  The transformation to smart factories and the automation of mobile robotics
is partly driven by a growing availability of ubiquitous cloud technologies. In
cyber-physical systems, such as control systems, critical parts can be migrated
to a cloud for offloading, enabling collaborative processes, improved
performance, and life-cycle management. Despite the performance uncertainty in
a cloud and the intermediate networks, presently, even cloud native function
services are being investigated for supporting critical applications that are
sensitive to time-varying execution and communication delays. In this paper, we
introduce, implement, and empirically evaluate an architecture that
successfully allows predictive controllers to take advantage of cloud native
technology. Our solution relies on continuously adapting the control system to
the present Quality of Service of the cloud and the intermediate network. As
our results show, this allows a control system to survive interruptions, noisy
neighbors, and time-variant resource availability. Without the proposed
solution, the control system will fail due to resource constraints and
insufficient response times. Further, we also show a system that can seamlessly
switch between clouds and that multiple controllers using shared resources
consequentially self-adapt so that no controller fails its objective.
