  In recent years, cloud and edge architectures have gained tremendous focus
for offloading computationally heavy applications. From machine learning and
Internet of Thing (IOT) to industrial procedures and robotics, cloud computing
have been used extensively for data processing and storage purposes, thanks to
its "infinite" resources. On the other hand, cloud computing is characterized
by long time delays due to the long distance between the cloud servers and the
machine requesting the resources. In contrast, edge computing provides almost
real-time services since edge servers are located significantly closer to the
source of data. This capability sets edge computing as an ideal option for
real-time applications, like high level control, for resource-constrained
platforms. In order to utilize the edge resources, several technologies, with
basic ones as containers and orchestrators like Kubernetes, have been developed
to provide an environment with many features, based on each application's
requirements. In this context, this works presents the implementation and
evaluation of a novel edge architecture based on Kubernetes orchestration for
controlling the trajectory of a resource-constrained Unmanned Aerial Vehicle
(UAV) by enabling Model Predictive Control (MPC).
