal case where robotic
manipulators operate in an uncoordinated fashion, resulting in significant
improvement in the utilization of the underlying network resources.
